fix: renumber withdrawal migration

Co-authored-by: multica-agent <github@multica.ai>
新功能(#41 ): 提现优化 — 收款方式选择 + 取消提现 + 收款码上传
2026-05-26 09:01:17 -07:00 · 2026-05-26 08:36:25 -07:00 · 2026-05-26 08:35:45 -07:00 · 2026-05-26 08:07:08 -07:00 · 2026-05-26 08:06:50 -07:00 · 2026-05-25 21:56:01 -07:00
224 changed files with 20058 additions and 30114 deletions
@@ -1,38 +0,0 @@
 # .agents Directory
 This directory contains agent configuration and skills for OpenAI Codex CLI.
 ## Structure
 ```
 .agents/
  config.toml     # Main configuration file
  skills/         # Skill definitions
    skill-name/
      SKILL.md    # Skill instructions
      scripts/    # Optional scripts
      docs/       # Optional documentation
  README.md       # This file
 ```
 ## Configuration
 The `config.toml` file controls:
 - Model selection
 - Approval policies
 - Sandbox modes
 - MCP server connections
 - Skills configuration
 ## Skills
 Skills are invoked using `$skill-name` syntax. Each skill has:
 - YAML frontmatter with metadata
 - Trigger and skip conditions
 - Commands and examples
 ## Documentation
 - Main instructions: `AGENTS.md` (project root)
 - Local overrides: `.codex/AGENTS.override.md` (gitignored)
 - Claude Flow: https://github.com/ruvnet/claude-flow
@@ -1,298 +0,0 @@
 # =============================================================================
 # Claude Flow V3 - Codex Configuration
 # =============================================================================
 # Generated by: @claude-flow/codex
 # Documentation: https://github.com/ruvnet/claude-flow
 # 
 # This file configures the Codex CLI for Claude Flow integration.
 # Place in .agents/config.toml (project) or .codex/config.toml (user).
 # =============================================================================
 # =============================================================================
 # Core Settings
 # =============================================================================
 # Model selection - the AI model to use for code generation
 # Options: gpt-5.3-codex, gpt-4o, claude-sonnet, claude-opus
 model = "gpt-5.3-codex"
 # Approval policy determines when human approval is required
 # - untrusted: Always require approval
 # - on-failure: Require approval only after failures
 # - on-request: Require approval for significant changes
 # - never: Auto-approve all actions (use with caution)
 approval_policy = "on-request"
 # Sandbox mode controls file system access
 # - read-only: Can only read files, no modifications
 # - workspace-write: Can write within workspace directory
 # - danger-full-access: Full file system access (dangerous)
 sandbox_mode = "workspace-write"
 # Web search enables internet access for research
 # - disabled: No web access
 # - cached: Use cached results when available
 # - live: Always fetch fresh results
 web_search = "cached"
 # =============================================================================
 # Project Documentation
 # =============================================================================
 # Maximum bytes to read from AGENTS.md files
 project_doc_max_bytes = 65536
 # Fallback filenames if AGENTS.md not found
 project_doc_fallback_filenames = [
  "AGENTS.md",
  "TEAM_GUIDE.md",
  ".agents.md"
 ]
 # =============================================================================
 # Features
 # =============================================================================
 [features]
 # Enable child AGENTS.md guidance
 child_agents_md = true
 # Cache shell environment for faster repeated commands
 shell_snapshot = true
 # Smart approvals based on request context
 request_rule = true
 # Enable remote compaction for large histories
 remote_compaction = true
 # =============================================================================
 # MCP Servers
 # =============================================================================
 [mcp_servers.claude-flow]
 command = "npx"
 args = ["-y", "@claude-flow/cli@latest"]
 enabled = true
 tool_timeout_sec = 120
 # =============================================================================
 # Skills Configuration
 # =============================================================================
 [[skills.config]]
 path = ".agents/skills/swarm-orchestration"
 enabled = true
 [[skills.config]]
 path = ".agents/skills/memory-management"
 enabled = true
 [[skills.config]]
 path = ".agents/skills/sparc-methodology"
 enabled = true
 [[skills.config]]
 path = ".agents/skills/security-audit"
 enabled = true
 # =============================================================================
 # Profiles
 # =============================================================================
 # Development profile - more permissive for local work
 [profiles.dev]
 approval_policy = "never"
 sandbox_mode = "danger-full-access"
 web_search = "live"
 # Safe profile - maximum restrictions
 [profiles.safe]
 approval_policy = "untrusted"
 sandbox_mode = "read-only"
 web_search = "disabled"
 # CI profile - for automated pipelines
 [profiles.ci]
 approval_policy = "never"
 sandbox_mode = "workspace-write"
 web_search = "cached"
 # =============================================================================
 # History
 # =============================================================================
 [history]
 # Save all session transcripts
 persistence = "save-all"
 # =============================================================================
 # Shell Environment
 # =============================================================================
 [shell_environment_policy]
 # Inherit environment variables
 inherit = "core"
 # Exclude sensitive variables
 exclude = ["*_KEY", "*_SECRET", "*_TOKEN", "*_PASSWORD"]
 # =============================================================================
 # Sandbox Workspace Write Settings
 # =============================================================================
 [sandbox_workspace_write]
 # Additional writable paths beyond workspace
 writable_roots = []
 # Allow network access
 network_access = true
 # Exclude temp directories
 exclude_slash_tmp = false
 # =============================================================================
 # Security Settings
 # =============================================================================
 [security]
 # Enable input validation for all user inputs
 input_validation = true
 # Prevent directory traversal attacks
 path_traversal_prevention = true
 # Scan for hardcoded secrets
 secret_scanning = true
 # Scan dependencies for known CVEs
 cve_scanning = true
 # Maximum file size for operations (bytes)
 max_file_size = 10485760
 # Allowed file extensions (empty = allow all)
 allowed_extensions = []
 # Blocked file patterns (regex)
 blocked_patterns = ["\\.env$", "credentials\\.json$", "\\.pem$", "\\.key$"]
 # =============================================================================
 # Performance Settings
 # =============================================================================
 [performance]
 # Maximum concurrent agents
 max_agents = 8
 # Task timeout in seconds
 task_timeout = 300
 # Memory limit per agent
 memory_limit = "512MB"
 # Enable response caching
 cache_enabled = true
 # Cache TTL in seconds
 cache_ttl = 3600
 # Enable parallel task execution
 parallel_execution = true
 # =============================================================================
 # Logging Settings
 # =============================================================================
 [logging]
 # Log level: debug, info, warn, error
 level = "info"
 # Log format: json, text, pretty
 format = "pretty"
 # Log destination: stdout, file, both
 destination = "stdout"
 # =============================================================================
 # Neural Intelligence Settings
 # =============================================================================
 [neural]
 # Enable SONA (Self-Optimizing Neural Architecture)
 sona_enabled = true
 # Enable HNSW vector search
 hnsw_enabled = true
 # HNSW index parameters
 hnsw_m = 16
 hnsw_ef_construction = 200
 hnsw_ef_search = 100
 # Enable pattern learning
 pattern_learning = true
 # Learning rate for neural adaptation
 learning_rate = 0.01
 # =============================================================================
 # Swarm Orchestration Settings
 # =============================================================================
 [swarm]
 # Default topology: hierarchical, mesh, ring, star
 default_topology = "hierarchical"
 # Default strategy: balanced, specialized, adaptive
 default_strategy = "specialized"
 # Consensus algorithm: raft, byzantine, gossip
 consensus = "raft"
 # Enable anti-drift measures
 anti_drift = true
 # Checkpoint interval (tasks)
 checkpoint_interval = 10
 # =============================================================================
 # Hooks Configuration
 # =============================================================================
 [hooks]
 # Enable lifecycle hooks
 enabled = true
 # Pre-task hook
 pre_task = true
 # Post-task hook (for learning)
 post_task = true
 # Enable neural training on post-edit
 train_on_edit = true
 # =============================================================================
 # Background Workers
 # =============================================================================
 [workers]
 # Enable background workers
 enabled = true
 # Worker configuration
 [workers.audit]
 enabled = true
 priority = "critical"
 interval = 300
 [workers.optimize]
 enabled = true
 priority = "high"
 interval = 600
 [workers.consolidate]
 enabled = true
 priority = "low"
 interval = 1800
@@ -1,550 +0,0 @@
 ---
 name: "AgentDB Advanced Features"
 description: "Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications."
 ---
 # AgentDB Advanced Features
 ## What This Skill Does
 Covers advanced AgentDB capabilities for distributed systems, multi-database coordination, custom distance metrics, hybrid search (vector + metadata), QUIC synchronization, and production deployment patterns. Enables building sophisticated AI systems with sub-millisecond cross-node communication and advanced search capabilities.
 **Performance**: <1ms QUIC sync, hybrid search with filters, custom distance metrics.
 ## Prerequisites
 - Node.js 18+
 - AgentDB v1.0.7+ (via agentic-flow)
 - Understanding of distributed systems (for QUIC sync)
 - Vector search fundamentals
 ---
 ## QUIC Synchronization
 ### What is QUIC Sync?
 QUIC (Quick UDP Internet Connections) enables sub-millisecond latency synchronization between AgentDB instances across network boundaries with automatic retry, multiplexing, and encryption.
 **Benefits**:
 - <1ms latency between nodes
 - Multiplexed streams (multiple operations simultaneously)
 - Built-in encryption (TLS 1.3)
 - Automatic retry and recovery
 - Event-based broadcasting
 ### Enable QUIC Sync
 ```typescript
 import { createAgentDBAdapter } from 'agentic-flow/reasoningbank';
 // Initialize with QUIC synchronization
 const adapter = await createAgentDBAdapter({
  dbPath: '.agentdb/distributed.db',
  enableQUICSync: true,
  syncPort: 4433,
  syncPeers: [
    '192.168.1.10:4433',
    '192.168.1.11:4433',
    '192.168.1.12:4433',
  ],
 });
 // Patterns automatically sync across all peers
 await adapter.insertPattern({
  // ... pattern data
 });
 // Available on all peers within ~1ms
 ```
 ### QUIC Configuration
 ```typescript
 const adapter = await createAgentDBAdapter({
  enableQUICSync: true,
  syncPort: 4433,              // QUIC server port
  syncPeers: ['host1:4433'],   // Peer addresses
  syncInterval: 1000,          // Sync interval (ms)
  syncBatchSize: 100,          // Patterns per batch
  maxRetries: 3,               // Retry failed syncs
  compression: true,           // Enable compression
 });
 ```
 ### Multi-Node Deployment
 ```bash
 # Node 1 (192.168.1.10)
 AGENTDB_QUIC_SYNC=true \
 AGENTDB_QUIC_PORT=4433 \
 AGENTDB_QUIC_PEERS=192.168.1.11:4433,192.168.1.12:4433 \
 node server.js
 # Node 2 (192.168.1.11)
 AGENTDB_QUIC_SYNC=true \
 AGENTDB_QUIC_PORT=4433 \
 AGENTDB_QUIC_PEERS=192.168.1.10:4433,192.168.1.12:4433 \
 node server.js
 # Node 3 (192.168.1.12)
 AGENTDB_QUIC_SYNC=true \
 AGENTDB_QUIC_PORT=4433 \
 AGENTDB_QUIC_PEERS=192.168.1.10:4433,192.168.1.11:4433 \
 node server.js
 ```
 ---
 ## Distance Metrics
 ### Cosine Similarity (Default)
 Best for normalized vectors, semantic similarity:
 ```bash
 # CLI
 npx agentdb@latest query ./vectors.db "[0.1,0.2,...]" -m cosine
 # API
 const result = await adapter.retrieveWithReasoning(queryEmbedding, {
  metric: 'cosine',
  k: 10,
 });
 ```
 **Use Cases**:
 - Text embeddings (BERT, GPT, etc.)
 - Semantic search
 - Document similarity
 - Most general-purpose applications
 **Formula**: `cos(θ) = (A · B) / (||A|| × ||B||)`
 **Range**: [-1, 1] (1 = identical, -1 = opposite)
 ### Euclidean Distance (L2)
 Best for spatial data, geometric similarity:
 ```bash
 # CLI
 npx agentdb@latest query ./vectors.db "[0.1,0.2,...]" -m euclidean
 # API
 const result = await adapter.retrieveWithReasoning(queryEmbedding, {
  metric: 'euclidean',
  k: 10,
 });
 ```
 **Use Cases**:
 - Image embeddings
 - Spatial data
 - Computer vision
 - When vector magnitude matters
 **Formula**: `d = √(Σ(ai - bi)²)`
 **Range**: [0, ∞] (0 = identical, ∞ = very different)
 ### Dot Product
 Best for pre-normalized vectors, fast computation:
 ```bash
 # CLI
 npx agentdb@latest query ./vectors.db "[0.1,0.2,...]" -m dot
 # API
 const result = await adapter.retrieveWithReasoning(queryEmbedding, {
  metric: 'dot',
  k: 10,
 });
 ```
 **Use Cases**:
 - Pre-normalized embeddings
 - Fast similarity computation
 - When vectors are already unit-length
 **Formula**: `dot = Σ(ai × bi)`
 **Range**: [-∞, ∞] (higher = more similar)
 ### Custom Distance Metrics
 ```typescript
 // Implement custom distance function
 function customDistance(vec1: number[], vec2: number[]): number {
  // Weighted Euclidean distance
  const weights = [1.0, 2.0, 1.5, ...];
  let sum = 0;
  for (let i = 0; i < vec1.length; i++) {
    sum += weights[i] * Math.pow(vec1[i] - vec2[i], 2);
  }
  return Math.sqrt(sum);
 }
 // Use in search (requires custom implementation)
 ```
 ---
 ## Hybrid Search (Vector + Metadata)
 ### Basic Hybrid Search
 Combine vector similarity with metadata filtering:
 ```typescript
 // Store documents with metadata
 await adapter.insertPattern({
  id: '',
  type: 'document',
  domain: 'research-papers',
  pattern_data: JSON.stringify({
    embedding: documentEmbedding,
    text: documentText,
    metadata: {
      author: 'Jane Smith',
      year: 2025,
      category: 'machine-learning',
      citations: 150,
    }
  }),
  confidence: 1.0,
  usage_count: 0,
  success_count: 0,
  created_at: Date.now(),
  last_used: Date.now(),
 });
 // Hybrid search: vector similarity + metadata filters
 const result = await adapter.retrieveWithReasoning(queryEmbedding, {
  domain: 'research-papers',
  k: 20,
  filters: {
    year: { $gte: 2023 },          // Published 2023 or later
    category: 'machine-learning',   // ML papers only
    citations: { $gte: 50 },       // Highly cited
  },
 });
 ```
 ### Advanced Filtering
 ```typescript
 // Complex metadata queries
 const result = await adapter.retrieveWithReasoning(queryEmbedding, {
  domain: 'products',
  k: 50,
  filters: {
    price: { $gte: 10, $lte: 100 },      // Price range
    category: { $in: ['electronics', 'gadgets'] },  // Multiple categories
    rating: { $gte: 4.0 },                // High rated
    inStock: true,                        // Available
    tags: { $contains: 'wireless' },      // Has tag
  },
 });
 ```
 ### Weighted Hybrid Search
 Combine vector and metadata scores:
 ```typescript
 const result = await adapter.retrieveWithReasoning(queryEmbedding, {
  domain: 'content',
  k: 20,
  hybridWeights: {
    vectorSimilarity: 0.7,  // 70% weight on semantic similarity
    metadataScore: 0.3,     // 30% weight on metadata match
  },
  filters: {
    category: 'technology',
    recency: { $gte: Date.now() - 30 * 24 * 3600000 },  // Last 30 days
  },
 });
 ```
 ---
 ## Multi-Database Management
 ### Multiple Databases
 ```typescript
 // Separate databases for different domains
 const knowledgeDB = await createAgentDBAdapter({
  dbPath: '.agentdb/knowledge.db',
 });
 const conversationDB = await createAgentDBAdapter({
  dbPath: '.agentdb/conversations.db',
 });
 const codeDB = await createAgentDBAdapter({
  dbPath: '.agentdb/code.db',
 });
 // Use appropriate database for each task
 await knowledgeDB.insertPattern({ /* knowledge */ });
 await conversationDB.insertPattern({ /* conversation */ });
 await codeDB.insertPattern({ /* code */ });
 ```
 ### Database Sharding
 ```typescript
 // Shard by domain for horizontal scaling
 const shards = {
  'domain-a': await createAgentDBAdapter({ dbPath: '.agentdb/shard-a.db' }),
  'domain-b': await createAgentDBAdapter({ dbPath: '.agentdb/shard-b.db' }),
  'domain-c': await createAgentDBAdapter({ dbPath: '.agentdb/shard-c.db' }),
 };
 // Route queries to appropriate shard
 function getDBForDomain(domain: string) {
  const shardKey = domain.split('-')[0];  // Extract shard key
  return shards[shardKey] || shards['domain-a'];
 }
 // Insert to correct shard
 const db = getDBForDomain('domain-a-task');
 await db.insertPattern({ /* ... */ });
 ```
 ---
 ## MMR (Maximal Marginal Relevance)
 Retrieve diverse results to avoid redundancy:
 ```typescript
 // Without MMR: Similar results may be redundant
 const standardResults = await adapter.retrieveWithReasoning(queryEmbedding, {
  k: 10,
  useMMR: false,
 });
 // With MMR: Diverse, non-redundant results
 const diverseResults = await adapter.retrieveWithReasoning(queryEmbedding, {
  k: 10,
  useMMR: true,
  mmrLambda: 0.5,  // Balance relevance (0) vs diversity (1)
 });
 ```
 **MMR Parameters**:
 - `mmrLambda = 0`: Maximum relevance (may be redundant)
 - `mmrLambda = 0.5`: Balanced (default)
 - `mmrLambda = 1`: Maximum diversity (may be less relevant)
 **Use Cases**:
 - Search result diversification
 - Recommendation systems
 - Avoiding echo chambers
 - Exploratory search
 ---
 ## Context Synthesis
 Generate rich context from multiple memories:
 ```typescript
 const result = await adapter.retrieveWithReasoning(queryEmbedding, {
  domain: 'problem-solving',
  k: 10,
  synthesizeContext: true,  // Enable context synthesis
 });
 // ContextSynthesizer creates coherent narrative
 console.log('Synthesized Context:', result.context);
 // "Based on 10 similar problem-solving attempts, the most effective
 //  approach involves: 1) analyzing root cause, 2) brainstorming solutions,
 //  3) evaluating trade-offs, 4) implementing incrementally. Success rate: 85%"
 console.log('Patterns:', result.patterns);
 // Extracted common patterns across memories
 ```
 ---
 ## Production Patterns
 ### Connection Pooling
 ```typescript
 // Singleton pattern for shared adapter
 class AgentDBPool {
  private static instance: AgentDBAdapter;
  static async getInstance() {
    if (!this.instance) {
      this.instance = await createAgentDBAdapter({
        dbPath: '.agentdb/production.db',
        quantizationType: 'scalar',
        cacheSize: 2000,
      });
    }
    return this.instance;
  }
 }
 // Use in application
 const db = await AgentDBPool.getInstance();
 const results = await db.retrieveWithReasoning(queryEmbedding, { k: 10 });
 ```
 ### Error Handling
 ```typescript
 async function safeRetrieve(queryEmbedding: number[], options: any) {
  try {
    const result = await adapter.retrieveWithReasoning(queryEmbedding, options);
    return result;
  } catch (error) {
    if (error.code === 'DIMENSION_MISMATCH') {
      console.error('Query embedding dimension mismatch');
      // Handle dimension error
    } else if (error.code === 'DATABASE_LOCKED') {
      // Retry with exponential backoff
      await new Promise(resolve => setTimeout(resolve, 100));
      return safeRetrieve(queryEmbedding, options);
    }
    throw error;
  }
 }
 ```
 ### Monitoring and Logging
 ```typescript
 // Performance monitoring
 const startTime = Date.now();
 const result = await adapter.retrieveWithReasoning(queryEmbedding, { k: 10 });
 const latency = Date.now() - startTime;
 if (latency > 100) {
  console.warn('Slow query detected:', latency, 'ms');
 }
 // Log statistics
 const stats = await adapter.getStats();
 console.log('Database Stats:', {
  totalPatterns: stats.totalPatterns,
  dbSize: stats.dbSize,
  cacheHitRate: stats.cacheHitRate,
  avgSearchLatency: stats.avgSearchLatency,
 });
 ```
 ---
 ## CLI Advanced Operations
 ### Database Import/Export
 ```bash
 # Export with compression
 npx agentdb@latest export ./vectors.db ./backup.json.gz --compress
 # Import from backup
 npx agentdb@latest import ./backup.json.gz --decompress
 # Merge databases
 npx agentdb@latest merge ./db1.sqlite ./db2.sqlite ./merged.sqlite
 ```
 ### Database Optimization
 ```bash
 # Vacuum database (reclaim space)
 sqlite3 .agentdb/vectors.db "VACUUM;"
 # Analyze for query optimization
 sqlite3 .agentdb/vectors.db "ANALYZE;"
 # Rebuild indices
 npx agentdb@latest reindex ./vectors.db
 ```
 ---
 ## Environment Variables
 ```bash
 # AgentDB configuration
 AGENTDB_PATH=.agentdb/reasoningbank.db
 AGENTDB_ENABLED=true
 # Performance tuning
 AGENTDB_QUANTIZATION=binary     # binary|scalar|product|none
 AGENTDB_CACHE_SIZE=2000
 AGENTDB_HNSW_M=16
 AGENTDB_HNSW_EF=100
 # Learning plugins
 AGENTDB_LEARNING=true
 # Reasoning agents
 AGENTDB_REASONING=true
 # QUIC synchronization
 AGENTDB_QUIC_SYNC=true
 AGENTDB_QUIC_PORT=4433
 AGENTDB_QUIC_PEERS=host1:4433,host2:4433
 ```
 ---
 ## Troubleshooting
 ### Issue: QUIC sync not working
 ```bash
 # Check firewall allows UDP port 4433
 sudo ufw allow 4433/udp
 # Verify peers are reachable
 ping host1
 # Check QUIC logs
 DEBUG=agentdb:quic node server.js
 ```
 ### Issue: Hybrid search returns no results
 ```typescript
 // Relax filters
 const result = await adapter.retrieveWithReasoning(queryEmbedding, {
  k: 100,  // Increase k
  filters: {
    // Remove or relax filters
  },
 });
 ```
 ### Issue: Memory consolidation too aggressive
 ```typescript
 // Disable automatic optimization
 const result = await adapter.retrieveWithReasoning(queryEmbedding, {
  optimizeMemory: false,  // Disable auto-consolidation
  k: 10,
 });
 ```
 ---
 ## Learn More
 - **QUIC Protocol**: docs/quic-synchronization.pdf
 - **Hybrid Search**: docs/hybrid-search-guide.md
 - **GitHub**: https://github.com/ruvnet/agentic-flow/tree/main/packages/agentdb
 - **Website**: https://agentdb.ruv.io
 ---
 **Category**: Advanced / Distributed Systems
 **Difficulty**: Advanced
 **Estimated Time**: 45-60 minutes
@@ -1,545 +0,0 @@
 ---
 name: "AgentDB Learning Plugins"
 description: "Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience."
 ---
 # AgentDB Learning Plugins
 ## What This Skill Does
 Provides access to 9 reinforcement learning algorithms via AgentDB's plugin system. Create, train, and deploy learning plugins for autonomous agents that improve through experience. Includes offline RL (Decision Transformer), value-based learning (Q-Learning), policy gradients (Actor-Critic), and advanced techniques.
 **Performance**: Train models 10-100x faster with WASM-accelerated neural inference.
 ## Prerequisites
 - Node.js 18+
 - AgentDB v1.0.7+ (via agentic-flow)
 - Basic understanding of reinforcement learning (recommended)
 ---
 ## Quick Start with CLI
 ### Create Learning Plugin
 ```bash
 # Interactive wizard
 npx agentdb@latest create-plugin
 # Use specific template
 npx agentdb@latest create-plugin -t decision-transformer -n my-agent
 # Preview without creating
 npx agentdb@latest create-plugin -t q-learning --dry-run
 # Custom output directory
 npx agentdb@latest create-plugin -t actor-critic -o ./plugins
 ```
 ### List Available Templates
 ```bash
 # Show all plugin templates
 npx agentdb@latest list-templates
 # Available templates:
 # - decision-transformer (sequence modeling RL - recommended)
 # - q-learning (value-based learning)
 # - sarsa (on-policy TD learning)
 # - actor-critic (policy gradient with baseline)
 # - curiosity-driven (exploration-based)
 ```
 ### Manage Plugins
 ```bash
 # List installed plugins
 npx agentdb@latest list-plugins
 # Get plugin information
 npx agentdb@latest plugin-info my-agent
 # Shows: algorithm, configuration, training status
 ```
 ---
 ## Quick Start with API
 ```typescript
 import { createAgentDBAdapter } from 'agentic-flow/reasoningbank';
 // Initialize with learning enabled
 const adapter = await createAgentDBAdapter({
  dbPath: '.agentdb/learning.db',
  enableLearning: true,       // Enable learning plugins
  enableReasoning: true,
  cacheSize: 1000,
 });
 // Store training experience
 await adapter.insertPattern({
  id: '',
  type: 'experience',
  domain: 'game-playing',
  pattern_data: JSON.stringify({
    embedding: await computeEmbedding('state-action-reward'),
    pattern: {
      state: [0.1, 0.2, 0.3],
      action: 2,
      reward: 1.0,
      next_state: [0.15, 0.25, 0.35],
      done: false
    }
  }),
  confidence: 0.9,
  usage_count: 1,
  success_count: 1,
  created_at: Date.now(),
  last_used: Date.now(),
 });
 // Train learning model
 const metrics = await adapter.train({
  epochs: 50,
  batchSize: 32,
 });
 console.log('Training Loss:', metrics.loss);
 console.log('Duration:', metrics.duration, 'ms');
 ```
 ---
 ## Available Learning Algorithms (9 Total)
 ### 1. Decision Transformer (Recommended)
 **Type**: Offline Reinforcement Learning
 **Best For**: Learning from logged experiences, imitation learning
 **Strengths**: No online interaction needed, stable training
 ```bash
 npx agentdb@latest create-plugin -t decision-transformer -n dt-agent
 ```
 **Use Cases**:
 - Learn from historical data
 - Imitation learning from expert demonstrations
 - Safe learning without environment interaction
 - Sequence modeling tasks
 **Configuration**:
 ```json
 {
  "algorithm": "decision-transformer",
  "model_size": "base",
  "context_length": 20,
  "embed_dim": 128,
  "n_heads": 8,
  "n_layers": 6
 }
 ```
 ### 2. Q-Learning
 **Type**: Value-Based RL (Off-Policy)
 **Best For**: Discrete action spaces, sample efficiency
 **Strengths**: Proven, simple, works well for small/medium problems
 ```bash
 npx agentdb@latest create-plugin -t q-learning -n q-agent
 ```
 **Use Cases**:
 - Grid worlds, board games
 - Navigation tasks
 - Resource allocation
 - Discrete decision-making
 **Configuration**:
 ```json
 {
  "algorithm": "q-learning",
  "learning_rate": 0.001,
  "gamma": 0.99,
  "epsilon": 0.1,
  "epsilon_decay": 0.995
 }
 ```
 ### 3. SARSA
 **Type**: Value-Based RL (On-Policy)
 **Best For**: Safe exploration, risk-sensitive tasks
 **Strengths**: More conservative than Q-Learning, better for safety
 ```bash
 npx agentdb@latest create-plugin -t sarsa -n sarsa-agent
 ```
 **Use Cases**:
 - Safety-critical applications
 - Risk-sensitive decision-making
 - Online learning with exploration
 **Configuration**:
 ```json
 {
  "algorithm": "sarsa",
  "learning_rate": 0.001,
  "gamma": 0.99,
  "epsilon": 0.1
 }
 ```
 ### 4. Actor-Critic
 **Type**: Policy Gradient with Value Baseline
 **Best For**: Continuous actions, variance reduction
 **Strengths**: Stable, works for continuous/discrete actions
 ```bash
 npx agentdb@latest create-plugin -t actor-critic -n ac-agent
 ```
 **Use Cases**:
 - Continuous control (robotics, simulations)
 - Complex action spaces
 - Multi-agent coordination
 **Configuration**:
 ```json
 {
  "algorithm": "actor-critic",
  "actor_lr": 0.001,
  "critic_lr": 0.002,
  "gamma": 0.99,
  "entropy_coef": 0.01
 }
 ```
 ### 5. Active Learning
 **Type**: Query-Based Learning
 **Best For**: Label-efficient learning, human-in-the-loop
 **Strengths**: Minimizes labeling cost, focuses on uncertain samples
 **Use Cases**:
 - Human feedback incorporation
 - Label-efficient training
 - Uncertainty sampling
 - Annotation cost reduction
 ### 6. Adversarial Training
 **Type**: Robustness Enhancement
 **Best For**: Safety, robustness to perturbations
 **Strengths**: Improves model robustness, adversarial defense
 **Use Cases**:
 - Security applications
 - Robust decision-making
 - Adversarial defense
 - Safety testing
 ### 7. Curriculum Learning
 **Type**: Progressive Difficulty Training
 **Best For**: Complex tasks, faster convergence
 **Strengths**: Stable learning, faster convergence on hard tasks
 **Use Cases**:
 - Complex multi-stage tasks
 - Hard exploration problems
 - Skill composition
 - Transfer learning
 ### 8. Federated Learning
 **Type**: Distributed Learning
 **Best For**: Privacy, distributed data
 **Strengths**: Privacy-preserving, scalable
 **Use Cases**:
 - Multi-agent systems
 - Privacy-sensitive data
 - Distributed training
 - Collaborative learning
 ### 9. Multi-Task Learning
 **Type**: Transfer Learning
 **Best For**: Related tasks, knowledge sharing
 **Strengths**: Faster learning on new tasks, better generalization
 **Use Cases**:
 - Task families
 - Transfer learning
 - Domain adaptation
 - Meta-learning
 ---
 ## Training Workflow
 ### 1. Collect Experiences
 ```typescript
 // Store experiences during agent execution
 for (let i = 0; i < numEpisodes; i++) {
  const episode = runEpisode();
  for (const step of episode.steps) {
    await adapter.insertPattern({
      id: '',
      type: 'experience',
      domain: 'task-domain',
      pattern_data: JSON.stringify({
        embedding: await computeEmbedding(JSON.stringify(step)),
        pattern: {
          state: step.state,
          action: step.action,
          reward: step.reward,
          next_state: step.next_state,
          done: step.done
        }
      }),
      confidence: step.reward > 0 ? 0.9 : 0.5,
      usage_count: 1,
      success_count: step.reward > 0 ? 1 : 0,
      created_at: Date.now(),
      last_used: Date.now(),
    });
  }
 }
 ```
 ### 2. Train Model
 ```typescript
 // Train on collected experiences
 const trainingMetrics = await adapter.train({
  epochs: 100,
  batchSize: 64,
  learningRate: 0.001,
  validationSplit: 0.2,
 });
 console.log('Training Metrics:', trainingMetrics);
 // {
 //   loss: 0.023,
 //   valLoss: 0.028,
 //   duration: 1523,
 //   epochs: 100
 // }
 ```
 ### 3. Evaluate Performance
 ```typescript
 // Retrieve similar successful experiences
 const testQuery = await computeEmbedding(JSON.stringify(testState));
 const result = await adapter.retrieveWithReasoning(testQuery, {
  domain: 'task-domain',
  k: 10,
  synthesizeContext: true,
 });
 // Evaluate action quality
 const suggestedAction = result.memories[0].pattern.action;
 const confidence = result.memories[0].similarity;
 console.log('Suggested Action:', suggestedAction);
 console.log('Confidence:', confidence);
 ```
 ---
 ## Advanced Training Techniques
 ### Experience Replay
 ```typescript
 // Store experiences in buffer
 const replayBuffer = [];
 // Sample random batch for training
 const batch = sampleRandomBatch(replayBuffer, batchSize: 32);
 // Train on batch
 await adapter.train({
  data: batch,
  epochs: 1,
  batchSize: 32,
 });
 ```
 ### Prioritized Experience Replay
 ```typescript
 // Store experiences with priority (TD error)
 await adapter.insertPattern({
  // ... standard fields
  confidence: tdError,  // Use TD error as confidence/priority
  // ...
 });
 // Retrieve high-priority experiences
 const highPriority = await adapter.retrieveWithReasoning(queryEmbedding, {
  domain: 'task-domain',
  k: 32,
  minConfidence: 0.7,  // Only high TD-error experiences
 });
 ```
 ### Multi-Agent Training
 ```typescript
 // Collect experiences from multiple agents
 for (const agent of agents) {
  const experience = await agent.step();
  await adapter.insertPattern({
    // ... store experience with agent ID
    domain: `multi-agent/${agent.id}`,
  });
 }
 // Train shared model
 await adapter.train({
  epochs: 50,
  batchSize: 64,
 });
 ```
 ---
 ## Performance Optimization
 ### Batch Training
 ```typescript
 // Collect batch of experiences
 const experiences = collectBatch(size: 1000);
 // Batch insert (500x faster)
 for (const exp of experiences) {
  await adapter.insertPattern({ /* ... */ });
 }
 // Train on batch
 await adapter.train({
  epochs: 10,
  batchSize: 128,  // Larger batch for efficiency
 });
 ```
 ### Incremental Learning
 ```typescript
 // Train incrementally as new data arrives
 setInterval(async () => {
  const newExperiences = getNewExperiences();
  if (newExperiences.length > 100) {
    await adapter.train({
      epochs: 5,
      batchSize: 32,
    });
  }
 }, 60000);  // Every minute
 ```
 ---
 ## Integration with Reasoning Agents
 Combine learning with reasoning for better performance:
 ```typescript
 // Train learning model
 await adapter.train({ epochs: 50, batchSize: 32 });
 // Use reasoning agents for inference
 const result = await adapter.retrieveWithReasoning(queryEmbedding, {
  domain: 'decision-making',
  k: 10,
  useMMR: true,              // Diverse experiences
  synthesizeContext: true,    // Rich context
  optimizeMemory: true,       // Consolidate patterns
 });
 // Make decision based on learned experiences + reasoning
 const decision = result.context.suggestedAction;
 const confidence = result.memories[0].similarity;
 ```
 ---
 ## CLI Operations
 ```bash
 # Create plugin
 npx agentdb@latest create-plugin -t decision-transformer -n my-plugin
 # List plugins
 npx agentdb@latest list-plugins
 # Get plugin info
 npx agentdb@latest plugin-info my-plugin
 # List templates
 npx agentdb@latest list-templates
 ```
 ---
 ## Troubleshooting
 ### Issue: Training not converging
 ```typescript
 // Reduce learning rate
 await adapter.train({
  epochs: 100,
  batchSize: 32,
  learningRate: 0.0001,  // Lower learning rate
 });
 ```
 ### Issue: Overfitting
 ```typescript
 // Use validation split
 await adapter.train({
  epochs: 50,
  batchSize: 64,
  validationSplit: 0.2,  // 20% validation
 });
 // Enable memory optimization
 await adapter.retrieveWithReasoning(queryEmbedding, {
  optimizeMemory: true,  // Consolidate, reduce overfitting
 });
 ```
 ### Issue: Slow training
 ```bash
 # Enable quantization for faster inference
 # Use binary quantization (32x faster)
 ```
 ---
 ## Learn More
 - **Algorithm Papers**: See docs/algorithms/ for detailed papers
 - **GitHub**: https://github.com/ruvnet/agentic-flow/tree/main/packages/agentdb
 - **MCP Integration**: `npx agentdb@latest mcp`
 - **Website**: https://agentdb.ruv.io
 ---
 **Category**: Machine Learning / Reinforcement Learning
 **Difficulty**: Intermediate to Advanced
 **Estimated Time**: 30-60 minutes
@@ -1,339 +0,0 @@
 ---
 name: "AgentDB Memory Patterns"
 description: "Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants."
 ---
 # AgentDB Memory Patterns
 ## What This Skill Does
 Provides memory management patterns for AI agents using AgentDB's persistent storage and ReasoningBank integration. Enables agents to remember conversations, learn from interactions, and maintain context across sessions.
 **Performance**: 150x-12,500x faster than traditional solutions with 100% backward compatibility.
 ## Prerequisites
 - Node.js 18+
 - AgentDB v1.0.7+ (via agentic-flow or standalone)
 - Understanding of agent architectures
 ## Quick Start with CLI
 ### Initialize AgentDB
 ```bash
 # Initialize vector database
 npx agentdb@latest init ./agents.db
 # Or with custom dimensions
 npx agentdb@latest init ./agents.db --dimension 768
 # Use preset configurations
 npx agentdb@latest init ./agents.db --preset large
 # In-memory database for testing
 npx agentdb@latest init ./memory.db --in-memory
 ```
 ### Start MCP Server for Codex
 ```bash
 # Start MCP server (integrates with Codex)
 npx agentdb@latest mcp
 # Add to Codex (one-time setup)
 Codex mcp add agentdb npx agentdb@latest mcp
 ```
 ### Create Learning Plugin
 ```bash
 # Interactive plugin wizard
 npx agentdb@latest create-plugin
 # Use template directly
 npx agentdb@latest create-plugin -t decision-transformer -n my-agent
 # Available templates:
 # - decision-transformer (sequence modeling RL)
 # - q-learning (value-based learning)
 # - sarsa (on-policy TD learning)
 # - actor-critic (policy gradient)
 # - curiosity-driven (exploration-based)
 ```
 ## Quick Start with API
 ```typescript
 import { createAgentDBAdapter } from 'agentic-flow/reasoningbank';
 // Initialize with default configuration
 const adapter = await createAgentDBAdapter({
  dbPath: '.agentdb/reasoningbank.db',
  enableLearning: true,      // Enable learning plugins
  enableReasoning: true,      // Enable reasoning agents
  quantizationType: 'scalar', // binary | scalar | product | none
  cacheSize: 1000,            // In-memory cache
 });
 // Store interaction memory
 const patternId = await adapter.insertPattern({
  id: '',
  type: 'pattern',
  domain: 'conversation',
  pattern_data: JSON.stringify({
    embedding: await computeEmbedding('What is the capital of France?'),
    pattern: {
      user: 'What is the capital of France?',
      assistant: 'The capital of France is Paris.',
      timestamp: Date.now()
    }
  }),
  confidence: 0.95,
  usage_count: 1,
  success_count: 1,
  created_at: Date.now(),
  last_used: Date.now(),
 });
 // Retrieve context with reasoning
 const context = await adapter.retrieveWithReasoning(queryEmbedding, {
  domain: 'conversation',
  k: 10,
  useMMR: true,              // Maximal Marginal Relevance
  synthesizeContext: true,    // Generate rich context
 });
 ```
 ## Memory Patterns
 ### 1. Session Memory
 ```typescript
 class SessionMemory {
  async storeMessage(role: string, content: string) {
    return await db.storeMemory({
      sessionId: this.sessionId,
      role,
      content,
      timestamp: Date.now()
    });
  }
  async getSessionHistory(limit = 20) {
    return await db.query({
      filters: { sessionId: this.sessionId },
      orderBy: 'timestamp',
      limit
    });
  }
 }
 ```
 ### 2. Long-Term Memory
 ```typescript
 // Store important facts
 await db.storeFact({
  category: 'user_preference',
  key: 'language',
  value: 'English',
  confidence: 1.0,
  source: 'explicit'
 });
 // Retrieve facts
 const prefs = await db.getFacts({
  category: 'user_preference'
 });
 ```
 ### 3. Pattern Learning
 ```typescript
 // Learn from successful interactions
 await db.storePattern({
  trigger: 'user_asks_time',
  response: 'provide_formatted_time',
  success: true,
  context: { timezone: 'UTC' }
 });
 // Apply learned patterns
 const pattern = await db.matchPattern(currentContext);
 ```
 ## Advanced Patterns
 ### Hierarchical Memory
 ```typescript
 // Organize memory in hierarchy
 await memory.organize({
  immediate: recentMessages,    // Last 10 messages
  shortTerm: sessionContext,    // Current session
  longTerm: importantFacts,     // Persistent facts
  semantic: embeddedKnowledge   // Vector search
 });
 ```
 ### Memory Consolidation
 ```typescript
 // Periodically consolidate memories
 await memory.consolidate({
  strategy: 'importance',       // Keep important memories
  maxSize: 10000,              // Size limit
  minScore: 0.5                // Relevance threshold
 });
 ```
 ## CLI Operations
 ### Query Database
 ```bash
 # Query with vector embedding
 npx agentdb@latest query ./agents.db "[0.1,0.2,0.3,...]"
 # Top-k results
 npx agentdb@latest query ./agents.db "[0.1,0.2,0.3]" -k 10
 # With similarity threshold
 npx agentdb@latest query ./agents.db "0.1 0.2 0.3" -t 0.75
 # JSON output
 npx agentdb@latest query ./agents.db "[...]" -f json
 ```
 ### Import/Export Data
 ```bash
 # Export vectors to file
 npx agentdb@latest export ./agents.db ./backup.json
 # Import vectors from file
 npx agentdb@latest import ./backup.json
 # Get database statistics
 npx agentdb@latest stats ./agents.db
 ```
 ### Performance Benchmarks
 ```bash
 # Run performance benchmarks
 npx agentdb@latest benchmark
 # Results show:
 # - Pattern Search: 150x faster (100µs vs 15ms)
 # - Batch Insert: 500x faster (2ms vs 1s)
 # - Large-scale Query: 12,500x faster (8ms vs 100s)
 ```
 ## Integration with ReasoningBank
 ```typescript
 import { createAgentDBAdapter, migrateToAgentDB } from 'agentic-flow/reasoningbank';
 // Migrate from legacy ReasoningBank
 const result = await migrateToAgentDB(
  '.swarm/memory.db',           // Source (legacy)
  '.agentdb/reasoningbank.db'   // Destination (AgentDB)
 );
 console.log(`✅ Migrated ${result.patternsMigrated} patterns`);
 // Train learning model
 const adapter = await createAgentDBAdapter({
  enableLearning: true,
 });
 await adapter.train({
  epochs: 50,
  batchSize: 32,
 });
 // Get optimal strategy with reasoning
 const result = await adapter.retrieveWithReasoning(queryEmbedding, {
  domain: 'task-planning',
  synthesizeContext: true,
  optimizeMemory: true,
 });
 ```
 ## Learning Plugins
 ### Available Algorithms (9 Total)
 1. **Decision Transformer** - Sequence modeling RL (recommended)
 2. **Q-Learning** - Value-based learning
 3. **SARSA** - On-policy TD learning
 4. **Actor-Critic** - Policy gradient with baseline
 5. **Active Learning** - Query selection
 6. **Adversarial Training** - Robustness
 7. **Curriculum Learning** - Progressive difficulty
 8. **Federated Learning** - Distributed learning
 9. **Multi-task Learning** - Transfer learning
 ### List and Manage Plugins
 ```bash
 # List available plugins
 npx agentdb@latest list-plugins
 # List plugin templates
 npx agentdb@latest list-templates
 # Get plugin info
 npx agentdb@latest plugin-info <name>
 ```
 ## Reasoning Agents (4 Modules)
 1. **PatternMatcher** - Find similar patterns with HNSW indexing
 2. **ContextSynthesizer** - Generate rich context from multiple sources
 3. **MemoryOptimizer** - Consolidate similar patterns, prune low-quality
 4. **ExperienceCurator** - Quality-based experience filtering
 ## Best Practices
 1. **Enable quantization**: Use scalar/binary for 4-32x memory reduction
 2. **Use caching**: 1000 pattern cache for <1ms retrieval
 3. **Batch operations**: 500x faster than individual inserts
 4. **Train regularly**: Update learning models with new experiences
 5. **Enable reasoning**: Automatic context synthesis and optimization
 6. **Monitor metrics**: Use `stats` command to track performance
 ## Troubleshooting
 ### Issue: Memory growing too large
 ```bash
 # Check database size
 npx agentdb@latest stats ./agents.db
 # Enable quantization
 # Use 'binary' (32x smaller) or 'scalar' (4x smaller)
 ```
 ### Issue: Slow search performance
 ```bash
 # Enable HNSW indexing and caching
 # Results: <100µs search time
 ```
 ### Issue: Migration from legacy ReasoningBank
 ```bash
 # Automatic migration with validation
 npx agentdb@latest migrate --source .swarm/memory.db
 ```
 ## Performance Characteristics
 - **Vector Search**: <100µs (HNSW indexing)
 - **Pattern Retrieval**: <1ms (with cache)
 - **Batch Insert**: 2ms for 100 patterns
 - **Memory Efficiency**: 4-32x reduction with quantization
 - **Backward Compatibility**: 100% compatible with ReasoningBank API
 ## Learn More
 - GitHub: https://github.com/ruvnet/agentic-flow/tree/main/packages/agentdb
 - Documentation: node_modules/agentic-flow/docs/AGENTDB_INTEGRATION.md
 - MCP Integration: `npx agentdb@latest mcp` for Codex
 - Website: https://agentdb.ruv.io
@@ -1,509 +0,0 @@
 ---
 name: "AgentDB Performance Optimization"
 description: "Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors."
 ---
 # AgentDB Performance Optimization
 ## What This Skill Does
 Provides comprehensive performance optimization techniques for AgentDB vector databases. Achieve 150x-12,500x performance improvements through quantization, HNSW indexing, caching strategies, and batch operations. Reduce memory usage by 4-32x while maintaining accuracy.
 **Performance**: <100µs vector search, <1ms pattern retrieval, 2ms batch insert for 100 vectors.
 ## Prerequisites
 - Node.js 18+
 - AgentDB v1.0.7+ (via agentic-flow)
 - Existing AgentDB database or application
 ---
 ## Quick Start
 ### Run Performance Benchmarks
 ```bash
 # Comprehensive performance benchmarking
 npx agentdb@latest benchmark
 # Results show:
 # ✅ Pattern Search: 150x faster (100µs vs 15ms)
 # ✅ Batch Insert: 500x faster (2ms vs 1s for 100 vectors)
 # ✅ Large-scale Query: 12,500x faster (8ms vs 100s at 1M vectors)
 # ✅ Memory Efficiency: 4-32x reduction with quantization
 ```
 ### Enable Optimizations
 ```typescript
 import { createAgentDBAdapter } from 'agentic-flow/reasoningbank';
 // Optimized configuration
 const adapter = await createAgentDBAdapter({
  dbPath: '.agentdb/optimized.db',
  quantizationType: 'binary',   // 32x memory reduction
  cacheSize: 1000,               // In-memory cache
  enableLearning: true,
  enableReasoning: true,
 });
 ```
 ---
 ## Quantization Strategies
 ### 1. Binary Quantization (32x Reduction)
 **Best For**: Large-scale deployments (1M+ vectors), memory-constrained environments
 **Trade-off**: ~2-5% accuracy loss, 32x memory reduction, 10x faster
 ```typescript
 const adapter = await createAgentDBAdapter({
  quantizationType: 'binary',
  // 768-dim float32 (3072 bytes) → 96 bytes binary
  // 1M vectors: 3GB → 96MB
 });
 ```
 **Use Cases**:
 - Mobile/edge deployment
 - Large-scale vector storage (millions of vectors)
 - Real-time search with memory constraints
 **Performance**:
 - Memory: 32x smaller
 - Search Speed: 10x faster (bit operations)
 - Accuracy: 95-98% of original
 ### 2. Scalar Quantization (4x Reduction)
 **Best For**: Balanced performance/accuracy, moderate datasets
 **Trade-off**: ~1-2% accuracy loss, 4x memory reduction, 3x faster
 ```typescript
 const adapter = await createAgentDBAdapter({
  quantizationType: 'scalar',
  // 768-dim float32 (3072 bytes) → 768 bytes (uint8)
  // 1M vectors: 3GB → 768MB
 });
 ```
 **Use Cases**:
 - Production applications requiring high accuracy
 - Medium-scale deployments (10K-1M vectors)
 - General-purpose optimization
 **Performance**:
 - Memory: 4x smaller
 - Search Speed: 3x faster
 - Accuracy: 98-99% of original
 ### 3. Product Quantization (8-16x Reduction)
 **Best For**: High-dimensional vectors, balanced compression
 **Trade-off**: ~3-7% accuracy loss, 8-16x memory reduction, 5x faster
 ```typescript
 const adapter = await createAgentDBAdapter({
  quantizationType: 'product',
  // 768-dim float32 (3072 bytes) → 48-96 bytes
  // 1M vectors: 3GB → 192MB
 });
 ```
 **Use Cases**:
 - High-dimensional embeddings (>512 dims)
 - Image/video embeddings
 - Large-scale similarity search
 **Performance**:
 - Memory: 8-16x smaller
 - Search Speed: 5x faster
 - Accuracy: 93-97% of original
 ### 4. No Quantization (Full Precision)
 **Best For**: Maximum accuracy, small datasets
 **Trade-off**: No accuracy loss, full memory usage
 ```typescript
 const adapter = await createAgentDBAdapter({
  quantizationType: 'none',
  // Full float32 precision
 });
 ```
 ---
 ## HNSW Indexing
 **Hierarchical Navigable Small World** - O(log n) search complexity
 ### Automatic HNSW
 AgentDB automatically builds HNSW indices:
 ```typescript
 const adapter = await createAgentDBAdapter({
  dbPath: '.agentdb/vectors.db',
  // HNSW automatically enabled
 });
 // Search with HNSW (100µs vs 15ms linear scan)
 const results = await adapter.retrieveWithReasoning(queryEmbedding, {
  k: 10,
 });
 ```
 ### HNSW Parameters
 ```typescript
 // Advanced HNSW configuration
 const adapter = await createAgentDBAdapter({
  dbPath: '.agentdb/vectors.db',
  hnswM: 16,              // Connections per layer (default: 16)
  hnswEfConstruction: 200, // Build quality (default: 200)
  hnswEfSearch: 100,       // Search quality (default: 100)
 });
 ```
 **Parameter Tuning**:
 - **M** (connections): Higher = better recall, more memory
  - Small datasets (<10K): M = 8
  - Medium datasets (10K-100K): M = 16
  - Large datasets (>100K): M = 32
 - **efConstruction**: Higher = better index quality, slower build
  - Fast build: 100
  - Balanced: 200 (default)
  - High quality: 400
 - **efSearch**: Higher = better recall, slower search
  - Fast search: 50
  - Balanced: 100 (default)
  - High recall: 200
 ---
 ## Caching Strategies
 ### In-Memory Pattern Cache
 ```typescript
 const adapter = await createAgentDBAdapter({
  cacheSize: 1000,  // Cache 1000 most-used patterns
 });
 // First retrieval: ~2ms (database)
 // Subsequent: <1ms (cache hit)
 const result = await adapter.retrieveWithReasoning(queryEmbedding, {
  k: 10,
 });
 ```
 **Cache Tuning**:
 - Small applications: 100-500 patterns
 - Medium applications: 500-2000 patterns
 - Large applications: 2000-5000 patterns
 ### LRU Cache Behavior
 ```typescript
 // Cache automatically evicts least-recently-used patterns
 // Most frequently accessed patterns stay in cache
 // Monitor cache performance
 const stats = await adapter.getStats();
 console.log('Cache Hit Rate:', stats.cacheHitRate);
 // Aim for >80% hit rate
 ```
 ---
 ## Batch Operations
 ### Batch Insert (500x Faster)
 ```typescript
 // ❌ SLOW: Individual inserts
 for (const doc of documents) {
  await adapter.insertPattern({ /* ... */ });  // 1s for 100 docs
 }
 // ✅ FAST: Batch insert
 const patterns = documents.map(doc => ({
  id: '',
  type: 'document',
  domain: 'knowledge',
  pattern_data: JSON.stringify({
    embedding: doc.embedding,
    text: doc.text,
  }),
  confidence: 1.0,
  usage_count: 0,
  success_count: 0,
  created_at: Date.now(),
  last_used: Date.now(),
 }));
 // Insert all at once (2ms for 100 docs)
 for (const pattern of patterns) {
  await adapter.insertPattern(pattern);
 }
 ```
 ### Batch Retrieval
 ```typescript
 // Retrieve multiple queries efficiently
 const queries = [queryEmbedding1, queryEmbedding2, queryEmbedding3];
 // Parallel retrieval
 const results = await Promise.all(
  queries.map(q => adapter.retrieveWithReasoning(q, { k: 5 }))
 );
 ```
 ---
 ## Memory Optimization
 ### Automatic Consolidation
 ```typescript
 // Enable automatic pattern consolidation
 const result = await adapter.retrieveWithReasoning(queryEmbedding, {
  domain: 'documents',
  optimizeMemory: true,  // Consolidate similar patterns
  k: 10,
 });
 console.log('Optimizations:', result.optimizations);
 // {
 //   consolidated: 15,  // Merged 15 similar patterns
 //   pruned: 3,         // Removed 3 low-quality patterns
 //   improved_quality: 0.12  // 12% quality improvement
 // }
 ```
 ### Manual Optimization
 ```typescript
 // Manually trigger optimization
 await adapter.optimize();
 // Get statistics
 const stats = await adapter.getStats();
 console.log('Before:', stats.totalPatterns);
 console.log('After:', stats.totalPatterns);  // Reduced by ~10-30%
 ```
 ### Pruning Strategies
 ```typescript
 // Prune low-confidence patterns
 await adapter.prune({
  minConfidence: 0.5,     // Remove confidence < 0.5
  minUsageCount: 2,       // Remove usage_count < 2
  maxAge: 30 * 24 * 3600, // Remove >30 days old
 });
 ```
 ---
 ## Performance Monitoring
 ### Database Statistics
 ```bash
 # Get comprehensive stats
 npx agentdb@latest stats .agentdb/vectors.db
 # Output:
 # Total Patterns: 125,430
 # Database Size: 47.2 MB (with binary quantization)
 # Avg Confidence: 0.87
 # Domains: 15
 # Cache Hit Rate: 84%
 # Index Type: HNSW
 ```
 ### Runtime Metrics
 ```typescript
 const stats = await adapter.getStats();
 console.log('Performance Metrics:');
 console.log('Total Patterns:', stats.totalPatterns);
 console.log('Database Size:', stats.dbSize);
 console.log('Avg Confidence:', stats.avgConfidence);
 console.log('Cache Hit Rate:', stats.cacheHitRate);
 console.log('Search Latency (avg):', stats.avgSearchLatency);
 console.log('Insert Latency (avg):', stats.avgInsertLatency);
 ```
 ---
 ## Optimization Recipes
 ### Recipe 1: Maximum Speed (Sacrifice Accuracy)
 ```typescript
 const adapter = await createAgentDBAdapter({
  quantizationType: 'binary',  // 32x memory reduction
  cacheSize: 5000,             // Large cache
  hnswM: 8,                    // Fewer connections = faster
  hnswEfSearch: 50,            // Low search quality = faster
 });
 // Expected: <50µs search, 90-95% accuracy
 ```
 ### Recipe 2: Balanced Performance
 ```typescript
 const adapter = await createAgentDBAdapter({
  quantizationType: 'scalar',  // 4x memory reduction
  cacheSize: 1000,             // Standard cache
  hnswM: 16,                   // Balanced connections
  hnswEfSearch: 100,           // Balanced quality
 });
 // Expected: <100µs search, 98-99% accuracy
 ```
 ### Recipe 3: Maximum Accuracy
 ```typescript
 const adapter = await createAgentDBAdapter({
  quantizationType: 'none',    // No quantization
  cacheSize: 2000,             // Large cache
  hnswM: 32,                   // Many connections
  hnswEfSearch: 200,           // High search quality
 });
 // Expected: <200µs search, 100% accuracy
 ```
 ### Recipe 4: Memory-Constrained (Mobile/Edge)
 ```typescript
 const adapter = await createAgentDBAdapter({
  quantizationType: 'binary',  // 32x memory reduction
  cacheSize: 100,              // Small cache
  hnswM: 8,                    // Minimal connections
 });
 // Expected: <100µs search, ~10MB for 100K vectors
 ```
 ---
 ## Scaling Strategies
 ### Small Scale (<10K vectors)
 ```typescript
 const adapter = await createAgentDBAdapter({
  quantizationType: 'none',    // Full precision
  cacheSize: 500,
  hnswM: 8,
 });
 ```
 ### Medium Scale (10K-100K vectors)
 ```typescript
 const adapter = await createAgentDBAdapter({
  quantizationType: 'scalar',  // 4x reduction
  cacheSize: 1000,
  hnswM: 16,
 });
 ```
 ### Large Scale (100K-1M vectors)
 ```typescript
 const adapter = await createAgentDBAdapter({
  quantizationType: 'binary',  // 32x reduction
  cacheSize: 2000,
  hnswM: 32,
 });
 ```
 ### Massive Scale (>1M vectors)
 ```typescript
 const adapter = await createAgentDBAdapter({
  quantizationType: 'product',  // 8-16x reduction
  cacheSize: 5000,
  hnswM: 48,
  hnswEfConstruction: 400,
 });
 ```
 ---
 ## Troubleshooting
 ### Issue: High memory usage
 ```bash
 # Check database size
 npx agentdb@latest stats .agentdb/vectors.db
 # Enable quantization
 # Use 'binary' for 32x reduction
 ```
 ### Issue: Slow search performance
 ```typescript
 // Increase cache size
 const adapter = await createAgentDBAdapter({
  cacheSize: 2000,  // Increase from 1000
 });
 // Reduce search quality (faster)
 const result = await adapter.retrieveWithReasoning(queryEmbedding, {
  k: 5,  // Reduce from 10
 });
 ```
 ### Issue: Low accuracy
 ```typescript
 // Disable or use lighter quantization
 const adapter = await createAgentDBAdapter({
  quantizationType: 'scalar',  // Instead of 'binary'
  hnswEfSearch: 200,           // Higher search quality
 });
 ```
 ---
 ## Performance Benchmarks
 **Test System**: AMD Ryzen 9 5950X, 64GB RAM
 | Operation | Vector Count | No Optimization | Optimized | Improvement |
 |-----------|-------------|-----------------|-----------|-------------|
 | Search | 10K | 15ms | 100µs | 150x |
 | Search | 100K | 150ms | 120µs | 1,250x |
 | Search | 1M | 100s | 8ms | 12,500x |
 | Batch Insert (100) | - | 1s | 2ms | 500x |
 | Memory Usage | 1M | 3GB | 96MB | 32x (binary) |
 ---
 ## Learn More
 - **Quantization Paper**: docs/quantization-techniques.pdf
 - **HNSW Algorithm**: docs/hnsw-index.pdf
 - **GitHub**: https://github.com/ruvnet/agentic-flow/tree/main/packages/agentdb
 - **Website**: https://agentdb.ruv.io
 ---
 **Category**: Performance / Optimization
 **Difficulty**: Intermediate
 **Estimated Time**: 20-30 minutes
@@ -1,339 +0,0 @@
 ---
 name: "AgentDB Vector Search"
 description: "Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases."
 ---
 # AgentDB Vector Search
 ## What This Skill Does
 Implements vector-based semantic search using AgentDB's high-performance vector database with **150x-12,500x faster** operations than traditional solutions. Features HNSW indexing, quantization, and sub-millisecond search (<100µs).
 ## Prerequisites
 - Node.js 18+
 - AgentDB v1.0.7+ (via agentic-flow or standalone)
 - OpenAI API key (for embeddings) or custom embedding model
 ## Quick Start with CLI
 ### Initialize Vector Database
 ```bash
 # Initialize with default dimensions (1536 for OpenAI ada-002)
 npx agentdb@latest init ./vectors.db
 # Custom dimensions for different embedding models
 npx agentdb@latest init ./vectors.db --dimension 768  # sentence-transformers
 npx agentdb@latest init ./vectors.db --dimension 384  # all-MiniLM-L6-v2
 # Use preset configurations
 npx agentdb@latest init ./vectors.db --preset small   # <10K vectors
 npx agentdb@latest init ./vectors.db --preset medium  # 10K-100K vectors
 npx agentdb@latest init ./vectors.db --preset large   # >100K vectors
 # In-memory database for testing
 npx agentdb@latest init ./vectors.db --in-memory
 ```
 ### Query Vector Database
 ```bash
 # Basic similarity search
 npx agentdb@latest query ./vectors.db "[0.1,0.2,0.3,...]"
 # Top-k results
 npx agentdb@latest query ./vectors.db "[0.1,0.2,0.3]" -k 10
 # With similarity threshold (cosine similarity)
 npx agentdb@latest query ./vectors.db "0.1 0.2 0.3" -t 0.75 -m cosine
 # Different distance metrics
 npx agentdb@latest query ./vectors.db "[...]" -m euclidean  # L2 distance
 npx agentdb@latest query ./vectors.db "[...]" -m dot        # Dot product
 # JSON output for automation
 npx agentdb@latest query ./vectors.db "[...]" -f json -k 5
 # Verbose output with distances
 npx agentdb@latest query ./vectors.db "[...]" -v
 ```
 ### Import/Export Vectors
 ```bash
 # Export vectors to JSON
 npx agentdb@latest export ./vectors.db ./backup.json
 # Import vectors from JSON
 npx agentdb@latest import ./backup.json
 # Get database statistics
 npx agentdb@latest stats ./vectors.db
 ```
 ## Quick Start with API
 ```typescript
 import { createAgentDBAdapter, computeEmbedding } from 'agentic-flow/reasoningbank';
 // Initialize with vector search optimizations
 const adapter = await createAgentDBAdapter({
  dbPath: '.agentdb/vectors.db',
  enableLearning: false,       // Vector search only
  enableReasoning: true,       // Enable semantic matching
  quantizationType: 'binary',  // 32x memory reduction
  cacheSize: 1000,             // Fast retrieval
 });
 // Store document with embedding
 const text = "The quantum computer achieved 100 qubits";
 const embedding = await computeEmbedding(text);
 await adapter.insertPattern({
  id: '',
  type: 'document',
  domain: 'technology',
  pattern_data: JSON.stringify({
    embedding,
    text,
    metadata: { category: "quantum", date: "2025-01-15" }
  }),
  confidence: 1.0,
  usage_count: 0,
  success_count: 0,
  created_at: Date.now(),
  last_used: Date.now(),
 });
 // Semantic search with MMR (Maximal Marginal Relevance)
 const queryEmbedding = await computeEmbedding("quantum computing advances");
 const results = await adapter.retrieveWithReasoning(queryEmbedding, {
  domain: 'technology',
  k: 10,
  useMMR: true,              // Diverse results
  synthesizeContext: true,    // Rich context
 });
 ```
 ## Core Features
 ### 1. Vector Storage
 ```typescript
 // Store with automatic embedding
 await db.storeWithEmbedding({
  content: "Your document text",
  metadata: { source: "docs", page: 42 }
 });
 ```
 ### 2. Similarity Search
 ```typescript
 // Find similar documents
 const similar = await db.findSimilar("quantum computing", {
  limit: 5,
  minScore: 0.75
 });
 ```
 ### 3. Hybrid Search (Vector + Metadata)
 ```typescript
 // Combine vector similarity with metadata filtering
 const results = await db.hybridSearch({
  query: "machine learning models",
  filters: {
    category: "research",
    date: { $gte: "2024-01-01" }
  },
  limit: 20
 });
 ```
 ## Advanced Usage
 ### RAG (Retrieval Augmented Generation)
 ```typescript
 // Build RAG pipeline
 async function ragQuery(question: string) {
  // 1. Get relevant context
  const context = await db.searchSimilar(
    await embed(question),
    { limit: 5, threshold: 0.7 }
  );
  // 2. Generate answer with context
  const prompt = `Context: ${context.map(c => c.text).join('\n')}
 Question: ${question}`;
  return await llm.generate(prompt);
 }
 ```
 ### Batch Operations
 ```typescript
 // Efficient batch storage
 await db.batchStore(documents.map(doc => ({
  text: doc.content,
  embedding: doc.vector,
  metadata: doc.meta
 })));
 ```
 ## MCP Server Integration
 ```bash
 # Start AgentDB MCP server for Codex
 npx agentdb@latest mcp
 # Add to Codex (one-time setup)
 Codex mcp add agentdb npx agentdb@latest mcp
 # Now use MCP tools in Codex:
 # - agentdb_query: Semantic vector search
 # - agentdb_store: Store documents with embeddings
 # - agentdb_stats: Database statistics
 ```
 ## Performance Benchmarks
 ```bash
 # Run comprehensive benchmarks
 npx agentdb@latest benchmark
 # Results:
 # ✅ Pattern Search: 150x faster (100µs vs 15ms)
 # ✅ Batch Insert: 500x faster (2ms vs 1s for 100 vectors)
 # ✅ Large-scale Query: 12,500x faster (8ms vs 100s at 1M vectors)
 # ✅ Memory Efficiency: 4-32x reduction with quantization
 ```
 ## Quantization Options
 AgentDB provides multiple quantization strategies for memory efficiency:
 ### Binary Quantization (32x reduction)
 ```typescript
 const adapter = await createAgentDBAdapter({
  quantizationType: 'binary',  // 768-dim → 96 bytes
 });
 ```
 ### Scalar Quantization (4x reduction)
 ```typescript
 const adapter = await createAgentDBAdapter({
  quantizationType: 'scalar',  // 768-dim → 768 bytes
 });
 ```
 ### Product Quantization (8-16x reduction)
 ```typescript
 const adapter = await createAgentDBAdapter({
  quantizationType: 'product',  // 768-dim → 48-96 bytes
 });
 ```
 ## Distance Metrics
 ```bash
 # Cosine similarity (default, best for most use cases)
 npx agentdb@latest query ./db.sqlite "[...]" -m cosine
 # Euclidean distance (L2 norm)
 npx agentdb@latest query ./db.sqlite "[...]" -m euclidean
 # Dot product (for normalized vectors)
 npx agentdb@latest query ./db.sqlite "[...]" -m dot
 ```
 ## Advanced Features
 ### HNSW Indexing
 - **O(log n) search complexity**
 - **Sub-millisecond retrieval** (<100µs)
 - **Automatic index building**
 ### Caching
 - **1000 pattern in-memory cache**
 - **<1ms pattern retrieval**
 - **Automatic cache invalidation**
 ### MMR (Maximal Marginal Relevance)
 - **Diverse result sets**
 - **Avoid redundancy**
 - **Balance relevance and diversity**
 ## Performance Tips
 1. **Enable HNSW indexing**: Automatic with AgentDB, 10-100x faster
 2. **Use quantization**: Binary (32x), Scalar (4x), Product (8-16x) memory reduction
 3. **Batch operations**: 500x faster for bulk inserts
 4. **Match dimensions**: 1536 (OpenAI), 768 (sentence-transformers), 384 (MiniLM)
 5. **Similarity threshold**: Start at 0.7 for quality, adjust based on use case
 6. **Enable caching**: 1000 pattern cache for frequent queries
 ## Troubleshooting
 ### Issue: Slow search performance
 ```bash
 # Check if HNSW indexing is enabled (automatic)
 npx agentdb@latest stats ./vectors.db
 # Expected: <100µs search time
 ```
 ### Issue: High memory usage
 ```bash
 # Enable binary quantization (32x reduction)
 # Use in adapter: quantizationType: 'binary'
 ```
 ### Issue: Poor relevance
 ```bash
 # Adjust similarity threshold
 npx agentdb@latest query ./db.sqlite "[...]" -t 0.8  # Higher threshold
 # Or use MMR for diverse results
 # Use in adapter: useMMR: true
 ```
 ### Issue: Wrong dimensions
 ```bash
 # Check embedding model dimensions:
 # - OpenAI ada-002: 1536
 # - sentence-transformers: 768
 # - all-MiniLM-L6-v2: 384
 npx agentdb@latest init ./db.sqlite --dimension 768
 ```
 ## Database Statistics
 ```bash
 # Get comprehensive stats
 npx agentdb@latest stats ./vectors.db
 # Shows:
 # - Total patterns/vectors
 # - Database size
 # - Average confidence
 # - Domains distribution
 # - Index status
 ```
 ## Performance Characteristics
 - **Vector Search**: <100µs (HNSW indexing)
 - **Pattern Retrieval**: <1ms (with cache)
 - **Batch Insert**: 2ms for 100 vectors
 - **Memory Efficiency**: 4-32x reduction with quantization
 - **Scalability**: Handles 1M+ vectors efficiently
 - **Latency**: Sub-millisecond for most operations
 ## Learn More
 - GitHub: https://github.com/ruvnet/agentic-flow/tree/main/packages/agentdb
 - Documentation: node_modules/agentic-flow/docs/AGENTDB_INTEGRATION.md
 - MCP Integration: `npx agentdb@latest mcp` for Codex
 - Website: https://agentdb.ruv.io
 - CLI Help: `npx agentdb@latest --help`
 - Command Help: `npx agentdb@latest help <command>`
@@ -1,204 +0,0 @@
 ---
 name: browser
 description: Web browser automation with AI-optimized snapshots for Codex-flow agents
 version: 1.0.0
 triggers:
  - /browser
  - browse
  - web automation
  - scrape
  - navigate
  - screenshot
 tools:
  - browser/open
  - browser/snapshot
  - browser/click
  - browser/fill
  - browser/screenshot
  - browser/close
 ---
 # Browser Automation Skill
 Web browser automation using agent-browser with AI-optimized snapshots. Reduces context by 93% using element refs (@e1, @e2) instead of full DOM.
 ## Core Workflow
 ```bash
 # 1. Navigate to page
 agent-browser open <url>
 # 2. Get accessibility tree with element refs
 agent-browser snapshot -i    # -i = interactive elements only
 # 3. Interact using refs from snapshot
 agent-browser click @e2
 agent-browser fill @e3 "text"
 # 4. Re-snapshot after page changes
 agent-browser snapshot -i
 ```
 ## Quick Reference
 ### Navigation
 | Command | Description |
 |---------|-------------|
 | `open <url>` | Navigate to URL |
 | `back` | Go back |
 | `forward` | Go forward |
 | `reload` | Reload page |
 | `close` | Close browser |
 ### Snapshots (AI-Optimized)
 | Command | Description |
 |---------|-------------|
 | `snapshot` | Full accessibility tree |
 | `snapshot -i` | Interactive elements only (buttons, links, inputs) |
 | `snapshot -c` | Compact (remove empty elements) |
 | `snapshot -d 3` | Limit depth to 3 levels |
 | `screenshot [path]` | Capture screenshot (base64 if no path) |
 ### Interaction
 | Command | Description |
 |---------|-------------|
 | `click <sel>` | Click element |
 | `fill <sel> <text>` | Clear and fill input |
 | `type <sel> <text>` | Type with key events |
 | `press <key>` | Press key (Enter, Tab, etc.) |
 | `hover <sel>` | Hover element |
 | `select <sel> <val>` | Select dropdown option |
 | `check/uncheck <sel>` | Toggle checkbox |
 | `scroll <dir> [px]` | Scroll page |
 ### Get Info
 | Command | Description |
 |---------|-------------|
 | `get text <sel>` | Get text content |
 | `get html <sel>` | Get innerHTML |
 | `get value <sel>` | Get input value |
 | `get attr <sel> <attr>` | Get attribute |
 | `get title` | Get page title |
 | `get url` | Get current URL |
 ### Wait
 | Command | Description |
 |---------|-------------|
 | `wait <selector>` | Wait for element |
 | `wait <ms>` | Wait milliseconds |
 | `wait --text "text"` | Wait for text |
 | `wait --url "pattern"` | Wait for URL |
 | `wait --load networkidle` | Wait for load state |
 ### Sessions
 | Command | Description |
 |---------|-------------|
 | `--session <name>` | Use isolated session |
 | `session list` | List active sessions |
 ## Selectors
 ### Element Refs (Recommended)
 ```bash
 # Get refs from snapshot
 agent-browser snapshot -i
 # Output: button "Submit" [ref=e2]
 # Use ref to interact
 agent-browser click @e2
 ```
 ### CSS Selectors
 ```bash
 agent-browser click "#submit"
 agent-browser fill ".email-input" "test@test.com"
 ```
 ### Semantic Locators
 ```bash
 agent-browser find role button click --name "Submit"
 agent-browser find label "Email" fill "test@test.com"
 agent-browser find testid "login-btn" click
 ```
 ## Examples
 ### Login Flow
 ```bash
 agent-browser open https://example.com/login
 agent-browser snapshot -i
 agent-browser fill @e2 "user@example.com"
 agent-browser fill @e3 "password123"
 agent-browser click @e4
 agent-browser wait --url "**/dashboard"
 ```
 ### Form Submission
 ```bash
 agent-browser open https://example.com/contact
 agent-browser snapshot -i
 agent-browser fill @e1 "John Doe"
 agent-browser fill @e2 "john@example.com"
 agent-browser fill @e3 "Hello, this is my message"
 agent-browser click @e4
 agent-browser wait --text "Thank you"
 ```
 ### Data Extraction
 ```bash
 agent-browser open https://example.com/products
 agent-browser snapshot -i
 # Iterate through product refs
 agent-browser get text @e1  # Product name
 agent-browser get text @e2  # Price
 agent-browser get attr @e3 href  # Link
 ```
 ### Multi-Session (Swarm)
 ```bash
 # Session 1: Navigator
 agent-browser --session nav open https://example.com
 agent-browser --session nav state save auth.json
 # Session 2: Scraper (uses same auth)
 agent-browser --session scrape state load auth.json
 agent-browser --session scrape open https://example.com/data
 agent-browser --session scrape snapshot -i
 ```
 ## Integration with Codex Flow
 ### MCP Tools
 All browser operations are available as MCP tools with `browser/` prefix:
 - `browser/open`
 - `browser/snapshot`
 - `browser/click`
 - `browser/fill`
 - `browser/screenshot`
 - etc.
 ### Memory Integration
 ```bash
 # Store successful patterns
 npx @Codex-flow/cli memory store --namespace browser-patterns --key "login-flow" --value "snapshot->fill->click->wait"
 # Retrieve before similar task
 npx @Codex-flow/cli memory search --query "login automation"
 ```
 ### Hooks
 ```bash
 # Pre-browse hook (get context)
 npx @Codex-flow/cli hooks pre-edit --file "browser-task.ts"
 # Post-browse hook (record success)
 npx @Codex-flow/cli hooks post-task --task-id "browse-1" --success true
 ```
 ## Tips
 1. **Always use snapshots** - They're optimized for AI with refs
 2. **Prefer `-i` flag** - Gets only interactive elements, smaller output
 3. **Use refs, not selectors** - More reliable, deterministic
 4. **Re-snapshot after navigation** - Page state changes
 5. **Use sessions for parallel work** - Each session is isolated
@@ -1,874 +0,0 @@
 ---
 name: github-multi-repo
 version: 1.0.0
 description: Multi-repository coordination, synchronization, and architecture management with AI swarm orchestration
 category: github-integration
 tags: [multi-repo, synchronization, architecture, coordination, github]
 author: Codex Flow Team
 requires:
  - ruv-swarm@^1.0.11
  - gh-cli@^2.0.0
 capabilities:
  - cross-repository coordination
  - package synchronization
  - architecture optimization
  - template management
  - distributed workflows
 ---
 # GitHub Multi-Repository Coordination Skill
 ## Overview
 Advanced multi-repository coordination system that combines swarm intelligence, package synchronization, and repository architecture optimization. This skill enables organization-wide automation, cross-project collaboration, and scalable repository management.
 ## Core Capabilities
 ### 🔄 Multi-Repository Swarm Coordination
 Cross-repository AI swarm orchestration for distributed development workflows.
 ### 📦 Package Synchronization
 Intelligent dependency resolution and version alignment across multiple packages.
 ### 🏗️ Repository Architecture
 Structure optimization and template management for scalable projects.
 ### 🔗 Integration Management
 Cross-package integration testing and deployment coordination.
 ## Quick Start
 ### Initialize Multi-Repo Coordination
 ```bash
 # Basic swarm initialization
 npx Codex-flow skill run github-multi-repo init \
  --repos "org/frontend,org/backend,org/shared" \
  --topology hierarchical
 # Advanced initialization with synchronization
 npx Codex-flow skill run github-multi-repo init \
  --repos "org/frontend,org/backend,org/shared" \
  --topology mesh \
  --shared-memory \
  --sync-strategy eventual
 ```
 ### Synchronize Packages
 ```bash
 # Synchronize package versions and dependencies
 npx Codex-flow skill run github-multi-repo sync \
  --packages "Codex-flow,ruv-swarm" \
  --align-versions \
  --update-docs
 ```
 ### Optimize Architecture
 ```bash
 # Analyze and optimize repository structure
 npx Codex-flow skill run github-multi-repo optimize \
  --analyze-structure \
  --suggest-improvements \
  --create-templates
 ```
 ## Features
 ### 1. Cross-Repository Swarm Orchestration
 #### Repository Discovery
 ```javascript
 // Auto-discover related repositories with gh CLI
 const REPOS = Bash(`gh repo list my-organization --limit 100 \
  --json name,description,languages,topics \
  --jq '.[] | select(.languages | keys | contains(["TypeScript"]))'`)
 // Analyze repository dependencies
 const DEPS = Bash(`gh repo list my-organization --json name | \
  jq -r '.[].name' | while read -r repo; do
    gh api repos/my-organization/$repo/contents/package.json \
      --jq '.content' 2>/dev/null | base64 -d | jq '{name, dependencies}'
  done | jq -s '.'`)
 // Initialize swarm with discovered repositories
 mcp__claude-flow__swarm_init({
  topology: "hierarchical",
  maxAgents: 8,
  metadata: { repos: REPOS, dependencies: DEPS }
 })
 ```
 #### Synchronized Operations
 ```javascript
 // Execute synchronized changes across repositories
 [Parallel Multi-Repo Operations]:
  // Spawn coordination agents
  Task("Repository Coordinator", "Coordinate changes across all repositories", "coordinator")
  Task("Dependency Analyzer", "Analyze cross-repo dependencies", "analyst")
  Task("Integration Tester", "Validate cross-repo changes", "tester")
  // Get matching repositories
  Bash(`gh repo list org --limit 100 --json name \
    --jq '.[] | select(.name | test("-service$")) | .name' > /tmp/repos.txt`)
  // Execute task across repositories
  Bash(`cat /tmp/repos.txt | while read -r repo; do
    gh repo clone org/$repo /tmp/$repo -- --depth=1
    cd /tmp/$repo
    # Apply changes
    npm update
    npm test
    # Create PR if successful
    if [ $? -eq 0 ]; then
      git checkout -b update-dependencies-$(date +%Y%m%d)
      git add -A
      git commit -m "chore: Update dependencies"
      git push origin HEAD
      gh pr create --title "Update dependencies" --body "Automated update" --label "dependencies"
    fi
  done`)
  // Track all operations
  TodoWrite { todos: [
    { id: "discover", content: "Discover all service repositories", status: "completed" },
    { id: "update", content: "Update dependencies", status: "completed" },
    { id: "test", content: "Run integration tests", status: "in_progress" },
    { id: "pr", content: "Create pull requests", status: "pending" }
  ]}
 ```
 ### 2. Package Synchronization
 #### Version Alignment
 ```javascript
 // Synchronize package dependencies and versions
 [Complete Package Sync]:
  // Initialize sync swarm
  mcp__claude-flow__swarm_init({ topology: "mesh", maxAgents: 5 })
  // Spawn sync agents
  Task("Sync Coordinator", "Coordinate version alignment", "coordinator")
  Task("Dependency Analyzer", "Analyze dependencies", "analyst")
  Task("Integration Tester", "Validate synchronization", "tester")
  // Read package states
  Read("/workspaces/ruv-FANN/Codex-flow/Codex-flow/package.json")
  Read("/workspaces/ruv-FANN/ruv-swarm/npm/package.json")
  // Align versions using gh CLI
  Bash(`gh api repos/:owner/:repo/git/refs \
    -f ref='refs/heads/sync/package-alignment' \
    -f sha=$(gh api repos/:owner/:repo/git/refs/heads/main --jq '.object.sha')`)
  // Update package.json files
  Bash(`gh api repos/:owner/:repo/contents/package.json \
    --method PUT \
    -f message="feat: Align Node.js version requirements" \
    -f branch="sync/package-alignment" \
    -f content="$(cat aligned-package.json | base64)"`)
  // Store sync state
  mcp__claude-flow__memory_usage({
    action: "store",
    key: "sync/packages/status",
    value: {
      timestamp: Date.now(),
      packages_synced: ["Codex-flow", "ruv-swarm"],
      status: "synchronized"
    }
  })
 ```
 #### Documentation Synchronization
 ```javascript
 // Synchronize AGENTS.md files across packages
 [Documentation Sync]:
  // Get source documentation
  Bash(`gh api repos/:owner/:repo/contents/ruv-swarm/docs/AGENTS.md \
    --jq '.content' | base64 -d > /tmp/Codex-source.md`)
  // Update target documentation
  Bash(`gh api repos/:owner/:repo/contents/Codex-flow/AGENTS.md \
    --method PUT \
    -f message="docs: Synchronize AGENTS.md" \
    -f branch="sync/documentation" \
    -f content="$(cat /tmp/Codex-source.md | base64)"`)
  // Track sync status
  mcp__claude-flow__memory_usage({
    action: "store",
    key: "sync/documentation/status",
    value: { status: "synchronized", files: ["AGENTS.md"] }
  })
 ```
 #### Cross-Package Integration
 ```javascript
 // Coordinate feature implementation across packages
 [Cross-Package Feature]:
  // Push changes to all packages
  mcp__github__push_files({
    branch: "feature/github-integration",
    files: [
      {
        path: "Codex-flow/.Codex/commands/github/github-modes.md",
        content: "[GitHub modes documentation]"
      },
      {
        path: "ruv-swarm/src/github-coordinator/hooks.js",
        content: "[GitHub coordination hooks]"
      }
    ],
    message: "feat: Add GitHub workflow integration"
  })
  // Create coordinated PR
  Bash(`gh pr create \
    --title "Feature: GitHub Workflow Integration" \
    --body "## 🚀 GitHub Integration
 ### Features
 - ✅ Multi-repo coordination
 - ✅ Package synchronization
 - ✅ Architecture optimization
 ### Testing
 - [x] Package dependency verification
 - [x] Integration tests
 - [x] Cross-package compatibility"`)
 ```
 ### 3. Repository Architecture
 #### Structure Analysis
 ```javascript
 // Analyze and optimize repository structure
 [Architecture Analysis]:
  // Initialize architecture swarm
  mcp__claude-flow__swarm_init({ topology: "hierarchical", maxAgents: 6 })
  // Spawn architecture agents
  Task("Senior Architect", "Analyze repository structure", "architect")
  Task("Structure Analyst", "Identify optimization opportunities", "analyst")
  Task("Performance Optimizer", "Optimize structure for scalability", "optimizer")
  Task("Best Practices Researcher", "Research architecture patterns", "researcher")
  // Analyze current structures
  LS("/workspaces/ruv-FANN/Codex-flow/Codex-flow")
  LS("/workspaces/ruv-FANN/ruv-swarm/npm")
  // Search for best practices
  Bash(`gh search repos "language:javascript template architecture" \
    --limit 10 \
    --json fullName,description,stargazersCount \
    --sort stars \
    --order desc`)
  // Store analysis results
  mcp__claude-flow__memory_usage({
    action: "store",
    key: "architecture/analysis/results",
    value: {
      repositories_analyzed: ["Codex-flow", "ruv-swarm"],
      optimization_areas: ["structure", "workflows", "templates"],
      recommendations: ["standardize_structure", "improve_workflows"]
    }
  })
 ```
 #### Template Creation
 ```javascript
 // Create standardized repository template
 [Template Creation]:
  // Create template repository
  mcp__github__create_repository({
    name: "Codex-project-template",
    description: "Standardized template for Codex projects",
    private: false,
    autoInit: true
  })
  // Push template structure
  mcp__github__push_files({
    repo: "Codex-project-template",
    files: [
      {
        path: ".Codex/commands/github/github-modes.md",
        content: "[GitHub modes template]"
      },
      {
        path: ".Codex/config.json",
        content: JSON.stringify({
          version: "1.0",
          mcp_servers: {
            "ruv-swarm": {
              command: "npx",
              args: ["ruv-swarm", "mcp", "start"]
            }
          }
        })
      },
      {
        path: "AGENTS.md",
        content: "[Standardized AGENTS.md]"
      },
      {
        path: "package.json",
        content: JSON.stringify({
          name: "Codex-project-template",
          engines: { node: ">=20.0.0" },
          dependencies: { "ruv-swarm": "^1.0.11" }
        })
      }
    ],
    message: "feat: Create standardized template"
  })
 ```
 #### Cross-Repository Standardization
 ```javascript
 // Synchronize structure across repositories
 [Structure Standardization]:
  const repositories = ["Codex-flow", "ruv-swarm", "Codex-extensions"]
  // Update common files across all repositories
  repositories.forEach(repo => {
    mcp__github__create_or_update_file({
      repo: "ruv-FANN",
      path: `${repo}/.github/workflows/integration.yml`,
      content: `name: Integration Tests
 on: [push, pull_request]
 jobs:
  test:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - uses: actions/setup-node@v3
        with: { node-version: '20' }
      - run: npm install && npm test`,
      message: "ci: Standardize integration workflow",
      branch: "structure/standardization"
    })
  })
 ```
 ### 4. Orchestration Workflows
 #### Dependency Management
 ```javascript
 // Update dependencies across all repositories
 [Organization-Wide Dependency Update]:
  // Create tracking issue
  TRACKING_ISSUE=$(Bash(`gh issue create \
    --title "Dependency Update: typescript@5.0.0" \
    --body "Tracking TypeScript update across all repositories" \
    --label "dependencies,tracking" \
    --json number -q .number`))
  // Find all TypeScript repositories
  TS_REPOS=$(Bash(`gh repo list org --limit 100 --json name | \
    jq -r '.[].name' | while read -r repo; do
      if gh api repos/org/$repo/contents/package.json 2>/dev/null | \
         jq -r '.content' | base64 -d | grep -q '"typescript"'; then
        echo "$repo"
      fi
    done`))
  // Update each repository
  Bash(`echo "$TS_REPOS" | while read -r repo; do
    gh repo clone org/$repo /tmp/$repo -- --depth=1
    cd /tmp/$repo
    npm install --save-dev typescript@5.0.0
    if npm test; then
      git checkout -b update-typescript-5
      git add package.json package-lock.json
      git commit -m "chore: Update TypeScript to 5.0.0
 Part of #$TRACKING_ISSUE"
      git push origin HEAD
      gh pr create \
        --title "Update TypeScript to 5.0.0" \
        --body "Updates TypeScript\n\nTracking: #$TRACKING_ISSUE" \
        --label "dependencies"
    else
      gh issue comment $TRACKING_ISSUE \
        --body "❌ Failed to update $repo - tests failing"
    fi
  done`)
 ```
 #### Refactoring Operations
 ```javascript
 // Coordinate large-scale refactoring
 [Cross-Repo Refactoring]:
  // Initialize refactoring swarm
  mcp__claude-flow__swarm_init({ topology: "mesh", maxAgents: 8 })
  // Spawn specialized agents
  Task("Refactoring Coordinator", "Coordinate refactoring across repos", "coordinator")
  Task("Impact Analyzer", "Analyze refactoring impact", "analyst")
  Task("Code Transformer", "Apply refactoring changes", "coder")
  Task("Migration Guide Creator", "Create migration documentation", "documenter")
  Task("Integration Tester", "Validate refactored code", "tester")
  // Execute refactoring
  mcp__claude-flow__task_orchestrate({
    task: "Rename OldAPI to NewAPI across all repositories",
    strategy: "sequential",
    priority: "high"
  })
 ```
 #### Security Updates
 ```javascript
 // Coordinate security patches
 [Security Patch Deployment]:
  // Scan all repositories
  Bash(`gh repo list org --limit 100 --json name | jq -r '.[].name' | \
    while read -r repo; do
      gh repo clone org/$repo /tmp/$repo -- --depth=1
      cd /tmp/$repo
      npm audit --json > /tmp/audit-$repo.json
    done`)
  // Apply patches
  Bash(`for repo in /tmp/audit-*.json; do
    if [ $(jq '.vulnerabilities | length' $repo) -gt 0 ]; then
      cd /tmp/$(basename $repo .json | sed 's/audit-//')
      npm audit fix
      if npm test; then
        git checkout -b security/patch-$(date +%Y%m%d)
        git add -A
        git commit -m "security: Apply security patches"
        git push origin HEAD
        gh pr create --title "Security patches" --label "security"
      fi
    fi
  done`)
 ```
 ## Configuration
 ### Multi-Repo Config File
 ```yaml
 # .swarm/multi-repo.yml
 version: 1
 organization: my-org
 repositories:
  - name: frontend
    url: github.com/my-org/frontend
    role: ui
    agents: [coder, designer, tester]
  - name: backend
    url: github.com/my-org/backend
    role: api
    agents: [architect, coder, tester]
  - name: shared
    url: github.com/my-org/shared
    role: library
    agents: [analyst, coder]
 coordination:
  topology: hierarchical
  communication: webhook
  memory: redis://shared-memory
 dependencies:
  - from: frontend
    to: [backend, shared]
  - from: backend
    to: [shared]
 ```
 ### Repository Roles
 ```javascript
 {
  "roles": {
    "ui": {
      "responsibilities": ["user-interface", "ux", "accessibility"],
      "default-agents": ["designer", "coder", "tester"]
    },
    "api": {
      "responsibilities": ["endpoints", "business-logic", "data"],
      "default-agents": ["architect", "coder", "security"]
    },
    "library": {
      "responsibilities": ["shared-code", "utilities", "types"],
      "default-agents": ["analyst", "coder", "documenter"]
    }
  }
 }
 ```
 ## Communication Strategies
 ### 1. Webhook-Based Coordination
 ```javascript
 const { MultiRepoSwarm } = require('ruv-swarm');
 const swarm = new MultiRepoSwarm({
  webhook: {
    url: 'https://swarm-coordinator.example.com',
    secret: process.env.WEBHOOK_SECRET
  }
 });
 swarm.on('repo:update', async (event) => {
  await swarm.propagate(event, {
    to: event.dependencies,
    strategy: 'eventual-consistency'
  });
 });
 ```
 ### 2. Event Streaming
 ```yaml
 # Kafka configuration for real-time coordination
 kafka:
  brokers: ['kafka1:9092', 'kafka2:9092']
  topics:
    swarm-events:
      partitions: 10
      replication: 3
    swarm-memory:
      partitions: 5
      replication: 3
 ```
 ## Synchronization Patterns
 ### 1. Eventually Consistent
 ```javascript
 {
  "sync": {
    "strategy": "eventual",
    "max-lag": "5m",
    "retry": {
      "attempts": 3,
      "backoff": "exponential"
    }
  }
 }
 ```
 ### 2. Strong Consistency
 ```javascript
 {
  "sync": {
    "strategy": "strong",
    "consensus": "raft",
    "quorum": 0.51,
    "timeout": "30s"
  }
 }
 ```
 ### 3. Hybrid Approach
 ```javascript
 {
  "sync": {
    "default": "eventual",
    "overrides": {
      "security-updates": "strong",
      "dependency-updates": "strong",
      "documentation": "eventual"
    }
  }
 }
 ```
 ## Use Cases
 ### 1. Microservices Coordination
 ```bash
 npx Codex-flow skill run github-multi-repo microservices \
  --services "auth,users,orders,payments" \
  --ensure-compatibility \
  --sync-contracts \
  --integration-tests
 ```
 ### 2. Library Updates
 ```bash
 npx Codex-flow skill run github-multi-repo lib-update \
  --library "org/shared-lib" \
  --version "2.0.0" \
  --find-consumers \
  --update-imports \
  --run-tests
 ```
 ### 3. Organization-Wide Changes
 ```bash
 npx Codex-flow skill run github-multi-repo org-policy \
  --policy "add-security-headers" \
  --repos "org/*" \
  --validate-compliance \
  --create-reports
 ```
 ## Architecture Patterns
 ### Monorepo Structure
 ```
 ruv-FANN/
 ├── packages/
 │   ├── Codex-flow/
 │   │   ├── src/
 │   │   ├── .Codex/
 │   │   └── package.json
 │   ├── ruv-swarm/
 │   │   ├── src/
 │   │   ├── wasm/
 │   │   └── package.json
 │   └── shared/
 │       ├── types/
 │       ├── utils/
 │       └── config/
 ├── tools/
 │   ├── build/
 │   ├── test/
 │   └── deploy/
 ├── docs/
 │   ├── architecture/
 │   ├── integration/
 │   └── examples/
 └── .github/
    ├── workflows/
    ├── templates/
    └── actions/
 ```
 ### Command Structure
 ```
 .Codex/
 ├── commands/
 │   ├── github/
 │   │   ├── github-modes.md
 │   │   ├── pr-manager.md
 │   │   ├── issue-tracker.md
 │   │   └── sync-coordinator.md
 │   ├── sparc/
 │   │   ├── sparc-modes.md
 │   │   ├── coder.md
 │   │   └── tester.md
 │   └── swarm/
 │       ├── coordination.md
 │       └── orchestration.md
 ├── templates/
 │   ├── issue.md
 │   ├── pr.md
 │   └── project.md
 └── config.json
 ```
 ## Monitoring & Visualization
 ### Multi-Repo Dashboard
 ```bash
 npx Codex-flow skill run github-multi-repo dashboard \
  --port 3000 \
  --metrics "agent-activity,task-progress,memory-usage" \
  --real-time
 ```
 ### Dependency Graph
 ```bash
 npx Codex-flow skill run github-multi-repo dep-graph \
  --format mermaid \
  --include-agents \
  --show-data-flow
 ```
 ### Health Monitoring
 ```bash
 npx Codex-flow skill run github-multi-repo health-check \
  --repos "org/*" \
  --check "connectivity,memory,agents" \
  --alert-on-issues
 ```
 ## Best Practices
 ### 1. Repository Organization
 - Clear repository roles and boundaries
 - Consistent naming conventions
 - Documented dependencies
 - Shared configuration standards
 ### 2. Communication
 - Use appropriate sync strategies
 - Implement circuit breakers
 - Monitor latency and failures
 - Clear error propagation
 ### 3. Security
 - Secure cross-repo authentication
 - Encrypted communication channels
 - Audit trail for all operations
 - Principle of least privilege
 ### 4. Version Management
 - Semantic versioning alignment
 - Dependency compatibility validation
 - Automated version bump coordination
 ### 5. Testing Integration
 - Cross-package test validation
 - Integration test automation
 - Performance regression detection
 ## Performance Optimization
 ### Caching Strategy
 ```bash
 npx Codex-flow skill run github-multi-repo cache-strategy \
  --analyze-patterns \
  --suggest-cache-layers \
  --implement-invalidation
 ```
 ### Parallel Execution
 ```bash
 npx Codex-flow skill run github-multi-repo parallel-optimize \
  --analyze-dependencies \
  --identify-parallelizable \
  --execute-optimal
 ```
 ### Resource Pooling
 ```bash
 npx Codex-flow skill run github-multi-repo resource-pool \
  --share-agents \
  --distribute-load \
  --monitor-usage
 ```
 ## Troubleshooting
 ### Connectivity Issues
 ```bash
 npx Codex-flow skill run github-multi-repo diagnose-connectivity \
  --test-all-repos \
  --check-permissions \
  --verify-webhooks
 ```
 ### Memory Synchronization
 ```bash
 npx Codex-flow skill run github-multi-repo debug-memory \
  --check-consistency \
  --identify-conflicts \
  --repair-state
 ```
 ### Performance Bottlenecks
 ```bash
 npx Codex-flow skill run github-multi-repo perf-analysis \
  --profile-operations \
  --identify-bottlenecks \
  --suggest-optimizations
 ```
 ## Advanced Features
 ### 1. Distributed Task Queue
 ```bash
 npx Codex-flow skill run github-multi-repo queue \
  --backend redis \
  --workers 10 \
  --priority-routing \
  --dead-letter-queue
 ```
 ### 2. Cross-Repo Testing
 ```bash
 npx Codex-flow skill run github-multi-repo test \
  --setup-test-env \
  --link-services \
  --run-e2e \
  --tear-down
 ```
 ### 3. Monorepo Migration
 ```bash
 npx Codex-flow skill run github-multi-repo to-monorepo \
  --analyze-repos \
  --suggest-structure \
  --preserve-history \
  --create-migration-prs
 ```
 ## Examples
 ### Full-Stack Application Update
 ```bash
 npx Codex-flow skill run github-multi-repo fullstack-update \
  --frontend "org/web-app" \
  --backend "org/api-server" \
  --database "org/db-migrations" \
  --coordinate-deployment
 ```
 ### Cross-Team Collaboration
 ```bash
 npx Codex-flow skill run github-multi-repo cross-team \
  --teams "frontend,backend,devops" \
  --task "implement-feature-x" \
  --assign-by-expertise \
  --track-progress
 ```
 ## Metrics and Reporting
 ### Sync Quality Metrics
 - Package version alignment percentage
 - Documentation consistency score
 - Integration test success rate
 - Synchronization completion time
 ### Architecture Health Metrics
 - Repository structure consistency score
 - Documentation coverage percentage
 - Cross-repository integration success rate
 - Template adoption and usage statistics
 ### Automated Reporting
 - Weekly sync status reports
 - Dependency drift detection
 - Documentation divergence alerts
 - Integration health monitoring
 ## Integration Points
 ### Related Skills
 - `github-workflow` - GitHub workflow automation
 - `github-pr` - Pull request management
 - `sparc-architect` - Architecture design
 - `sparc-optimizer` - Performance optimization
 ### Related Commands
 - `/github sync-coordinator` - Cross-repo synchronization
 - `/github release-manager` - Coordinated releases
 - `/github repo-architect` - Repository optimization
 - `/sparc architect` - Detailed architecture design
 ## Support and Resources
 - Documentation: https://github.com/ruvnet/Codex-flow
 - Issues: https://github.com/ruvnet/Codex-flow/issues
 - Examples: `.Codex/examples/github-multi-repo/`
 ---
 **Version:** 1.0.0
 **Last Updated:** 2025-10-19
 **Maintainer:** Codex Flow Team
@@ -1,126 +0,0 @@
 ---
 name: memory-management
 description: >
  AgentDB memory system with HNSW vector search. Provides 150x-12,500x faster pattern retrieval, persistent storage, and semantic search capabilities for learning and knowledge management.
  Use when: need to store successful patterns, searching for similar solutions, semantic lookup of past work, learning from previous tasks, sharing knowledge between agents, building knowledge base.
  Skip when: no learning needed, ephemeral one-off tasks, external data sources available, read-only exploration.
 ---
 # Memory Management Skill
 ## Purpose
 AgentDB memory system with HNSW vector search. Provides 150x-12,500x faster pattern retrieval, persistent storage, and semantic search capabilities for learning and knowledge management.
 ## When to Trigger
 - need to store successful patterns
 - searching for similar solutions
 - semantic lookup of past work
 - learning from previous tasks
 - sharing knowledge between agents
 - building knowledge base
 ## When to Skip
 - no learning needed
 - ephemeral one-off tasks
 - external data sources available
 - read-only exploration
 ## Commands
 ### Store Pattern
 Store a pattern or knowledge item in memory
 ```bash
 npx @claude-flow/cli memory store --key "[key]" --value "[value]" --namespace patterns
 ```
 **Example:**
 ```bash
 npx @claude-flow/cli memory store --key "auth-jwt-pattern" --value "JWT validation with refresh tokens" --namespace patterns
 ```
 ### Semantic Search
 Search memory using semantic similarity
 ```bash
 npx @claude-flow/cli memory search --query "[search terms]" --limit 10
 ```
 **Example:**
 ```bash
 npx @claude-flow/cli memory search --query "authentication best practices" --limit 5
 ```
 ### Retrieve Entry
 Retrieve a specific memory entry by key
 ```bash
 npx @claude-flow/cli memory get --key "[key]" --namespace [namespace]
 ```
 **Example:**
 ```bash
 npx @claude-flow/cli memory get --key "auth-jwt-pattern" --namespace patterns
 ```
 ### List Entries
 List all entries in a namespace
 ```bash
 npx @claude-flow/cli memory list --namespace [namespace]
 ```
 **Example:**
 ```bash
 npx @claude-flow/cli memory list --namespace patterns --limit 20
 ```
 ### Delete Entry
 Delete a memory entry
 ```bash
 npx @claude-flow/cli memory delete --key "[key]" --namespace [namespace]
 ```
 ### Initialize HNSW Index
 Initialize HNSW vector search index
 ```bash
 npx @claude-flow/cli memory init --enable-hnsw
 ```
 ### Memory Stats
 Show memory usage statistics
 ```bash
 npx @claude-flow/cli memory stats
 ```
 ### Export Memory
 Export memory to JSON
 ```bash
 npx @claude-flow/cli memory export --output memory-backup.json
 ```
 ## Scripts
 | Script | Path | Description |
 |--------|------|-------------|
 | `memory-backup` | `.agents/scripts/memory-backup.sh` | Backup memory to external storage |
 | `memory-consolidate` | `.agents/scripts/memory-consolidate.sh` | Consolidate and optimize memory |
 ## References
 | Document | Path | Description |
 |----------|------|-------------|
 | `HNSW Guide` | `docs/hnsw.md` | HNSW vector search configuration |
 | `Memory Schema` | `docs/memory-schema.md` | Memory namespace and schema reference |
 ## Best Practices
 1. Check memory for existing patterns before starting
 2. Use hierarchical topology for coordination
 3. Store successful patterns after completion
 4. Document any new learnings
@@ -1,446 +0,0 @@
 ---
 name: "ReasoningBank with AgentDB"
 description: "Implement ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning agents, optimizing decision-making, or implementing experience replay systems."
 ---
 # ReasoningBank with AgentDB
 ## What This Skill Does
 Provides ReasoningBank adaptive learning patterns using AgentDB's high-performance backend (150x-12,500x faster). Enables agents to learn from experiences, judge outcomes, distill memories, and improve decision-making over time with 100% backward compatibility.
 **Performance**: 150x faster pattern retrieval, 500x faster batch operations, <1ms memory access.
 ## Prerequisites
 - Node.js 18+
 - AgentDB v1.0.7+ (via agentic-flow)
 - Understanding of reinforcement learning concepts (optional)
 ---
 ## Quick Start with CLI
 ### Initialize ReasoningBank Database
 ```bash
 # Initialize AgentDB for ReasoningBank
 npx agentdb@latest init ./.agentdb/reasoningbank.db --dimension 1536
 # Start MCP server for Codex integration
 npx agentdb@latest mcp
 Codex mcp add agentdb npx agentdb@latest mcp
 ```
 ### Migrate from Legacy ReasoningBank
 ```bash
 # Automatic migration with validation
 npx agentdb@latest migrate --source .swarm/memory.db
 # Verify migration
 npx agentdb@latest stats ./.agentdb/reasoningbank.db
 ```
 ---
 ## Quick Start with API
 ```typescript
 import { createAgentDBAdapter, computeEmbedding } from 'agentic-flow/reasoningbank';
 // Initialize ReasoningBank with AgentDB
 const rb = await createAgentDBAdapter({
  dbPath: '.agentdb/reasoningbank.db',
  enableLearning: true,      // Enable learning plugins
  enableReasoning: true,      // Enable reasoning agents
  cacheSize: 1000,            // 1000 pattern cache
 });
 // Store successful experience
 const query = "How to optimize database queries?";
 const embedding = await computeEmbedding(query);
 await rb.insertPattern({
  id: '',
  type: 'experience',
  domain: 'database-optimization',
  pattern_data: JSON.stringify({
    embedding,
    pattern: {
      query,
      approach: 'indexing + query optimization',
      outcome: 'success',
      metrics: { latency_reduction: 0.85 }
    }
  }),
  confidence: 0.95,
  usage_count: 1,
  success_count: 1,
  created_at: Date.now(),
  last_used: Date.now(),
 });
 // Retrieve similar experiences with reasoning
 const result = await rb.retrieveWithReasoning(embedding, {
  domain: 'database-optimization',
  k: 5,
  useMMR: true,              // Diverse results
  synthesizeContext: true,    // Rich context synthesis
 });
 console.log('Memories:', result.memories);
 console.log('Context:', result.context);
 console.log('Patterns:', result.patterns);
 ```
 ---
 ## Core ReasoningBank Concepts
 ### 1. Trajectory Tracking
 Track agent execution paths and outcomes:
 ```typescript
 // Record trajectory (sequence of actions)
 const trajectory = {
  task: 'optimize-api-endpoint',
  steps: [
    { action: 'analyze-bottleneck', result: 'found N+1 query' },
    { action: 'add-eager-loading', result: 'reduced queries' },
    { action: 'add-caching', result: 'improved latency' }
  ],
  outcome: 'success',
  metrics: { latency_before: 2500, latency_after: 150 }
 };
 const embedding = await computeEmbedding(JSON.stringify(trajectory));
 await rb.insertPattern({
  id: '',
  type: 'trajectory',
  domain: 'api-optimization',
  pattern_data: JSON.stringify({ embedding, pattern: trajectory }),
  confidence: 0.9,
  usage_count: 1,
  success_count: 1,
  created_at: Date.now(),
  last_used: Date.now(),
 });
 ```
 ### 2. Verdict Judgment
 Judge whether a trajectory was successful:
 ```typescript
 // Retrieve similar past trajectories
 const similar = await rb.retrieveWithReasoning(queryEmbedding, {
  domain: 'api-optimization',
  k: 10,
 });
 // Judge based on similarity to successful patterns
 const verdict = similar.memories.filter(m =>
  m.pattern.outcome === 'success' &&
  m.similarity > 0.8
 ).length > 5 ? 'likely_success' : 'needs_review';
 console.log('Verdict:', verdict);
 console.log('Confidence:', similar.memories[0]?.similarity || 0);
 ```
 ### 3. Memory Distillation
 Consolidate similar experiences into patterns:
 ```typescript
 // Get all experiences in domain
 const experiences = await rb.retrieveWithReasoning(embedding, {
  domain: 'api-optimization',
  k: 100,
  optimizeMemory: true,  // Automatic consolidation
 });
 // Distill into high-level pattern
 const distilledPattern = {
  domain: 'api-optimization',
  pattern: 'For N+1 queries: add eager loading, then cache',
  success_rate: 0.92,
  sample_size: experiences.memories.length,
  confidence: 0.95
 };
 await rb.insertPattern({
  id: '',
  type: 'distilled-pattern',
  domain: 'api-optimization',
  pattern_data: JSON.stringify({
    embedding: await computeEmbedding(JSON.stringify(distilledPattern)),
    pattern: distilledPattern
  }),
  confidence: 0.95,
  usage_count: 0,
  success_count: 0,
  created_at: Date.now(),
  last_used: Date.now(),
 });
 ```
 ---
 ## Integration with Reasoning Agents
 AgentDB provides 4 reasoning modules that enhance ReasoningBank:
 ### 1. PatternMatcher
 Find similar successful patterns:
 ```typescript
 const result = await rb.retrieveWithReasoning(queryEmbedding, {
  domain: 'problem-solving',
  k: 10,
  useMMR: true,  // Maximal Marginal Relevance for diversity
 });
 // PatternMatcher returns diverse, relevant memories
 result.memories.forEach(mem => {
  console.log(`Pattern: ${mem.pattern.approach}`);
  console.log(`Similarity: ${mem.similarity}`);
  console.log(`Success Rate: ${mem.success_count / mem.usage_count}`);
 });
 ```
 ### 2. ContextSynthesizer
 Generate rich context from multiple memories:
 ```typescript
 const result = await rb.retrieveWithReasoning(queryEmbedding, {
  domain: 'code-optimization',
  synthesizeContext: true,  // Enable context synthesis
  k: 5,
 });
 // ContextSynthesizer creates coherent narrative
 console.log('Synthesized Context:', result.context);
 // "Based on 5 similar optimizations, the most effective approach
 //  involves profiling, identifying bottlenecks, and applying targeted
 //  improvements. Success rate: 87%"
 ```
 ### 3. MemoryOptimizer
 Automatically consolidate and prune:
 ```typescript
 const result = await rb.retrieveWithReasoning(queryEmbedding, {
  domain: 'testing',
  optimizeMemory: true,  // Enable automatic optimization
 });
 // MemoryOptimizer consolidates similar patterns and prunes low-quality
 console.log('Optimizations:', result.optimizations);
 // { consolidated: 15, pruned: 3, improved_quality: 0.12 }
 ```
 ### 4. ExperienceCurator
 Filter by quality and relevance:
 ```typescript
 const result = await rb.retrieveWithReasoning(queryEmbedding, {
  domain: 'debugging',
  k: 20,
  minConfidence: 0.8,  // Only high-confidence experiences
 });
 // ExperienceCurator returns only quality experiences
 result.memories.forEach(mem => {
  console.log(`Confidence: ${mem.confidence}`);
  console.log(`Success Rate: ${mem.success_count / mem.usage_count}`);
 });
 ```
 ---
 ## Legacy API Compatibility
 AgentDB maintains 100% backward compatibility with legacy ReasoningBank:
 ```typescript
 import {
  retrieveMemories,
  judgeTrajectory,
  distillMemories
 } from 'agentic-flow/reasoningbank';
 // Legacy API works unchanged (uses AgentDB backend automatically)
 const memories = await retrieveMemories(query, {
  domain: 'code-generation',
  agent: 'coder'
 });
 const verdict = await judgeTrajectory(trajectory, query);
 const newMemories = await distillMemories(
  trajectory,
  verdict,
  query,
  { domain: 'code-generation' }
 );
 ```
 ---
 ## Performance Characteristics
 - **Pattern Search**: 150x faster (100µs vs 15ms)
 - **Memory Retrieval**: <1ms (with cache)
 - **Batch Insert**: 500x faster (2ms vs 1s for 100 patterns)
 - **Trajectory Judgment**: <5ms (including retrieval + analysis)
 - **Memory Distillation**: <50ms (consolidate 100 patterns)
 ---
 ## Advanced Patterns
 ### Hierarchical Memory
 Organize memories by abstraction level:
 ```typescript
 // Low-level: Specific implementation
 await rb.insertPattern({
  type: 'concrete',
  domain: 'debugging/null-pointer',
  pattern_data: JSON.stringify({
    embedding,
    pattern: { bug: 'NPE in UserService.getUser()', fix: 'Add null check' }
  }),
  confidence: 0.9,
  // ...
 });
 // Mid-level: Pattern across similar cases
 await rb.insertPattern({
  type: 'pattern',
  domain: 'debugging',
  pattern_data: JSON.stringify({
    embedding,
    pattern: { category: 'null-pointer', approach: 'defensive-checks' }
  }),
  confidence: 0.85,
  // ...
 });
 // High-level: General principle
 await rb.insertPattern({
  type: 'principle',
  domain: 'software-engineering',
  pattern_data: JSON.stringify({
    embedding,
    pattern: { principle: 'fail-fast with clear errors' }
  }),
  confidence: 0.95,
  // ...
 });
 ```
 ### Multi-Domain Learning
 Transfer learning across domains:
 ```typescript
 // Learn from backend optimization
 const backendExperience = await rb.retrieveWithReasoning(embedding, {
  domain: 'backend-optimization',
  k: 10,
 });
 // Apply to frontend optimization
 const transferredKnowledge = backendExperience.memories.map(mem => ({
  ...mem,
  domain: 'frontend-optimization',
  adapted: true,
 }));
 ```
 ---
 ## CLI Operations
 ### Database Management
 ```bash
 # Export trajectories and patterns
 npx agentdb@latest export ./.agentdb/reasoningbank.db ./backup.json
 # Import experiences
 npx agentdb@latest import ./experiences.json
 # Get statistics
 npx agentdb@latest stats ./.agentdb/reasoningbank.db
 # Shows: total patterns, domains, confidence distribution
 ```
 ### Migration
 ```bash
 # Migrate from legacy ReasoningBank
 npx agentdb@latest migrate --source .swarm/memory.db --target .agentdb/reasoningbank.db
 # Validate migration
 npx agentdb@latest stats .agentdb/reasoningbank.db
 ```
 ---
 ## Troubleshooting
 ### Issue: Migration fails
 ```bash
 # Check source database exists
 ls -la .swarm/memory.db
 # Run with verbose logging
 DEBUG=agentdb:* npx agentdb@latest migrate --source .swarm/memory.db
 ```
 ### Issue: Low confidence scores
 ```typescript
 // Enable context synthesis for better quality
 const result = await rb.retrieveWithReasoning(embedding, {
  synthesizeContext: true,
  useMMR: true,
  k: 10,
 });
 ```
 ### Issue: Memory growing too large
 ```typescript
 // Enable automatic optimization
 const result = await rb.retrieveWithReasoning(embedding, {
  optimizeMemory: true,  // Consolidates similar patterns
 });
 // Or manually optimize
 await rb.optimize();
 ```
 ---
 ## Learn More
 - **AgentDB Integration**: node_modules/agentic-flow/docs/AGENTDB_INTEGRATION.md
 - **GitHub**: https://github.com/ruvnet/agentic-flow/tree/main/packages/agentdb
 - **MCP Integration**: `npx agentdb@latest mcp`
 - **Website**: https://agentdb.ruv.io
 ---
 **Category**: Machine Learning / Reinforcement Learning
 **Difficulty**: Intermediate
 **Estimated Time**: 20-30 minutes
@@ -1,201 +0,0 @@
 ---
 name: "ReasoningBank Intelligence"
 description: "Implement adaptive learning with ReasoningBank for pattern recognition, strategy optimization, and continuous improvement. Use when building self-learning agents, optimizing workflows, or implementing meta-cognitive systems."
 ---
 # ReasoningBank Intelligence
 ## What This Skill Does
 Implements ReasoningBank's adaptive learning system for AI agents to learn from experience, recognize patterns, and optimize strategies over time. Enables meta-cognitive capabilities and continuous improvement.
 ## Prerequisites
 - agentic-flow v3.0.0-alpha.1+
 - AgentDB v3.0.0-alpha.10+ (for persistence)
 - Node.js 18+
 ## Quick Start
 ```typescript
 import { ReasoningBank } from 'agentic-flow/reasoningbank';
 // Initialize ReasoningBank
 const rb = new ReasoningBank({
  persist: true,
  learningRate: 0.1,
  adapter: 'agentdb' // Use AgentDB for storage
 });
 // Record task outcome
 await rb.recordExperience({
  task: 'code_review',
  approach: 'static_analysis_first',
  outcome: {
    success: true,
    metrics: {
      bugs_found: 5,
      time_taken: 120,
      false_positives: 1
    }
  },
  context: {
    language: 'typescript',
    complexity: 'medium'
  }
 });
 // Get optimal strategy
 const strategy = await rb.recommendStrategy('code_review', {
  language: 'typescript',
  complexity: 'high'
 });
 ```
 ## Core Features
 ### 1. Pattern Recognition
 ```typescript
 // Learn patterns from data
 await rb.learnPattern({
  pattern: 'api_errors_increase_after_deploy',
  triggers: ['deployment', 'traffic_spike'],
  actions: ['rollback', 'scale_up'],
  confidence: 0.85
 });
 // Match patterns
 const matches = await rb.matchPatterns(currentSituation);
 ```
 ### 2. Strategy Optimization
 ```typescript
 // Compare strategies
 const comparison = await rb.compareStrategies('bug_fixing', [
  'tdd_approach',
  'debug_first',
  'reproduce_then_fix'
 ]);
 // Get best strategy
 const best = comparison.strategies[0];
 console.log(`Best: ${best.name} (score: ${best.score})`);
 ```
 ### 3. Continuous Learning
 ```typescript
 // Enable auto-learning from all tasks
 await rb.enableAutoLearning({
  threshold: 0.7,        // Only learn from high-confidence outcomes
  updateFrequency: 100   // Update models every 100 experiences
 });
 ```
 ## Advanced Usage
 ### Meta-Learning
 ```typescript
 // Learn about learning
 await rb.metaLearn({
  observation: 'parallel_execution_faster_for_independent_tasks',
  confidence: 0.95,
  applicability: {
    task_types: ['batch_processing', 'data_transformation'],
    conditions: ['tasks_independent', 'io_bound']
  }
 });
 ```
 ### Transfer Learning
 ```typescript
 // Apply knowledge from one domain to another
 await rb.transferKnowledge({
  from: 'code_review_javascript',
  to: 'code_review_typescript',
  similarity: 0.8
 });
 ```
 ### Adaptive Agents
 ```typescript
 // Create self-improving agent
 class AdaptiveAgent {
  async execute(task: Task) {
    // Get optimal strategy
    const strategy = await rb.recommendStrategy(task.type, task.context);
    // Execute with strategy
    const result = await this.executeWithStrategy(task, strategy);
    // Learn from outcome
    await rb.recordExperience({
      task: task.type,
      approach: strategy.name,
      outcome: result,
      context: task.context
    });
    return result;
  }
 }
 ```
 ## Integration with AgentDB
 ```typescript
 // Persist ReasoningBank data
 await rb.configure({
  storage: {
    type: 'agentdb',
    options: {
      database: './reasoning-bank.db',
      enableVectorSearch: true
    }
  }
 });
 // Query learned patterns
 const patterns = await rb.query({
  category: 'optimization',
  minConfidence: 0.8,
  timeRange: { last: '30d' }
 });
 ```
 ## Performance Metrics
 ```typescript
 // Track learning effectiveness
 const metrics = await rb.getMetrics();
 console.log(`
  Total Experiences: ${metrics.totalExperiences}
  Patterns Learned: ${metrics.patternsLearned}
  Strategy Success Rate: ${metrics.strategySuccessRate}
  Improvement Over Time: ${metrics.improvement}
 `);
 ```
 ## Best Practices
 1. **Record consistently**: Log all task outcomes, not just successes
 2. **Provide context**: Rich context improves pattern matching
 3. **Set thresholds**: Filter low-confidence learnings
 4. **Review periodically**: Audit learned patterns for quality
 5. **Use vector search**: Enable semantic pattern matching
 ## Troubleshooting
 ### Issue: Poor recommendations
 **Solution**: Ensure sufficient training data (100+ experiences per task type)
 ### Issue: Slow pattern matching
 **Solution**: Enable vector indexing in AgentDB
 ### Issue: Memory growing large
 **Solution**: Set TTL for old experiences or enable pruning
 ## Learn More
 - ReasoningBank Guide: agentic-flow/src/reasoningbank/README.md
 - AgentDB Integration: packages/agentdb/docs/reasoningbank.md
 - Pattern Learning: docs/reasoning/patterns.md
@@ -1,135 +0,0 @@
 ---
 name: security-audit
 description: >
  Comprehensive security scanning and vulnerability detection. Includes input validation, path traversal prevention, CVE detection, and secure coding pattern enforcement.
  Use when: authentication implementation, authorization logic, payment processing, user data handling, API endpoint creation, file upload handling, database queries, external API integration.
  Skip when: read-only operations on public data, internal development tooling, static documentation, styling changes.
 ---
 # Security Audit Skill
 ## Purpose
 Comprehensive security scanning and vulnerability detection. Includes input validation, path traversal prevention, CVE detection, and secure coding pattern enforcement.
 ## When to Trigger
 - authentication implementation
 - authorization logic
 - payment processing
 - user data handling
 - API endpoint creation
 - file upload handling
 - database queries
 - external API integration
 ## When to Skip
 - read-only operations on public data
 - internal development tooling
 - static documentation
 - styling changes
 ## Commands
 ### Full Security Scan
 Run comprehensive security analysis on the codebase
 ```bash
 npx @claude-flow/cli security scan --depth full
 ```
 **Example:**
 ```bash
 npx @claude-flow/cli security scan --depth full --output security-report.json
 ```
 ### Input Validation Check
 Check for input validation issues
 ```bash
 npx @claude-flow/cli security scan --check input-validation
 ```
 **Example:**
 ```bash
 npx @claude-flow/cli security scan --check input-validation --path ./src/api
 ```
 ### Path Traversal Check
 Check for path traversal vulnerabilities
 ```bash
 npx @claude-flow/cli security scan --check path-traversal
 ```
 ### SQL Injection Check
 Check for SQL injection vulnerabilities
 ```bash
 npx @claude-flow/cli security scan --check sql-injection
 ```
 ### XSS Check
 Check for cross-site scripting vulnerabilities
 ```bash
 npx @claude-flow/cli security scan --check xss
 ```
 ### CVE Scan
 Scan dependencies for known CVEs
 ```bash
 npx @claude-flow/cli security cve --scan
 ```
 **Example:**
 ```bash
 npx @claude-flow/cli security cve --scan --severity high
 ```
 ### Security Audit Report
 Generate full security audit report
 ```bash
 npx @claude-flow/cli security audit --report
 ```
 **Example:**
 ```bash
 npx @claude-flow/cli security audit --report --format markdown --output SECURITY.md
 ```
 ### Threat Modeling
 Run threat modeling analysis
 ```bash
 npx @claude-flow/cli security threats --analyze
 ```
 ### Validate Secrets
 Check for hardcoded secrets
 ```bash
 npx @claude-flow/cli security validate --check secrets
 ```
 ## Scripts
 | Script | Path | Description |
 |--------|------|-------------|
 | `security-scan` | `.agents/scripts/security-scan.sh` | Run full security scan pipeline |
 | `cve-remediate` | `.agents/scripts/cve-remediate.sh` | Auto-remediate known CVEs |
 ## References
 | Document | Path | Description |
 |----------|------|-------------|
 | `Security Checklist` | `docs/security-checklist.md` | Security review checklist |
 | `OWASP Guide` | `docs/owasp-top10.md` | OWASP Top 10 mitigation guide |
 ## Best Practices
 1. Check memory for existing patterns before starting
 2. Use hierarchical topology for coordination
 3. Store successful patterns after completion
 4. Document any new learnings
@@ -1,910 +0,0 @@
 ---
 name: "Skill Builder"
 description: "Create new Codex Skills with proper YAML frontmatter, progressive disclosure structure, and complete directory organization. Use when you need to build custom skills for specific workflows, generate skill templates, or understand the Codex Skills specification."
 ---
 # Skill Builder
 ## What This Skill Does
 Creates production-ready Codex Skills with proper YAML frontmatter, progressive disclosure architecture, and complete file/folder structure. This skill guides you through building skills that Codex can autonomously discover and use across all surfaces (Codex.ai, Codex, SDK, API).
 ## Prerequisites
 - Codex 2.0+ or Codex.ai with Skills support
 - Basic understanding of Markdown and YAML
 - Text editor or IDE
 ## Quick Start
 ### Creating Your First Skill
 ```bash
 # 1. Create skill directory (MUST be at top level, NOT in subdirectories!)
 mkdir -p ~/.Codex/skills/my-first-skill
 # 2. Create SKILL.md with proper format
 cat > ~/.Codex/skills/my-first-skill/SKILL.md << 'EOF'
 ---
 name: "My First Skill"
 description: "Brief description of what this skill does and when Codex should use it. Maximum 1024 characters."
 ---
 # My First Skill
 ## What This Skill Does
 [Your instructions here]
 ## Quick Start
 [Basic usage]
 EOF
 # 3. Verify skill is detected
 # Restart Codex or refresh Codex.ai
 ```
 ---
 ## Complete Specification
 ### 📋 YAML Frontmatter (REQUIRED)
 Every SKILL.md **must** start with YAML frontmatter containing exactly two required fields:
 ```yaml
 ---
 name: "Skill Name"                    # REQUIRED: Max 64 chars
 description: "What this skill does    # REQUIRED: Max 1024 chars
 and when Codex should use it."       # Include BOTH what & when
 ---
 ```
 #### Field Requirements
 **`name`** (REQUIRED):
 - **Type**: String
 - **Max Length**: 64 characters
 - **Format**: Human-friendly display name
 - **Usage**: Shown in skill lists, UI, and loaded into Codex's system prompt
 - **Best Practice**: Use Title Case, be concise and descriptive
 - **Examples**:
  - ✅ "API Documentation Generator"
  - ✅ "React Component Builder"
  - ✅ "Database Schema Designer"
  - ❌ "skill-1" (not descriptive)
  - ❌ "This is a very long skill name that exceeds sixty-four characters" (too long)
 **`description`** (REQUIRED):
 - **Type**: String
 - **Max Length**: 1024 characters
 - **Format**: Plain text or minimal markdown
 - **Content**: MUST include:
  1. **What** the skill does (functionality)
  2. **When** Codex should invoke it (trigger conditions)
 - **Usage**: Loaded into Codex's system prompt for autonomous matching
 - **Best Practice**: Front-load key trigger words, be specific about use cases
 - **Examples**:
  - ✅ "Generate OpenAPI 3.0 documentation from Express.js routes. Use when creating API docs, documenting endpoints, or building API specifications."
  - ✅ "Create React functional components with TypeScript, hooks, and tests. Use when scaffolding new components or converting class components."
  - ❌ "A comprehensive guide to API documentation" (no "when" clause)
  - ❌ "Documentation tool" (too vague)
 #### YAML Formatting Rules
 ```yaml
 ---
 # ✅ CORRECT: Simple string
 name: "API Builder"
 description: "Creates REST APIs with Express and TypeScript."
 # ✅ CORRECT: Multi-line description
 name: "Full-Stack Generator"
 description: "Generates full-stack applications with React frontend and Node.js backend. Use when starting new projects or scaffolding applications."
 # ✅ CORRECT: Special characters quoted
 name: "JSON:API Builder"
 description: "Creates JSON:API compliant endpoints: pagination, filtering, relationships."
 # ❌ WRONG: Missing quotes with special chars
 name: API:Builder  # YAML parse error!
 # ❌ WRONG: Extra fields (ignored but discouraged)
 name: "My Skill"
 description: "My description"
 version: "1.0.0"       # NOT part of spec
 author: "Me"           # NOT part of spec
 tags: ["dev", "api"]   # NOT part of spec
 ---
 ```
 **Critical**: Only `name` and `description` are used by Codex. Additional fields are ignored.
 ---
 ### 📂 Directory Structure
 #### Minimal Skill (Required)
 ```
 ~/.Codex/skills/                    # Personal skills location
 └── my-skill/                        # Skill directory (MUST be at top level!)
    └── SKILL.md                     # REQUIRED: Main skill file
 ```
 **IMPORTANT**: Skills MUST be directly under `~/.Codex/skills/[skill-name]/`.
 Codex does NOT support nested subdirectories or namespaces!
 #### Full-Featured Skill (Recommended)
 ```
 ~/.Codex/skills/
 └── my-skill/                        # Top-level skill directory
        ├── SKILL.md                 # REQUIRED: Main skill file
        ├── README.md                # Optional: Human-readable docs
        ├── scripts/                 # Optional: Executable scripts
        │   ├── setup.sh
        │   ├── validate.js
        │   └── deploy.py
        ├── resources/               # Optional: Supporting files
        │   ├── templates/
        │   │   ├── api-template.js
        │   │   └── component.tsx
        │   ├── examples/
        │   │   └── sample-output.json
        │   └── schemas/
        │       └── config-schema.json
        └── docs/                    # Optional: Additional documentation
            ├── ADVANCED.md
            ├── TROUBLESHOOTING.md
            └── API_REFERENCE.md
 ```
 #### Skills Locations
 **Personal Skills** (available across all projects):
 ```
 ~/.Codex/skills/
 └── [your-skills]/
 ```
 - **Path**: `~/.Codex/skills/` or `$HOME/.Codex/skills/`
 - **Scope**: Available in all projects for this user
 - **Version Control**: NOT committed to git (outside repo)
 - **Use Case**: Personal productivity tools, custom workflows
 **Project Skills** (team-shared, version controlled):
 ```
 <project-root>/.Codex/skills/
 └── [team-skills]/
 ```
 - **Path**: `.Codex/skills/` in project root
 - **Scope**: Available only in this project
 - **Version Control**: SHOULD be committed to git
 - **Use Case**: Team workflows, project-specific tools, shared knowledge
 ---
 ### 🎯 Progressive Disclosure Architecture
 Codex uses a **3-level progressive disclosure system** to scale to 100+ skills without context penalty:
 #### Level 1: Metadata (Name + Description)
 **Loaded**: At Codex startup, always
 **Size**: ~200 chars per skill
 **Purpose**: Enable autonomous skill matching
 **Context**: Loaded into system prompt for ALL skills
 ```yaml
 ---
 name: "API Builder"                   # 11 chars
 description: "Creates REST APIs..."   # ~50 chars
 ---
 # Total: ~61 chars per skill
 # 100 skills = ~6KB context (minimal!)
 ```
 #### Level 2: SKILL.md Body
 **Loaded**: When skill is triggered/matched
 **Size**: ~1-10KB typically
 **Purpose**: Main instructions and procedures
 **Context**: Only loaded for ACTIVE skills
 ```markdown
 # API Builder
 ## What This Skill Does
 [Main instructions - loaded only when skill is active]
 ## Quick Start
 [Basic procedures]
 ## Step-by-Step Guide
 [Detailed instructions]
 ```
 #### Level 3+: Referenced Files
 **Loaded**: On-demand as Codex navigates
 **Size**: Variable (KB to MB)
 **Purpose**: Deep reference, examples, schemas
 **Context**: Loaded only when Codex accesses specific files
 ```markdown
 # In SKILL.md
 See [Advanced Configuration](docs/ADVANCED.md) for complex scenarios.
 See [API Reference](docs/API_REFERENCE.md) for complete documentation.
 Use template: `resources/templates/api-template.js`
 # Codex will load these files ONLY if needed
 ```
 **Benefit**: Install 100+ skills with ~6KB context. Only active skill content (1-10KB) enters context.
 ---
 ### 📝 SKILL.md Content Structure
 #### Recommended 4-Level Structure
 ```markdown
 ---
 name: "Your Skill Name"
 description: "What it does and when to use it"
 ---
 # Your Skill Name
 ## Level 1: Overview (Always Read First)
 Brief 2-3 sentence description of the skill.
 ## Prerequisites
 - Requirement 1
 - Requirement 2
 ## What This Skill Does
 1. Primary function
 2. Secondary function
 3. Key benefit
 ---
 ## Level 2: Quick Start (For Fast Onboarding)
 ### Basic Usage
 ```bash
 # Simplest use case
 command --option value
 ```
 ### Common Scenarios
 1. **Scenario 1**: How to...
 2. **Scenario 2**: How to...
 ---
 ## Level 3: Detailed Instructions (For Deep Work)
 ### Step-by-Step Guide
 #### Step 1: Initial Setup
 ```bash
 # Commands
 ```
 Expected output:
 ```
 Success message
 ```
 #### Step 2: Configuration
 - Configuration option 1
 - Configuration option 2
 #### Step 3: Execution
 - Run the main command
 - Verify results
 ### Advanced Options
 #### Option 1: Custom Configuration
 ```bash
 # Advanced usage
 ```
 #### Option 2: Integration
 ```bash
 # Integration steps
 ```
 ---
 ## Level 4: Reference (Rarely Needed)
 ### Troubleshooting
 #### Issue: Common Problem
 **Symptoms**: What you see
 **Cause**: Why it happens
 **Solution**: How to fix
 ```bash
 # Fix command
 ```
 #### Issue: Another Problem
 **Solution**: Steps to resolve
 ### Complete API Reference
 See [API_REFERENCE.md](docs/API_REFERENCE.md)
 ### Examples
 See [examples/](resources/examples/)
 ### Related Skills
 - [Related Skill 1](#)
 - [Related Skill 2](#)
 ### Resources
 - [External Link 1](https://example.com)
 - [Documentation](https://docs.example.com)
 ```
 ---
 ### 🎨 Content Best Practices
 #### Writing Effective Descriptions
 **Front-Load Keywords**:
 ```yaml
 # ✅ GOOD: Keywords first
 description: "Generate TypeScript interfaces from JSON schema. Use when converting schemas, creating types, or building API clients."
 # ❌ BAD: Keywords buried
 description: "This skill helps developers who need to work with JSON schemas by providing a way to generate TypeScript interfaces."
 ```
 **Include Trigger Conditions**:
 ```yaml
 # ✅ GOOD: Clear "when" clause
 description: "Debug React performance issues using Chrome DevTools. Use when components re-render unnecessarily, investigating slow updates, or optimizing bundle size."
 # ❌ BAD: No trigger conditions
 description: "Helps with React performance debugging."
 ```
 **Be Specific**:
 ```yaml
 # ✅ GOOD: Specific technologies
 description: "Create Express.js REST endpoints with Joi validation, Swagger docs, and Jest tests. Use when building new APIs or adding endpoints."
 # ❌ BAD: Too generic
 description: "Build API endpoints with proper validation and testing."
 ```
 #### Progressive Disclosure Writing
 **Keep Level 1 Brief** (Overview):
 ```markdown
 ## What This Skill Does
 Creates production-ready React components with TypeScript, hooks, and tests in 3 steps.
 ```
 **Level 2 for Common Paths** (Quick Start):
 ```markdown
 ## Quick Start
 ```bash
 # Most common use case (80% of users)
 generate-component MyComponent
 ```
 ```
 **Level 3 for Details** (Step-by-Step):
 ```markdown
 ## Step-by-Step Guide
 ### Creating a Basic Component
 1. Run generator
 2. Choose template
 3. Customize options
 [Detailed explanations]
 ```
 **Level 4 for Edge Cases** (Reference):
 ```markdown
 ## Advanced Configuration
 For complex scenarios like HOCs, render props, or custom hooks, see [ADVANCED.md](docs/ADVANCED.md).
 ```
 ---
 ### 🛠️ Adding Scripts and Resources
 #### Scripts Directory
 **Purpose**: Executable scripts that Codex can run
 **Location**: `scripts/` in skill directory
 **Usage**: Referenced from SKILL.md
 Example:
 ```bash
 # In skill directory
 scripts/
 ├── setup.sh          # Initialization script
 ├── validate.js       # Validation logic
 ├── generate.py       # Code generation
 └── deploy.sh         # Deployment script
 ```
 Reference from SKILL.md:
 ```markdown
 ## Setup
 Run the setup script:
 ```bash
 ./scripts/setup.sh
 ```
 ## Validation
 Validate your configuration:
 ```bash
 node scripts/validate.js config.json
 ```
 ```
 #### Resources Directory
 **Purpose**: Templates, examples, schemas, static files
 **Location**: `resources/` in skill directory
 **Usage**: Referenced or copied by scripts
 Example:
 ```bash
 resources/
 ├── templates/
 │   ├── component.tsx.template
 │   ├── test.spec.ts.template
 │   └── story.stories.tsx.template
 ├── examples/
 │   ├── basic-example/
 │   ├── advanced-example/
 │   └── integration-example/
 └── schemas/
    ├── config.schema.json
    └── output.schema.json
 ```
 Reference from SKILL.md:
 ```markdown
 ## Templates
 Use the component template:
 ```bash
 cp resources/templates/component.tsx.template src/components/MyComponent.tsx
 ```
 ## Examples
 See working examples in `resources/examples/`:
 - `basic-example/` - Simple component
 - `advanced-example/` - With hooks and context
 ```
 ---
 ### 🔗 File References and Navigation
 Codex can navigate to referenced files automatically. Use these patterns:
 #### Markdown Links
 ```markdown
 See [Advanced Configuration](docs/ADVANCED.md) for complex scenarios.
 See [Troubleshooting Guide](docs/TROUBLESHOOTING.md) if you encounter errors.
 ```
 #### Relative File Paths
 ```markdown
 Use the template located at `resources/templates/api-template.js`
 See examples in `resources/examples/basic-usage/`
 ```
 #### Inline File Content
 ```markdown
 ## Example Configuration
 See `resources/examples/config.json`:
 ```json
 {
  "option": "value"
 }
 ```
 ```
 **Best Practice**: Keep SKILL.md lean (~2-5KB). Move lengthy content to separate files and reference them. Codex will load only what's needed.
 ---
 ### ✅ Validation Checklist
 Before publishing a skill, verify:
 **YAML Frontmatter**:
 - [ ] Starts with `---`
 - [ ] Contains `name` field (max 64 chars)
 - [ ] Contains `description` field (max 1024 chars)
 - [ ] Description includes "what" and "when"
 - [ ] Ends with `---`
 - [ ] No YAML syntax errors
 **File Structure**:
 - [ ] SKILL.md exists in skill directory
 - [ ] Directory is DIRECTLY in `~/.Codex/skills/[skill-name]/` or `.Codex/skills/[skill-name]/`
 - [ ] Uses clear, descriptive directory name
 - [ ] **NO nested subdirectories** (Codex requires top-level structure)
 **Content Quality**:
 - [ ] Level 1 (Overview) is brief and clear
 - [ ] Level 2 (Quick Start) shows common use case
 - [ ] Level 3 (Details) provides step-by-step guide
 - [ ] Level 4 (Reference) links to advanced content
 - [ ] Examples are concrete and runnable
 - [ ] Troubleshooting section addresses common issues
 **Progressive Disclosure**:
 - [ ] Core instructions in SKILL.md (~2-5KB)
 - [ ] Advanced content in separate docs/
 - [ ] Large resources in resources/ directory
 - [ ] Clear navigation between levels
 **Testing**:
 - [ ] Skill appears in Codex's skill list
 - [ ] Description triggers on relevant queries
 - [ ] Instructions are clear and actionable
 - [ ] Scripts execute successfully (if included)
 - [ ] Examples work as documented
 ---
 ## Skill Builder Templates
 ### Template 1: Basic Skill (Minimal)
 ```markdown
 ---
 name: "My Basic Skill"
 description: "One sentence what. One sentence when to use."
 ---
 # My Basic Skill
 ## What This Skill Does
 [2-3 sentences describing functionality]
 ## Quick Start
 ```bash
 # Single command to get started
 ```
 ## Step-by-Step Guide
 ### Step 1: Setup
 [Instructions]
 ### Step 2: Usage
 [Instructions]
 ### Step 3: Verify
 [Instructions]
 ## Troubleshooting
 - **Issue**: Problem description
  - **Solution**: Fix description
 ```
 ### Template 2: Intermediate Skill (With Scripts)
 ```markdown
 ---
 name: "My Intermediate Skill"
 description: "Detailed what with key features. When to use with specific triggers: scaffolding, generating, building."
 ---
 # My Intermediate Skill
 ## Prerequisites
 - Requirement 1
 - Requirement 2
 ## What This Skill Does
 1. Primary function
 2. Secondary function
 3. Integration capability
 ## Quick Start
 ```bash
 ./scripts/setup.sh
 ./scripts/generate.sh my-project
 ```
 ## Configuration
 Edit `config.json`:
 ```json
 {
  "option1": "value1",
  "option2": "value2"
 }
 ```
 ## Step-by-Step Guide
 ### Basic Usage
 [Steps for 80% use case]
 ### Advanced Usage
 [Steps for complex scenarios]
 ## Available Scripts
 - `scripts/setup.sh` - Initial setup
 - `scripts/generate.sh` - Code generation
 - `scripts/validate.sh` - Validation
 ## Resources
 - Templates: `resources/templates/`
 - Examples: `resources/examples/`
 ## Troubleshooting
 [Common issues and solutions]
 ```
 ### Template 3: Advanced Skill (Full-Featured)
 ```markdown
 ---
 name: "My Advanced Skill"
 description: "Comprehensive what with all features and integrations. Use when [trigger 1], [trigger 2], or [trigger 3]. Supports [technology stack]."
 ---
 # My Advanced Skill
 ## Overview
 [Brief 2-3 sentence description]
 ## Prerequisites
 - Technology 1 (version X+)
 - Technology 2 (version Y+)
 - API keys or credentials
 ## What This Skill Does
 1. **Core Feature**: Description
 2. **Integration**: Description
 3. **Automation**: Description
 ---
 ## Quick Start (60 seconds)
 ### Installation
 ```bash
 ./scripts/install.sh
 ```
 ### First Use
 ```bash
 ./scripts/quickstart.sh
 ```
 Expected output:
 ```
 ✓ Setup complete
 ✓ Configuration validated
 → Ready to use
 ```
 ---
 ## Configuration
 ### Basic Configuration
 Edit `config.json`:
 ```json
 {
  "mode": "production",
  "features": ["feature1", "feature2"]
 }
 ```
 ### Advanced Configuration
 See [Configuration Guide](docs/CONFIGURATION.md)
 ---
 ## Step-by-Step Guide
 ### 1. Initial Setup
 [Detailed steps]
 ### 2. Core Workflow
 [Main procedures]
 ### 3. Integration
 [Integration steps]
 ---
 ## Advanced Features
 ### Feature 1: Custom Templates
 ```bash
 ./scripts/generate.sh --template custom
 ```
 ### Feature 2: Batch Processing
 ```bash
 ./scripts/batch.sh --input data.json
 ```
 ### Feature 3: CI/CD Integration
 See [CI/CD Guide](docs/CICD.md)
 ---
 ## Scripts Reference
 | Script | Purpose | Usage |
 |--------|---------|-------|
 | `install.sh` | Install dependencies | `./scripts/install.sh` |
 | `generate.sh` | Generate code | `./scripts/generate.sh [name]` |
 | `validate.sh` | Validate output | `./scripts/validate.sh` |
 | `deploy.sh` | Deploy to environment | `./scripts/deploy.sh [env]` |
 ---
 ## Resources
 ### Templates
 - `resources/templates/basic.template` - Basic template
 - `resources/templates/advanced.template` - Advanced template
 ### Examples
 - `resources/examples/basic/` - Simple example
 - `resources/examples/advanced/` - Complex example
 - `resources/examples/integration/` - Integration example
 ### Schemas
 - `resources/schemas/config.schema.json` - Configuration schema
 - `resources/schemas/output.schema.json` - Output validation
 ---
 ## Troubleshooting
 ### Issue: Installation Failed
 **Symptoms**: Error during `install.sh`
 **Cause**: Missing dependencies
 **Solution**:
 ```bash
 # Install prerequisites
 npm install -g required-package
 ./scripts/install.sh --force
 ```
 ### Issue: Validation Errors
 **Symptoms**: Validation script fails
 **Solution**: See [Troubleshooting Guide](docs/TROUBLESHOOTING.md)
 ---
 ## API Reference
 Complete API documentation: [API_REFERENCE.md](docs/API_REFERENCE.md)
 ## Related Skills
 - [Related Skill 1](../related-skill-1/)
 - [Related Skill 2](../related-skill-2/)
 ## Resources
 - [Official Documentation](https://example.com/docs)
 - [GitHub Repository](https://github.com/example/repo)
 - [Community Forum](https://forum.example.com)
 ---
 **Created**: 2025-10-19
 **Category**: Advanced
 **Difficulty**: Intermediate
 **Estimated Time**: 15-30 minutes
 ```
 ---
 ## Examples from the Wild
 ### Example 1: Simple Documentation Skill
 ```markdown
 ---
 name: "README Generator"
 description: "Generate comprehensive README.md files for GitHub repositories. Use when starting new projects, documenting code, or improving existing READMEs."
 ---
 # README Generator
 ## What This Skill Does
 Creates well-structured README.md files with badges, installation, usage, and contribution sections.
 ## Quick Start
 ```bash
 # Answer a few questions
 ./scripts/generate-readme.sh
 # README.md created with:
 # - Project title and description
 # - Installation instructions
 # - Usage examples
 # - Contribution guidelines
 ```
 ## Customization
 Edit sections in `resources/templates/sections/` before generating.
 ```
 ### Example 2: Code Generation Skill
 ```markdown
 ---
 name: "React Component Generator"
 description: "Generate React functional components with TypeScript, hooks, tests, and Storybook stories. Use when creating new components, scaffolding UI, or following component architecture patterns."
 ---
 # React Component Generator
 ## Prerequisites
 - Node.js 18+
 - React 18+
 - TypeScript 5+
 ## Quick Start
 ```bash
 ./scripts/generate-component.sh MyComponent
 # Creates:
 # - src/components/MyComponent/MyComponent.tsx
 # - src/components/MyComponent/MyComponent.test.tsx
 # - src/components/MyComponent/MyComponent.stories.tsx
 # - src/components/MyComponent/index.ts
 ```
 ## Step-by-Step Guide
 ### 1. Run Generator
 ```bash
 ./scripts/generate-component.sh ComponentName
 ```
 ### 2. Choose Template
 - Basic: Simple functional component
 - With State: useState hooks
 - With Context: useContext integration
 - With API: Data fetching component
 ### 3. Customize
 Edit generated files in `src/components/ComponentName/`
 ## Templates
 See `resources/templates/` for available component templates.
 ```
 ---
 ## Learn More
 ### Official Resources
 - [Anthropic Agent Skills Documentation](https://docs.Codex.com/en/docs/agents-and-tools/agent-skills)
 - [GitHub Skills Repository](https://github.com/anthropics/skills)
 - [Codex Documentation](https://docs.Codex.com/en/docs/Codex)
 ### Community
 - [Skills Marketplace](https://github.com/anthropics/skills) - Browse community skills
 - [Anthropic Discord](https://discord.gg/anthropic) - Get help from community
 ### Advanced Topics
 - Multi-file skills with complex navigation
 - Skills that spawn other skills
 - Integration with MCP tools
 - Dynamic skill generation
 ---
 **Created**: 2025-10-19
 **Version**: 1.0.0
 **Maintained By**: agentic-flow team
 **License**: MIT
@@ -1,144 +0,0 @@
 ---
 name: soft-delete-relogin-consistency
 description: |
  Fix for missing auth/identity records after account deletion + device re-login.
  Use when: (1) User deletes account but device records are intentionally kept
  (e.g., to prevent trial abuse), (2) Re-login via device succeeds but user
  appears to have wrong identity type, (3) Frontend shows incorrect UI because
  auth_methods or similar identity records are empty/wrong after re-login,
  (4) Soft-deleted records cause stale cache entries that misrepresent user state.
  Covers GORM soft-delete, device-based auth, cache invalidation after re-creation.
 author: Codex
 version: 1.0.0
 date: 2026-03-11
 ---
 # Soft-Delete + Re-Login Auth Consistency
 ## Problem
 When a system uses soft-delete for auth/identity records during account deletion but
 intentionally keeps primary records (like device records) for abuse prevention, re-login
 flows may succeed at the "find existing record" step but fail to re-create the
 soft-deleted identity records. This causes the user to exist in an inconsistent state
 where they're authenticated but missing critical identity metadata.
 ## Context / Trigger Conditions
 - Account deletion (注销) soft-deletes `auth_methods` (or equivalent identity records)
 - Device/hardware records are intentionally kept to prevent trial reward abuse
 - Device-based re-login finds existing device record -> reuses old user_id
 - But the "device found" code path skips identity record creation (only the
  "device not found" registration path creates them)
 - Result: User is logged in but `auth_methods` is empty or missing the expected type
 - Frontend UI breaks because it relies on `auth_methods[0].auth_type` to determine
  login mode and show/hide UI elements
 ### Symptoms
 - Buttons or UI elements that should be hidden for device-only users appear after
  account deletion + re-login
 - API returns user info with empty or unexpected `auth_methods` array
 - `isDeviceLogin()` or similar identity checks return wrong results
 - Cache returns stale user data even after re-login
 ## Solution
 ### Step 1: Identify the re-login code path
 Find the "device found" branch in the login logic. This is the code path that runs
 when a device record already exists (as opposed to the registration path).
 ### Step 2: Add identity record existence check
 After finding the user via device record, check if the expected identity record exists:
 ```go
 // After finding user via existing device record
 hasDeviceAuth := false
 for _, am := range userInfo.AuthMethods {
    if am.AuthType == "device" && am.AuthIdentifier == req.Identifier {
        hasDeviceAuth = true
        break
    }
 }
 if !hasDeviceAuth {
    // Re-create the soft-deleted auth record
    authMethod := &user.AuthMethods{
        UserId:         userInfo.Id,
        AuthType:       "device",
        AuthIdentifier: req.Identifier,
        Verified:       true,
    }
    if createErr := db.Create(authMethod).Error; createErr != nil {
        log.Error("re-create auth method failed", err)
    } else {
        // CRITICAL: Clear user cache so subsequent reads return updated data
        _ = userModel.ClearUserCache(ctx, userInfo)
    }
 }
 ```
 ### Step 3: Ensure cache invalidation
 After re-creating the identity record, clear the user cache. This is critical because
 cached user data (with `Preload("AuthMethods")`) will still show the old empty state
 until the cache is invalidated.
 ### Step 4: Verify GORM soft-delete behavior
 GORM's soft-delete (`deleted_at IS NULL` filter) means:
 - `Preload("AuthMethods")` will NOT return soft-deleted records
 - `db.Create()` will create a NEW record (not undelete the old one)
 - The old soft-deleted record remains in the database (harmless)
 ## Verification
 1. Delete account (注销)
 2. Re-login via device
 3. Call user info API - verify `auth_methods` contains the device type
 4. Check frontend UI - verify device-specific UI state is correct
 ## Example
 **Before fix:**
 ```
 1. User has auth_methods: [device_A, email_A]
 2. User deletes account -> auth_methods all soft-deleted
 3. Device record kept (abuse prevention)
 4. User re-logins via same device
 5. FindOneDeviceByIdentifier finds device -> reuses user_id
 6. FindOne returns user with AuthMethods=[] (soft-deleted, filtered out)
 7. Frontend: isDeviceLogin() = false (no auth_methods) -> shows wrong buttons
 ```
 **After fix:**
 ```
 1-4. Same as above
 5. FindOneDeviceByIdentifier finds device -> reuses user_id
 6. FindOne returns user with AuthMethods=[]
 7. NEW: Detects missing device auth_method, re-creates it, clears cache
 8. Frontend: isDeviceLogin() = true -> correct UI
 ```
 ## Notes
 - This pattern applies broadly to any system where:
  - Account deletion removes identity records but keeps usage records
  - Re-login can succeed via the usage records
  - UI/business logic depends on the identity records existing
 - The "don't delete device records" design is intentional for preventing abuse
  (e.g., users repeatedly deleting and re-creating accounts to get trial rewards)
 - Cache invalidation is the most commonly missed step - without it, the fix appears
  to not work because cached data is served until TTL expires
 - Consider whether `Unscoped()` (GORM) should be used to also query soft-deleted
  records, or whether re-creation is the better approach (usually re-creation is
  cleaner as it creates a fresh record with correct timestamps)
 ## Related Patterns
 - **Cache key dependency chains**: When `ClearUserCache` depends on `AuthMethods`
  to generate email cache keys, capture auth_methods BEFORE deletion, then explicitly
  clear derived cache keys after the transaction
 - **Family ownership transfer**: When an owner exits a shared resource group, transfer
  ownership to a remaining member instead of dissolving the group
@@ -1,118 +0,0 @@
 ---
 name: sparc-methodology
 description: >
  SPARC development workflow: Specification, Pseudocode, Architecture, Refinement, Completion. A structured approach for complex implementations that ensures thorough planning before coding.
  Use when: new feature implementation, complex implementations, architectural changes, system redesign, integration work, unclear requirements.
  Skip when: simple bug fixes, documentation updates, configuration changes, well-defined small tasks, routine maintenance.
 ---
 # Sparc Methodology Skill
 ## Purpose
 SPARC development workflow: Specification, Pseudocode, Architecture, Refinement, Completion. A structured approach for complex implementations that ensures thorough planning before coding.
 ## When to Trigger
 - new feature implementation
 - complex implementations
 - architectural changes
 - system redesign
 - integration work
 - unclear requirements
 ## When to Skip
 - simple bug fixes
 - documentation updates
 - configuration changes
 - well-defined small tasks
 - routine maintenance
 ## Commands
 ### Specification Phase
 Define requirements, acceptance criteria, and constraints
 ```bash
 npx @claude-flow/cli hooks route --task "specification: [requirements]"
 ```
 **Example:**
 ```bash
 npx @claude-flow/cli hooks route --task "specification: user authentication with OAuth2, MFA, and session management"
 ```
 ### Pseudocode Phase
 Write high-level pseudocode for the implementation
 ```bash
 npx @claude-flow/cli hooks route --task "pseudocode: [feature]"
 ```
 **Example:**
 ```bash
 npx @claude-flow/cli hooks route --task "pseudocode: OAuth2 login flow with token refresh"
 ```
 ### Architecture Phase
 Design system structure, interfaces, and dependencies
 ```bash
 npx @claude-flow/cli hooks route --task "architecture: [design]"
 ```
 **Example:**
 ```bash
 npx @claude-flow/cli hooks route --task "architecture: auth module with service layer, repository, and API endpoints"
 ```
 ### Refinement Phase
 Iterate on the design based on feedback
 ```bash
 npx @claude-flow/cli hooks route --task "refinement: [feedback]"
 ```
 **Example:**
 ```bash
 npx @claude-flow/cli hooks route --task "refinement: add rate limiting and brute force protection"
 ```
 ### Completion Phase
 Finalize implementation with tests and documentation
 ```bash
 npx @claude-flow/cli hooks route --task "completion: [final checks]"
 ```
 **Example:**
 ```bash
 npx @claude-flow/cli hooks route --task "completion: verify all tests pass, update API docs, security review"
 ```
 ### SPARC Coordinator
 Spawn SPARC coordinator agent
 ```bash
 npx @claude-flow/cli agent spawn --type sparc-coord --name sparc-lead
 ```
 ## Scripts
 | Script | Path | Description |
 |--------|------|-------------|
 | `sparc-init` | `.agents/scripts/sparc-init.sh` | Initialize SPARC workflow for a new feature |
 | `sparc-review` | `.agents/scripts/sparc-review.sh` | Run SPARC phase review checklist |
 ## References
 | Document | Path | Description |
 |----------|------|-------------|
 | `SPARC Overview` | `docs/sparc.md` | Complete SPARC methodology guide |
 | `Phase Templates` | `docs/sparc-templates.md` | Templates for each SPARC phase |
 ## Best Practices
 1. Check memory for existing patterns before starting
 2. Use hierarchical topology for coordination
 3. Store successful patterns after completion
 4. Document any new learnings
@@ -1,563 +0,0 @@
 ---
 name: stream-chain
 description: Stream-JSON chaining for multi-agent pipelines, data transformation, and sequential workflows
 version: 1.0.0
 category: workflow
 tags: [streaming, pipeline, chaining, multi-agent, workflow]
 ---
 # Stream-Chain Skill
 Execute sophisticated multi-step workflows where each agent's output flows into the next, enabling complex data transformations and sequential processing pipelines.
 ## Overview
 Stream-Chain provides two powerful modes for orchestrating multi-agent workflows:
 1. **Custom Chains** (`run`): Execute custom prompt sequences with full control
 2. **Predefined Pipelines** (`pipeline`): Use battle-tested workflows for common tasks
 Each step in a chain receives the complete output from the previous step, enabling sophisticated multi-agent coordination through streaming data flow.
 ---
 ## Quick Start
 ### Run a Custom Chain
 ```bash
 Codex-flow stream-chain run \
  "Analyze codebase structure" \
  "Identify improvement areas" \
  "Generate action plan"
 ```
 ### Execute a Pipeline
 ```bash
 Codex-flow stream-chain pipeline analysis
 ```
 ---
 ## Custom Chains (`run`)
 Execute custom stream chains with your own prompts for maximum flexibility.
 ### Syntax
 ```bash
 Codex-flow stream-chain run <prompt1> <prompt2> [...] [options]
 ```
 **Requirements:**
 - Minimum 2 prompts required
 - Each prompt becomes a step in the chain
 - Output flows sequentially through all steps
 ### Options
 | Option | Description | Default |
 |--------|-------------|---------|
 | `--verbose` | Show detailed execution information | `false` |
 | `--timeout <seconds>` | Timeout per step | `30` |
 | `--debug` | Enable debug mode with full logging | `false` |
 ### How Context Flows
 Each step receives the previous output as context:
 ```
 Step 1: "Write a sorting function"
 Output: [function implementation]
 Step 2 receives:
  "Previous step output:
  [function implementation]
  Next task: Add comprehensive tests"
 Step 3 receives:
  "Previous steps output:
  [function + tests]
  Next task: Optimize performance"
 ```
 ### Examples
 #### Basic Development Chain
 ```bash
 Codex-flow stream-chain run \
  "Write a user authentication function" \
  "Add input validation and error handling" \
  "Create unit tests with edge cases"
 ```
 #### Security Audit Workflow
 ```bash
 Codex-flow stream-chain run \
  "Analyze authentication system for vulnerabilities" \
  "Identify and categorize security issues by severity" \
  "Propose fixes with implementation priority" \
  "Generate security test cases" \
  --timeout 45 \
  --verbose
 ```
 #### Code Refactoring Chain
 ```bash
 Codex-flow stream-chain run \
  "Identify code smells in src/ directory" \
  "Create refactoring plan with specific changes" \
  "Apply refactoring to top 3 priority items" \
  "Verify refactored code maintains behavior" \
  --debug
 ```
 #### Data Processing Pipeline
 ```bash
 Codex-flow stream-chain run \
  "Extract data from API responses" \
  "Transform data into normalized format" \
  "Validate data against schema" \
  "Generate data quality report"
 ```
 ---
 ## Predefined Pipelines (`pipeline`)
 Execute battle-tested workflows optimized for common development tasks.
 ### Syntax
 ```bash
 Codex-flow stream-chain pipeline <type> [options]
 ```
 ### Available Pipelines
 #### 1. Analysis Pipeline
 Comprehensive codebase analysis and improvement identification.
 ```bash
 Codex-flow stream-chain pipeline analysis
 ```
 **Workflow Steps:**
 1. **Structure Analysis**: Map directory structure and identify components
 2. **Issue Detection**: Find potential improvements and problems
 3. **Recommendations**: Generate actionable improvement report
 **Use Cases:**
 - New codebase onboarding
 - Technical debt assessment
 - Architecture review
 - Code quality audits
 #### 2. Refactor Pipeline
 Systematic code refactoring with prioritization.
 ```bash
 Codex-flow stream-chain pipeline refactor
 ```
 **Workflow Steps:**
 1. **Candidate Identification**: Find code needing refactoring
 2. **Prioritization**: Create ranked refactoring plan
 3. **Implementation**: Provide refactored code for top priorities
 **Use Cases:**
 - Technical debt reduction
 - Code quality improvement
 - Legacy code modernization
 - Design pattern implementation
 #### 3. Test Pipeline
 Comprehensive test generation with coverage analysis.
 ```bash
 Codex-flow stream-chain pipeline test
 ```
 **Workflow Steps:**
 1. **Coverage Analysis**: Identify areas lacking tests
 2. **Test Design**: Create test cases for critical functions
 3. **Implementation**: Generate unit tests with assertions
 **Use Cases:**
 - Increasing test coverage
 - TDD workflow support
 - Regression test creation
 - Quality assurance
 #### 4. Optimize Pipeline
 Performance optimization with profiling and implementation.
 ```bash
 Codex-flow stream-chain pipeline optimize
 ```
 **Workflow Steps:**
 1. **Profiling**: Identify performance bottlenecks
 2. **Strategy**: Analyze and suggest optimization approaches
 3. **Implementation**: Provide optimized code
 **Use Cases:**
 - Performance improvement
 - Resource optimization
 - Scalability enhancement
 - Latency reduction
 ### Pipeline Options
 | Option | Description | Default |
 |--------|-------------|---------|
 | `--verbose` | Show detailed execution | `false` |
 | `--timeout <seconds>` | Timeout per step | `30` |
 | `--debug` | Enable debug mode | `false` |
 ### Pipeline Examples
 #### Quick Analysis
 ```bash
 Codex-flow stream-chain pipeline analysis
 ```
 #### Extended Refactoring
 ```bash
 Codex-flow stream-chain pipeline refactor --timeout 60 --verbose
 ```
 #### Debug Test Generation
 ```bash
 Codex-flow stream-chain pipeline test --debug
 ```
 #### Comprehensive Optimization
 ```bash
 Codex-flow stream-chain pipeline optimize --timeout 90 --verbose
 ```
 ### Pipeline Output
 Each pipeline execution provides:
 - **Progress**: Step-by-step execution status
 - **Results**: Success/failure per step
 - **Timing**: Total and per-step execution time
 - **Summary**: Consolidated results and recommendations
 ---
 ## Custom Pipeline Definitions
 Define reusable pipelines in `.Codex-flow/config.json`:
 ### Configuration Format
 ```json
 {
  "streamChain": {
    "pipelines": {
      "security": {
        "name": "Security Audit Pipeline",
        "description": "Comprehensive security analysis",
        "prompts": [
          "Scan codebase for security vulnerabilities",
          "Categorize issues by severity (critical/high/medium/low)",
          "Generate fixes with priority and implementation steps",
          "Create security test suite"
        ],
        "timeout": 45
      },
      "documentation": {
        "name": "Documentation Generation Pipeline",
        "prompts": [
          "Analyze code structure and identify undocumented areas",
          "Generate API documentation with examples",
          "Create usage guides and tutorials",
          "Build architecture diagrams and flow charts"
        ]
      }
    }
  }
 }
 ```
 ### Execute Custom Pipeline
 ```bash
 Codex-flow stream-chain pipeline security
 Codex-flow stream-chain pipeline documentation
 ```
 ---
 ## Advanced Use Cases
 ### Multi-Agent Coordination
 Chain different agent types for complex workflows:
 ```bash
 Codex-flow stream-chain run \
  "Research best practices for API design" \
  "Design REST API with discovered patterns" \
  "Implement API endpoints with validation" \
  "Generate OpenAPI specification" \
  "Create integration tests" \
  "Write deployment documentation"
 ```
 ### Data Transformation Pipeline
 Process and transform data through multiple stages:
 ```bash
 Codex-flow stream-chain run \
  "Extract user data from CSV files" \
  "Normalize and validate data format" \
  "Enrich data with external API calls" \
  "Generate analytics report" \
  "Create visualization code"
 ```
 ### Code Migration Workflow
 Systematic code migration with validation:
 ```bash
 Codex-flow stream-chain run \
  "Analyze legacy codebase dependencies" \
  "Create migration plan with risk assessment" \
  "Generate modernized code for high-priority modules" \
  "Create migration tests" \
  "Document migration steps and rollback procedures"
 ```
 ### Quality Assurance Chain
 Comprehensive code quality workflow:
 ```bash
 Codex-flow stream-chain pipeline analysis
 Codex-flow stream-chain pipeline refactor
 Codex-flow stream-chain pipeline test
 Codex-flow stream-chain pipeline optimize
 ```
 ---
 ## Best Practices
 ### 1. Clear and Specific Prompts
 **Good:**
 ```bash
 "Analyze authentication.js for SQL injection vulnerabilities"
 ```
 **Avoid:**
 ```bash
 "Check security"
 ```
 ### 2. Logical Progression
 Order prompts to build on previous outputs:
 ```bash
 1. "Identify the problem"
 2. "Analyze root causes"
 3. "Design solution"
 4. "Implement solution"
 5. "Verify implementation"
 ```
 ### 3. Appropriate Timeouts
 - Simple tasks: 30 seconds (default)
 - Analysis tasks: 45-60 seconds
 - Implementation tasks: 60-90 seconds
 - Complex workflows: 90-120 seconds
 ### 4. Verification Steps
 Include validation in your chains:
 ```bash
 Codex-flow stream-chain run \
  "Implement feature X" \
  "Write tests for feature X" \
  "Verify tests pass and cover edge cases"
 ```
 ### 5. Iterative Refinement
 Use chains for iterative improvement:
 ```bash
 Codex-flow stream-chain run \
  "Generate initial implementation" \
  "Review and identify issues" \
  "Refine based on issues found" \
  "Final quality check"
 ```
 ---
 ## Integration with Codex Flow
 ### Combine with Swarm Coordination
 ```bash
 # Initialize swarm for coordination
 Codex-flow swarm init --topology mesh
 # Execute stream chain with swarm agents
 Codex-flow stream-chain run \
  "Agent 1: Research task" \
  "Agent 2: Implement solution" \
  "Agent 3: Test implementation" \
  "Agent 4: Review and refine"
 ```
 ### Memory Integration
 Stream chains automatically store context in memory for cross-session persistence:
 ```bash
 # Execute chain with memory
 Codex-flow stream-chain run \
  "Analyze requirements" \
  "Design architecture" \
  --verbose
 # Results stored in .Codex-flow/memory/stream-chain/
 ```
 ### Neural Pattern Training
 Successful chains train neural patterns for improved performance:
 ```bash
 # Enable neural training
 Codex-flow stream-chain pipeline optimize --debug
 # Patterns learned and stored for future optimizations
 ```
 ---
 ## Troubleshooting
 ### Chain Timeout
 If steps timeout, increase timeout value:
 ```bash
 Codex-flow stream-chain run "complex task" --timeout 120
 ```
 ### Context Loss
 If context not flowing properly, use `--debug`:
 ```bash
 Codex-flow stream-chain run "step 1" "step 2" --debug
 ```
 ### Pipeline Not Found
 Verify pipeline name and custom definitions:
 ```bash
 # Check available pipelines
 cat .Codex-flow/config.json | grep -A 10 "streamChain"
 ```
 ---
 ## Performance Characteristics
 - **Throughput**: 2-5 steps per minute (varies by complexity)
 - **Context Size**: Up to 100K tokens per step
 - **Memory Usage**: ~50MB per active chain
 - **Concurrency**: Supports parallel chain execution
 ---
 ## Related Skills
 - **SPARC Methodology**: Systematic development workflow
 - **Swarm Coordination**: Multi-agent orchestration
 - **Memory Management**: Persistent context storage
 - **Neural Patterns**: Adaptive learning
 ---
 ## Examples Repository
 ### Complete Development Workflow
 ```bash
 # Full feature development chain
 Codex-flow stream-chain run \
  "Analyze requirements for user profile feature" \
  "Design database schema and API endpoints" \
  "Implement backend with validation" \
  "Create frontend components" \
  "Write comprehensive tests" \
  "Generate API documentation" \
  --timeout 60 \
  --verbose
 ```
 ### Code Review Pipeline
 ```bash
 # Automated code review workflow
 Codex-flow stream-chain run \
  "Analyze recent git changes" \
  "Identify code quality issues" \
  "Check for security vulnerabilities" \
  "Verify test coverage" \
  "Generate code review report with recommendations"
 ```
 ### Migration Assistant
 ```bash
 # Framework migration helper
 Codex-flow stream-chain run \
  "Analyze current Vue 2 codebase" \
  "Identify Vue 3 breaking changes" \
  "Create migration checklist" \
  "Generate migration scripts" \
  "Provide updated code examples"
 ```
 ---
 ## Conclusion
 Stream-Chain enables sophisticated multi-step workflows by:
 - **Sequential Processing**: Each step builds on previous results
 - **Context Preservation**: Full output history flows through chain
 - **Flexible Orchestration**: Custom chains or predefined pipelines
 - **Agent Coordination**: Natural multi-agent collaboration pattern
 - **Data Transformation**: Complex processing through simple steps
 Use `run` for custom workflows and `pipeline` for battle-tested solutions.
@@ -1,973 +0,0 @@
 ---
 name: swarm-advanced
 description: Advanced swarm orchestration patterns for research, development, testing, and complex distributed workflows
 version: 2.0.0
 category: orchestration
 tags: [swarm, distributed, parallel, research, testing, development, coordination]
 author: Codex Flow Team
 ---
 # Advanced Swarm Orchestration
 Master advanced swarm patterns for distributed research, development, and testing workflows. This skill covers comprehensive orchestration strategies using both MCP tools and CLI commands.
 ## Quick Start
 ### Prerequisites
 ```bash
 # Ensure Codex Flow is installed
 npm install -g Codex-flow@alpha
 # Add MCP server (if using MCP tools)
 Codex mcp add Codex-flow npx Codex-flow@alpha mcp start
 ```
 ### Basic Pattern
 ```javascript
 // 1. Initialize swarm topology
 mcp__claude-flow__swarm_init({ topology: "mesh", maxAgents: 6 })
 // 2. Spawn specialized agents
 mcp__claude-flow__agent_spawn({ type: "researcher", name: "Agent 1" })
 // 3. Orchestrate tasks
 mcp__claude-flow__task_orchestrate({ task: "...", strategy: "parallel" })
 ```
 ## Core Concepts
 ### Swarm Topologies
 **Mesh Topology** - Peer-to-peer communication, best for research and analysis
 - All agents communicate directly
 - High flexibility and resilience
 - Use for: Research, analysis, brainstorming
 **Hierarchical Topology** - Coordinator with subordinates, best for development
 - Clear command structure
 - Sequential workflow support
 - Use for: Development, structured workflows
 **Star Topology** - Central coordinator, best for testing
 - Centralized control and monitoring
 - Parallel execution with coordination
 - Use for: Testing, validation, quality assurance
 **Ring Topology** - Sequential processing chain
 - Step-by-step processing
 - Pipeline workflows
 - Use for: Multi-stage processing, data pipelines
 ### Agent Strategies
 **Adaptive** - Dynamic adjustment based on task complexity
 **Balanced** - Equal distribution of work across agents
 **Specialized** - Task-specific agent assignment
 **Parallel** - Maximum concurrent execution
 ## Pattern 1: Research Swarm
 ### Purpose
 Deep research through parallel information gathering, analysis, and synthesis.
 ### Architecture
 ```javascript
 // Initialize research swarm
 mcp__claude-flow__swarm_init({
  "topology": "mesh",
  "maxAgents": 6,
  "strategy": "adaptive"
 })
 // Spawn research team
 const researchAgents = [
  {
    type: "researcher",
    name: "Web Researcher",
    capabilities: ["web-search", "content-extraction", "source-validation"]
  },
  {
    type: "researcher",
    name: "Academic Researcher",
    capabilities: ["paper-analysis", "citation-tracking", "literature-review"]
  },
  {
    type: "analyst",
    name: "Data Analyst",
    capabilities: ["data-processing", "statistical-analysis", "visualization"]
  },
  {
    type: "analyst",
    name: "Pattern Analyzer",
    capabilities: ["trend-detection", "correlation-analysis", "outlier-detection"]
  },
  {
    type: "documenter",
    name: "Report Writer",
    capabilities: ["synthesis", "technical-writing", "formatting"]
  }
 ]
 // Spawn all agents
 researchAgents.forEach(agent => {
  mcp__claude-flow__agent_spawn({
    type: agent.type,
    name: agent.name,
    capabilities: agent.capabilities
  })
 })
 ```
 ### Research Workflow
 #### Phase 1: Information Gathering
 ```javascript
 // Parallel information collection
 mcp__claude-flow__parallel_execute({
  "tasks": [
    {
      "id": "web-search",
      "command": "search recent publications and articles"
    },
    {
      "id": "academic-search",
      "command": "search academic databases and papers"
    },
    {
      "id": "data-collection",
      "command": "gather relevant datasets and statistics"
    },
    {
      "id": "expert-search",
      "command": "identify domain experts and thought leaders"
    }
  ]
 })
 // Store research findings in memory
 mcp__claude-flow__memory_usage({
  "action": "store",
  "key": "research-findings-" + Date.now(),
  "value": JSON.stringify(findings),
  "namespace": "research",
  "ttl": 604800 // 7 days
 })
 ```
 #### Phase 2: Analysis and Validation
 ```javascript
 // Pattern recognition in findings
 mcp__claude-flow__pattern_recognize({
  "data": researchData,
  "patterns": ["trend", "correlation", "outlier", "emerging-pattern"]
 })
 // Cognitive analysis
 mcp__claude-flow__cognitive_analyze({
  "behavior": "research-synthesis"
 })
 // Quality assessment
 mcp__claude-flow__quality_assess({
  "target": "research-sources",
  "criteria": ["credibility", "relevance", "recency", "authority"]
 })
 // Cross-reference validation
 mcp__claude-flow__neural_patterns({
  "action": "analyze",
  "operation": "fact-checking",
  "metadata": { "sources": sourcesArray }
 })
 ```
 #### Phase 3: Knowledge Management
 ```javascript
 // Search existing knowledge base
 mcp__claude-flow__memory_search({
  "pattern": "topic X",
  "namespace": "research",
  "limit": 20
 })
 // Create knowledge graph connections
 mcp__claude-flow__neural_patterns({
  "action": "learn",
  "operation": "knowledge-graph",
  "metadata": {
    "topic": "X",
    "connections": relatedTopics,
    "depth": 3
  }
 })
 // Store connections for future use
 mcp__claude-flow__memory_usage({
  "action": "store",
  "key": "knowledge-graph-X",
  "value": JSON.stringify(knowledgeGraph),
  "namespace": "research/graphs",
  "ttl": 2592000 // 30 days
 })
 ```
 #### Phase 4: Report Generation
 ```javascript
 // Orchestrate report generation
 mcp__claude-flow__task_orchestrate({
  "task": "generate comprehensive research report",
  "strategy": "sequential",
  "priority": "high",
  "dependencies": ["gather", "analyze", "validate", "synthesize"]
 })
 // Monitor research progress
 mcp__claude-flow__swarm_status({
  "swarmId": "research-swarm"
 })
 // Generate final report
 mcp__claude-flow__workflow_execute({
  "workflowId": "research-report-generation",
  "params": {
    "findings": findings,
    "format": "comprehensive",
    "sections": ["executive-summary", "methodology", "findings", "analysis", "conclusions", "references"]
  }
 })
 ```
 ### CLI Fallback
 ```bash
 # Quick research swarm
 npx Codex-flow swarm "research AI trends in 2025" \
  --strategy research \
  --mode distributed \
  --max-agents 6 \
  --parallel \
  --output research-report.md
 ```
 ## Pattern 2: Development Swarm
 ### Purpose
 Full-stack development through coordinated specialist agents.
 ### Architecture
 ```javascript
 // Initialize development swarm with hierarchy
 mcp__claude-flow__swarm_init({
  "topology": "hierarchical",
  "maxAgents": 8,
  "strategy": "balanced"
 })
 // Spawn development team
 const devTeam = [
  { type: "architect", name: "System Architect", role: "coordinator" },
  { type: "coder", name: "Backend Developer", capabilities: ["node", "api", "database"] },
  { type: "coder", name: "Frontend Developer", capabilities: ["react", "ui", "ux"] },
  { type: "coder", name: "Database Engineer", capabilities: ["sql", "nosql", "optimization"] },
  { type: "tester", name: "QA Engineer", capabilities: ["unit", "integration", "e2e"] },
  { type: "reviewer", name: "Code Reviewer", capabilities: ["security", "performance", "best-practices"] },
  { type: "documenter", name: "Technical Writer", capabilities: ["api-docs", "guides", "tutorials"] },
  { type: "monitor", name: "DevOps Engineer", capabilities: ["ci-cd", "deployment", "monitoring"] }
 ]
 // Spawn all team members
 devTeam.forEach(member => {
  mcp__claude-flow__agent_spawn({
    type: member.type,
    name: member.name,
    capabilities: member.capabilities,
    swarmId: "dev-swarm"
  })
 })
 ```
 ### Development Workflow
 #### Phase 1: Architecture and Design
 ```javascript
 // System architecture design
 mcp__claude-flow__task_orchestrate({
  "task": "design system architecture for REST API",
  "strategy": "sequential",
  "priority": "critical",
  "assignTo": "System Architect"
 })
 // Store architecture decisions
 mcp__claude-flow__memory_usage({
  "action": "store",
  "key": "architecture-decisions",
  "value": JSON.stringify(architectureDoc),
  "namespace": "development/design"
 })
 ```
 #### Phase 2: Parallel Implementation
 ```javascript
 // Parallel development tasks
 mcp__claude-flow__parallel_execute({
  "tasks": [
    {
      "id": "backend-api",
      "command": "implement REST API endpoints",
      "assignTo": "Backend Developer"
    },
    {
      "id": "frontend-ui",
      "command": "build user interface components",
      "assignTo": "Frontend Developer"
    },
    {
      "id": "database-schema",
      "command": "design and implement database schema",
      "assignTo": "Database Engineer"
    },
    {
      "id": "api-documentation",
      "command": "create API documentation",
      "assignTo": "Technical Writer"
    }
  ]
 })
 // Monitor development progress
 mcp__claude-flow__swarm_monitor({
  "swarmId": "dev-swarm",
  "interval": 5000
 })
 ```
 #### Phase 3: Testing and Validation
 ```javascript
 // Comprehensive testing
 mcp__claude-flow__batch_process({
  "items": [
    { type: "unit", target: "all-modules" },
    { type: "integration", target: "api-endpoints" },
    { type: "e2e", target: "user-flows" },
    { type: "performance", target: "critical-paths" }
  ],
  "operation": "execute-tests"
 })
 // Quality assessment
 mcp__claude-flow__quality_assess({
  "target": "codebase",
  "criteria": ["coverage", "complexity", "maintainability", "security"]
 })
 ```
 #### Phase 4: Review and Deployment
 ```javascript
 // Code review workflow
 mcp__claude-flow__workflow_execute({
  "workflowId": "code-review-process",
  "params": {
    "reviewers": ["Code Reviewer"],
    "criteria": ["security", "performance", "best-practices"]
  }
 })
 // CI/CD pipeline
 mcp__claude-flow__pipeline_create({
  "config": {
    "stages": ["build", "test", "security-scan", "deploy"],
    "environment": "production"
  }
 })
 ```
 ### CLI Fallback
 ```bash
 # Quick development swarm
 npx Codex-flow swarm "build REST API with authentication" \
  --strategy development \
  --mode hierarchical \
  --monitor \
  --output sqlite
 ```
 ## Pattern 3: Testing Swarm
 ### Purpose
 Comprehensive quality assurance through distributed testing.
 ### Architecture
 ```javascript
 // Initialize testing swarm with star topology
 mcp__claude-flow__swarm_init({
  "topology": "star",
  "maxAgents": 7,
  "strategy": "parallel"
 })
 // Spawn testing team
 const testingTeam = [
  {
    type: "tester",
    name: "Unit Test Coordinator",
    capabilities: ["unit-testing", "mocking", "coverage", "tdd"]
  },
  {
    type: "tester",
    name: "Integration Tester",
    capabilities: ["integration", "api-testing", "contract-testing"]
  },
  {
    type: "tester",
    name: "E2E Tester",
    capabilities: ["e2e", "ui-testing", "user-flows", "selenium"]
  },
  {
    type: "tester",
    name: "Performance Tester",
    capabilities: ["load-testing", "stress-testing", "benchmarking"]
  },
  {
    type: "monitor",
    name: "Security Tester",
    capabilities: ["security-testing", "penetration-testing", "vulnerability-scanning"]
  },
  {
    type: "analyst",
    name: "Test Analyst",
    capabilities: ["coverage-analysis", "test-optimization", "reporting"]
  },
  {
    type: "documenter",
    name: "Test Documenter",
    capabilities: ["test-documentation", "test-plans", "reports"]
  }
 ]
 // Spawn all testers
 testingTeam.forEach(tester => {
  mcp__claude-flow__agent_spawn({
    type: tester.type,
    name: tester.name,
    capabilities: tester.capabilities,
    swarmId: "testing-swarm"
  })
 })
 ```
 ### Testing Workflow
 #### Phase 1: Test Planning
 ```javascript
 // Analyze test coverage requirements
 mcp__claude-flow__quality_assess({
  "target": "test-coverage",
  "criteria": [
    "line-coverage",
    "branch-coverage",
    "function-coverage",
    "edge-cases"
  ]
 })
 // Identify test scenarios
 mcp__claude-flow__pattern_recognize({
  "data": testScenarios,
  "patterns": [
    "edge-case",
    "boundary-condition",
    "error-path",
    "happy-path"
  ]
 })
 // Store test plan
 mcp__claude-flow__memory_usage({
  "action": "store",
  "key": "test-plan-" + Date.now(),
  "value": JSON.stringify(testPlan),
  "namespace": "testing/plans"
 })
 ```
 #### Phase 2: Parallel Test Execution
 ```javascript
 // Execute all test suites in parallel
 mcp__claude-flow__parallel_execute({
  "tasks": [
    {
      "id": "unit-tests",
      "command": "npm run test:unit",
      "assignTo": "Unit Test Coordinator"
    },
    {
      "id": "integration-tests",
      "command": "npm run test:integration",
      "assignTo": "Integration Tester"
    },
    {
      "id": "e2e-tests",
      "command": "npm run test:e2e",
      "assignTo": "E2E Tester"
    },
    {
      "id": "performance-tests",
      "command": "npm run test:performance",
      "assignTo": "Performance Tester"
    },
    {
      "id": "security-tests",
      "command": "npm run test:security",
      "assignTo": "Security Tester"
    }
  ]
 })
 // Batch process test suites
 mcp__claude-flow__batch_process({
  "items": testSuites,
  "operation": "execute-test-suite"
 })
 ```
 #### Phase 3: Performance and Security
 ```javascript
 // Run performance benchmarks
 mcp__claude-flow__benchmark_run({
  "suite": "comprehensive-performance"
 })
 // Bottleneck analysis
 mcp__claude-flow__bottleneck_analyze({
  "component": "application",
  "metrics": ["response-time", "throughput", "memory", "cpu"]
 })
 // Security scanning
 mcp__claude-flow__security_scan({
  "target": "application",
  "depth": "comprehensive"
 })
 // Vulnerability analysis
 mcp__claude-flow__error_analysis({
  "logs": securityScanLogs
 })
 ```
 #### Phase 4: Monitoring and Reporting
 ```javascript
 // Real-time test monitoring
 mcp__claude-flow__swarm_monitor({
  "swarmId": "testing-swarm",
  "interval": 2000
 })
 // Generate comprehensive test report
 mcp__claude-flow__performance_report({
  "format": "detailed",
  "timeframe": "current-run"
 })
 // Get test results
 mcp__claude-flow__task_results({
  "taskId": "test-execution-001"
 })
 // Trend analysis
 mcp__claude-flow__trend_analysis({
  "metric": "test-coverage",
  "period": "30d"
 })
 ```
 ### CLI Fallback
 ```bash
 # Quick testing swarm
 npx Codex-flow swarm "test application comprehensively" \
  --strategy testing \
  --mode star \
  --parallel \
  --timeout 600
 ```
 ## Pattern 4: Analysis Swarm
 ### Purpose
 Deep code and system analysis through specialized analyzers.
 ### Architecture
 ```javascript
 // Initialize analysis swarm
 mcp__claude-flow__swarm_init({
  "topology": "mesh",
  "maxAgents": 5,
  "strategy": "adaptive"
 })
 // Spawn analysis specialists
 const analysisTeam = [
  {
    type: "analyst",
    name: "Code Analyzer",
    capabilities: ["static-analysis", "complexity-analysis", "dead-code-detection"]
  },
  {
    type: "analyst",
    name: "Security Analyzer",
    capabilities: ["security-scan", "vulnerability-detection", "dependency-audit"]
  },
  {
    type: "analyst",
    name: "Performance Analyzer",
    capabilities: ["profiling", "bottleneck-detection", "optimization"]
  },
  {
    type: "analyst",
    name: "Architecture Analyzer",
    capabilities: ["dependency-analysis", "coupling-detection", "modularity-assessment"]
  },
  {
    type: "documenter",
    name: "Analysis Reporter",
    capabilities: ["reporting", "visualization", "recommendations"]
  }
 ]
 // Spawn all analysts
 analysisTeam.forEach(analyst => {
  mcp__claude-flow__agent_spawn({
    type: analyst.type,
    name: analyst.name,
    capabilities: analyst.capabilities
  })
 })
 ```
 ### Analysis Workflow
 ```javascript
 // Parallel analysis execution
 mcp__claude-flow__parallel_execute({
  "tasks": [
    { "id": "analyze-code", "command": "analyze codebase structure and quality" },
    { "id": "analyze-security", "command": "scan for security vulnerabilities" },
    { "id": "analyze-performance", "command": "identify performance bottlenecks" },
    { "id": "analyze-architecture", "command": "assess architectural patterns" }
  ]
 })
 // Generate comprehensive analysis report
 mcp__claude-flow__performance_report({
  "format": "detailed",
  "timeframe": "current"
 })
 // Cost analysis
 mcp__claude-flow__cost_analysis({
  "timeframe": "30d"
 })
 ```
 ## Advanced Techniques
 ### Error Handling and Fault Tolerance
 ```javascript
 // Setup fault tolerance for all agents
 mcp__claude-flow__daa_fault_tolerance({
  "agentId": "all",
  "strategy": "auto-recovery"
 })
 // Error handling pattern
 try {
  await mcp__claude-flow__task_orchestrate({
    "task": "complex operation",
    "strategy": "parallel",
    "priority": "high"
  })
 } catch (error) {
  // Check swarm health
  const status = await mcp__claude-flow__swarm_status({})
  // Analyze error patterns
  await mcp__claude-flow__error_analysis({
    "logs": [error.message]
  })
  // Auto-recovery attempt
  if (status.healthy) {
    await mcp__claude-flow__task_orchestrate({
      "task": "retry failed operation",
      "strategy": "sequential"
    })
  }
 }
 ```
 ### Memory and State Management
 ```javascript
 // Cross-session persistence
 mcp__claude-flow__memory_persist({
  "sessionId": "swarm-session-001"
 })
 // Namespace management for different swarms
 mcp__claude-flow__memory_namespace({
  "namespace": "research-swarm",
  "action": "create"
 })
 // Create state snapshot
 mcp__claude-flow__state_snapshot({
  "name": "development-checkpoint-1"
 })
 // Restore from snapshot if needed
 mcp__claude-flow__context_restore({
  "snapshotId": "development-checkpoint-1"
 })
 // Backup memory stores
 mcp__claude-flow__memory_backup({
  "path": "/workspaces/Codex-flow/backups/swarm-memory.json"
 })
 ```
 ### Neural Pattern Learning
 ```javascript
 // Train neural patterns from successful workflows
 mcp__claude-flow__neural_train({
  "pattern_type": "coordination",
  "training_data": JSON.stringify(successfulWorkflows),
  "epochs": 50
 })
 // Adaptive learning from experience
 mcp__claude-flow__learning_adapt({
  "experience": {
    "workflow": "research-to-report",
    "success": true,
    "duration": 3600,
    "quality": 0.95
  }
 })
 // Pattern recognition for optimization
 mcp__claude-flow__pattern_recognize({
  "data": workflowMetrics,
  "patterns": ["bottleneck", "optimization-opportunity", "efficiency-gain"]
 })
 ```
 ### Workflow Automation
 ```javascript
 // Create reusable workflow
 mcp__claude-flow__workflow_create({
  "name": "full-stack-development",
  "steps": [
    { "phase": "design", "agents": ["architect"] },
    { "phase": "implement", "agents": ["backend-dev", "frontend-dev"], "parallel": true },
    { "phase": "test", "agents": ["tester", "security-tester"], "parallel": true },
    { "phase": "review", "agents": ["reviewer"] },
    { "phase": "deploy", "agents": ["devops"] }
  ],
  "triggers": ["on-commit", "scheduled-daily"]
 })
 // Setup automation rules
 mcp__claude-flow__automation_setup({
  "rules": [
    {
      "trigger": "file-changed",
      "pattern": "*.js",
      "action": "run-tests"
    },
    {
      "trigger": "PR-created",
      "action": "code-review-swarm"
    }
  ]
 })
 // Event-driven triggers
 mcp__claude-flow__trigger_setup({
  "events": ["code-commit", "PR-merge", "deployment"],
  "actions": ["test", "analyze", "document"]
 })
 ```
 ### Performance Optimization
 ```javascript
 // Topology optimization
 mcp__claude-flow__topology_optimize({
  "swarmId": "current-swarm"
 })
 // Load balancing
 mcp__claude-flow__load_balance({
  "swarmId": "development-swarm",
  "tasks": taskQueue
 })
 // Agent coordination sync
 mcp__claude-flow__coordination_sync({
  "swarmId": "development-swarm"
 })
 // Auto-scaling
 mcp__claude-flow__swarm_scale({
  "swarmId": "development-swarm",
  "targetSize": 12
 })
 ```
 ### Monitoring and Metrics
 ```javascript
 // Real-time swarm monitoring
 mcp__claude-flow__swarm_monitor({
  "swarmId": "active-swarm",
  "interval": 3000
 })
 // Collect comprehensive metrics
 mcp__claude-flow__metrics_collect({
  "components": ["agents", "tasks", "memory", "performance"]
 })
 // Health monitoring
 mcp__claude-flow__health_check({
  "components": ["swarm", "agents", "neural", "memory"]
 })
 // Usage statistics
 mcp__claude-flow__usage_stats({
  "component": "swarm-orchestration"
 })
 // Trend analysis
 mcp__claude-flow__trend_analysis({
  "metric": "agent-performance",
  "period": "7d"
 })
 ```
 ## Best Practices
 ### 1. Choosing the Right Topology
 - **Mesh**: Research, brainstorming, collaborative analysis
 - **Hierarchical**: Structured development, sequential workflows
 - **Star**: Testing, validation, centralized coordination
 - **Ring**: Pipeline processing, staged workflows
 ### 2. Agent Specialization
 - Assign specific capabilities to each agent
 - Avoid overlapping responsibilities
 - Use coordination agents for complex workflows
 - Leverage memory for agent communication
 ### 3. Parallel Execution
 - Identify independent tasks for parallelization
 - Use sequential execution for dependent tasks
 - Monitor resource usage during parallel execution
 - Implement proper error handling
 ### 4. Memory Management
 - Use namespaces to organize memory
 - Set appropriate TTL values
 - Create regular backups
 - Implement state snapshots for checkpoints
 ### 5. Monitoring and Optimization
 - Monitor swarm health regularly
 - Collect and analyze metrics
 - Optimize topology based on performance
 - Use neural patterns to learn from success
 ### 6. Error Recovery
 - Implement fault tolerance strategies
 - Use auto-recovery mechanisms
 - Analyze error patterns
 - Create fallback workflows
 ## Real-World Examples
 ### Example 1: AI Research Project
 ```javascript
 // Research AI trends, analyze findings, generate report
 mcp__claude-flow__swarm_init({ topology: "mesh", maxAgents: 6 })
 // Spawn: 2 researchers, 2 analysts, 1 synthesizer, 1 documenter
 // Parallel gather → Analyze patterns → Synthesize → Report
 ```
 ### Example 2: Full-Stack Application
 ```javascript
 // Build complete web application with testing
 mcp__claude-flow__swarm_init({ topology: "hierarchical", maxAgents: 8 })
 // Spawn: 1 architect, 2 devs, 1 db engineer, 2 testers, 1 reviewer, 1 devops
 // Design → Parallel implement → Test → Review → Deploy
 ```
 ### Example 3: Security Audit
 ```javascript
 // Comprehensive security analysis
 mcp__claude-flow__swarm_init({ topology: "star", maxAgents: 5 })
 // Spawn: 1 coordinator, 1 code analyzer, 1 security scanner, 1 penetration tester, 1 reporter
 // Parallel scan → Vulnerability analysis → Penetration test → Report
 ```
 ### Example 4: Performance Optimization
 ```javascript
 // Identify and fix performance bottlenecks
 mcp__claude-flow__swarm_init({ topology: "mesh", maxAgents: 4 })
 // Spawn: 1 profiler, 1 bottleneck analyzer, 1 optimizer, 1 tester
 // Profile → Identify bottlenecks → Optimize → Validate
 ```
 ## Troubleshooting
 ### Common Issues
 **Issue**: Swarm agents not coordinating properly
 **Solution**: Check topology selection, verify memory usage, enable monitoring
 **Issue**: Parallel execution failing
 **Solution**: Verify task dependencies, check resource limits, implement error handling
 **Issue**: Memory persistence not working
 **Solution**: Verify namespaces, check TTL settings, ensure backup configuration
 **Issue**: Performance degradation
 **Solution**: Optimize topology, reduce agent count, analyze bottlenecks
 ## Related Skills
 - `sparc-methodology` - Systematic development workflow
 - `github-integration` - Repository management and automation
 - `neural-patterns` - AI-powered coordination optimization
 - `memory-management` - Cross-session state persistence
 ## References
 - [Codex Flow Documentation](https://github.com/ruvnet/Codex-flow)
 - [Swarm Orchestration Guide](https://github.com/ruvnet/Codex-flow/wiki/swarm)
 - [MCP Tools Reference](https://github.com/ruvnet/Codex-flow/wiki/mcp)
 - [Performance Optimization](https://github.com/ruvnet/Codex-flow/wiki/performance)
 ---
 **Version**: 2.0.0
 **Last Updated**: 2025-10-19
 **Skill Level**: Advanced
 **Estimated Learning Time**: 2-3 hours
@@ -1,114 +0,0 @@
 ---
 name: swarm-orchestration
 description: >
  Multi-agent swarm coordination for complex tasks. Uses hierarchical topology with specialized agents to break down and execute complex work across multiple files and modules.
  Use when: 3+ files need changes, new feature implementation, cross-module refactoring, API changes with tests, security-related changes, performance optimization across codebase, database schema changes.
  Skip when: single file edits, simple bug fixes (1-2 lines), documentation updates, configuration changes, quick exploration.
 ---
 # Swarm Orchestration Skill
 ## Purpose
 Multi-agent swarm coordination for complex tasks. Uses hierarchical topology with specialized agents to break down and execute complex work across multiple files and modules.
 ## When to Trigger
 - 3+ files need changes
 - new feature implementation
 - cross-module refactoring
 - API changes with tests
 - security-related changes
 - performance optimization across codebase
 - database schema changes
 ## When to Skip
 - single file edits
 - simple bug fixes (1-2 lines)
 - documentation updates
 - configuration changes
 - quick exploration
 ## Commands
 ### Initialize Swarm
 Start a new swarm with hierarchical topology (anti-drift)
 ```bash
 npx @claude-flow/cli swarm init --topology hierarchical --max-agents 8 --strategy specialized
 ```
 **Example:**
 ```bash
 npx @claude-flow/cli swarm init --topology hierarchical --max-agents 6 --strategy specialized
 ```
 ### Route Task
 Route a task to the appropriate agents based on task type
 ```bash
 npx @claude-flow/cli hooks route --task "[task description]"
 ```
 **Example:**
 ```bash
 npx @claude-flow/cli hooks route --task "implement OAuth2 authentication flow"
 ```
 ### Spawn Agent
 Spawn a specific agent type
 ```bash
 npx @claude-flow/cli agent spawn --type [type] --name [name]
 ```
 **Example:**
 ```bash
 npx @claude-flow/cli agent spawn --type coder --name impl-auth
 ```
 ### Monitor Status
 Check the current swarm status
 ```bash
 npx @claude-flow/cli swarm status --verbose
 ```
 ### Orchestrate Task
 Orchestrate a task across multiple agents
 ```bash
 npx @claude-flow/cli task orchestrate --task "[task]" --strategy adaptive
 ```
 **Example:**
 ```bash
 npx @claude-flow/cli task orchestrate --task "refactor auth module" --strategy parallel --max-agents 4
 ```
 ### List Agents
 List all active agents
 ```bash
 npx @claude-flow/cli agent list --filter active
 ```
 ## Scripts
 | Script | Path | Description |
 |--------|------|-------------|
 | `swarm-start` | `.agents/scripts/swarm-start.sh` | Initialize swarm with default settings |
 | `swarm-monitor` | `.agents/scripts/swarm-monitor.sh` | Real-time swarm monitoring dashboard |
 ## References
 | Document | Path | Description |
 |----------|------|-------------|
 | `Agent Types` | `docs/agents.md` | Complete list of agent types and capabilities |
 | `Topology Guide` | `docs/topology.md` | Swarm topology configuration guide |
 ## Best Practices
 1. Check memory for existing patterns before starting
 2. Use hierarchical topology for coordination
 3. Store successful patterns after completion
 4. Document any new learnings
@@ -1,872 +0,0 @@
 ---
 name: "V3 CLI Modernization"
 description: "CLI modernization and hooks system enhancement for Codex-flow v3. Implements interactive prompts, command decomposition, enhanced hooks integration, and intelligent workflow automation."
 ---
 # V3 CLI Modernization
 ## What This Skill Does
 Modernizes Codex-flow v3 CLI with interactive prompts, intelligent command decomposition, enhanced hooks integration, performance optimization, and comprehensive workflow automation capabilities.
 ## Quick Start
 ```bash
 # Initialize CLI modernization analysis
 Task("CLI architecture", "Analyze current CLI structure and identify optimization opportunities", "cli-hooks-developer")
 # Modernization implementation (parallel)
 Task("Command decomposition", "Break down large CLI files into focused modules", "cli-hooks-developer")
 Task("Interactive prompts", "Implement intelligent interactive CLI experience", "cli-hooks-developer")
 Task("Hooks enhancement", "Deep integrate hooks with CLI lifecycle", "cli-hooks-developer")
 ```
 ## CLI Architecture Modernization
 ### Current State Analysis
 ```
 Current CLI Issues:
 ├── index.ts: 108KB monolithic file
 ├── enterprise.ts: 68KB feature module
 ├── Limited interactivity: Basic command parsing
 ├── Hooks integration: Basic pre/post execution
 └── No intelligent workflows: Manual command chaining
 Target Architecture:
 ├── Modular Commands: <500 lines per command
 ├── Interactive Prompts: Smart context-aware UX
 ├── Enhanced Hooks: Deep lifecycle integration
 ├── Workflow Automation: Intelligent command orchestration
 └── Performance: <200ms command response time
 ```
 ### Modular Command Architecture
 ```typescript
 // src/cli/core/command-registry.ts
 interface CommandModule {
  name: string;
  description: string;
  category: CommandCategory;
  handler: CommandHandler;
  middleware: MiddlewareStack;
  permissions: Permission[];
  examples: CommandExample[];
 }
 export class ModularCommandRegistry {
  private commands = new Map<string, CommandModule>();
  private categories = new Map<CommandCategory, CommandModule[]>();
  private aliases = new Map<string, string>();
  registerCommand(command: CommandModule): void {
    this.commands.set(command.name, command);
    // Register in category index
    if (!this.categories.has(command.category)) {
      this.categories.set(command.category, []);
    }
    this.categories.get(command.category)!.push(command);
  }
  async executeCommand(name: string, args: string[]): Promise<CommandResult> {
    const command = this.resolveCommand(name);
    if (!command) {
      throw new CommandNotFoundError(name, this.getSuggestions(name));
    }
    // Execute middleware stack
    const context = await this.buildExecutionContext(command, args);
    const result = await command.middleware.execute(context);
    return result;
  }
  private resolveCommand(name: string): CommandModule | undefined {
    // Try exact match first
    if (this.commands.has(name)) {
      return this.commands.get(name);
    }
    // Try alias
    const aliasTarget = this.aliases.get(name);
    if (aliasTarget) {
      return this.commands.get(aliasTarget);
    }
    // Try fuzzy match
    return this.findFuzzyMatch(name);
  }
 }
 ```
 ## Command Decomposition Strategy
 ### Swarm Commands Module
 ```typescript
 // src/cli/commands/swarm/swarm.command.ts
@Command({
  name: 'swarm',
  description: 'Swarm coordination and management',
  category: 'orchestration'
 })
 export class SwarmCommand {
  constructor(
    private swarmCoordinator: UnifiedSwarmCoordinator,
    private promptService: InteractivePromptService
  ) {}
  @SubCommand('init')
  @Option('--topology', 'Swarm topology (mesh|hierarchical|adaptive)', 'hierarchical')
  @Option('--agents', 'Number of agents to spawn', 5)
  @Option('--interactive', 'Interactive agent configuration', false)
  async init(
    @Arg('projectName') projectName: string,
    options: SwarmInitOptions
  ): Promise<CommandResult> {
    if (options.interactive) {
      return this.interactiveSwarmInit(projectName);
    }
    return this.quickSwarmInit(projectName, options);
  }
  private async interactiveSwarmInit(projectName: string): Promise<CommandResult> {
    console.log(`🚀 Initializing Swarm for ${projectName}`);
    // Interactive topology selection
    const topology = await this.promptService.select({
      message: 'Select swarm topology:',
      choices: [
        { name: 'Hierarchical (Queen-led coordination)', value: 'hierarchical' },
        { name: 'Mesh (Peer-to-peer collaboration)', value: 'mesh' },
        { name: 'Adaptive (Dynamic topology switching)', value: 'adaptive' }
      ]
    });
    // Agent configuration
    const agents = await this.promptAgentConfiguration();
    // Initialize with configuration
    const swarm = await this.swarmCoordinator.initialize({
      name: projectName,
      topology,
      agents,
      hooks: {
        onAgentSpawn: this.handleAgentSpawn.bind(this),
        onTaskComplete: this.handleTaskComplete.bind(this),
        onSwarmComplete: this.handleSwarmComplete.bind(this)
      }
    });
    return CommandResult.success({
      message: `✅ Swarm ${projectName} initialized with ${agents.length} agents`,
      data: { swarmId: swarm.id, topology, agentCount: agents.length }
    });
  }
  @SubCommand('status')
  async status(): Promise<CommandResult> {
    const swarms = await this.swarmCoordinator.listActiveSwarms();
    if (swarms.length === 0) {
      return CommandResult.info('No active swarms found');
    }
    // Interactive swarm selection if multiple
    const selectedSwarm = swarms.length === 1
      ? swarms[0]
      : await this.promptService.select({
          message: 'Select swarm to inspect:',
          choices: swarms.map(s => ({
            name: `${s.name} (${s.agents.length} agents, ${s.topology})`,
            value: s
          }))
        });
    return this.displaySwarmStatus(selectedSwarm);
  }
 }
 ```
 ### Learning Commands Module
 ```typescript
 // src/cli/commands/learning/learning.command.ts
@Command({
  name: 'learning',
  description: 'Learning system management and optimization',
  category: 'intelligence'
 })
 export class LearningCommand {
  constructor(
    private learningService: IntegratedLearningService,
    private promptService: InteractivePromptService
  ) {}
  @SubCommand('start')
  @Option('--algorithm', 'RL algorithm to use', 'auto')
  @Option('--tier', 'Learning tier (basic|standard|advanced)', 'standard')
  async start(options: LearningStartOptions): Promise<CommandResult> {
    // Auto-detect optimal algorithm if not specified
    if (options.algorithm === 'auto') {
      const taskContext = await this.analyzeCurrentContext();
      options.algorithm = this.learningService.selectOptimalAlgorithm(taskContext);
      console.log(`🧠 Auto-selected ${options.algorithm} algorithm based on context`);
    }
    const session = await this.learningService.startSession({
      algorithm: options.algorithm,
      tier: options.tier,
      userId: await this.getCurrentUser()
    });
    return CommandResult.success({
      message: `🚀 Learning session started with ${options.algorithm}`,
      data: { sessionId: session.id, algorithm: options.algorithm, tier: options.tier }
    });
  }
  @SubCommand('feedback')
  @Arg('reward', 'Reward value (0-1)', 'number')
  async feedback(
    @Arg('reward') reward: number,
    @Option('--context', 'Additional context for learning')
    context?: string
  ): Promise<CommandResult> {
    const activeSession = await this.learningService.getActiveSession();
    if (!activeSession) {
      return CommandResult.error('No active learning session found. Start one with `learning start`');
    }
    await this.learningService.submitFeedback({
      sessionId: activeSession.id,
      reward,
      context,
      timestamp: new Date()
    });
    return CommandResult.success({
      message: `📊 Feedback recorded (reward: ${reward})`,
      data: { reward, sessionId: activeSession.id }
    });
  }
  @SubCommand('metrics')
  async metrics(): Promise<CommandResult> {
    const metrics = await this.learningService.getMetrics();
    // Interactive metrics display
    await this.displayInteractiveMetrics(metrics);
    return CommandResult.success('Metrics displayed');
  }
 }
 ```
 ## Interactive Prompt System
 ### Advanced Prompt Service
 ```typescript
 // src/cli/services/interactive-prompt.service.ts
 interface PromptOptions {
  message: string;
  type: 'select' | 'multiselect' | 'input' | 'confirm' | 'progress';
  choices?: PromptChoice[];
  default?: any;
  validate?: (input: any) => boolean | string;
  transform?: (input: any) => any;
 }
 export class InteractivePromptService {
  private inquirer: any; // Dynamic import for tree-shaking
  async select<T>(options: SelectPromptOptions<T>): Promise<T> {
    const { default: inquirer } = await import('inquirer');
    const result = await inquirer.prompt([{
      type: 'list',
      name: 'selection',
      message: options.message,
      choices: options.choices,
      default: options.default
    }]);
    return result.selection;
  }
  async multiSelect<T>(options: MultiSelectPromptOptions<T>): Promise<T[]> {
    const { default: inquirer } = await import('inquirer');
    const result = await inquirer.prompt([{
      type: 'checkbox',
      name: 'selections',
      message: options.message,
      choices: options.choices,
      validate: (input: T[]) => {
        if (options.minSelections && input.length < options.minSelections) {
          return `Please select at least ${options.minSelections} options`;
        }
        if (options.maxSelections && input.length > options.maxSelections) {
          return `Please select at most ${options.maxSelections} options`;
        }
        return true;
      }
    }]);
    return result.selections;
  }
  async input(options: InputPromptOptions): Promise<string> {
    const { default: inquirer } = await import('inquirer');
    const result = await inquirer.prompt([{
      type: 'input',
      name: 'input',
      message: options.message,
      default: options.default,
      validate: options.validate,
      transformer: options.transform
    }]);
    return result.input;
  }
  async progressTask<T>(
    task: ProgressTask<T>,
    options: ProgressOptions
  ): Promise<T> {
    const { default: cliProgress } = await import('cli-progress');
    const progressBar = new cliProgress.SingleBar({
      format: `${options.title} |{bar}| {percentage}% | {status}`,
      barCompleteChar: '█',
      barIncompleteChar: '░',
      hideCursor: true
    });
    progressBar.start(100, 0, { status: 'Starting...' });
    try {
      const result = await task({
        updateProgress: (percent: number, status?: string) => {
          progressBar.update(percent, { status: status || 'Processing...' });
        }
      });
      progressBar.update(100, { status: 'Complete!' });
      progressBar.stop();
      return result;
    } catch (error) {
      progressBar.stop();
      throw error;
    }
  }
  async confirmWithDetails(
    message: string,
    details: ConfirmationDetails
  ): Promise<boolean> {
    console.log('\n' + chalk.bold(message));
    console.log(chalk.gray('Details:'));
    for (const [key, value] of Object.entries(details)) {
      console.log(chalk.gray(`  ${key}: ${value}`));
    }
    return this.confirm('\nProceed?');
  }
 }
 ```
 ## Enhanced Hooks Integration
 ### Deep CLI Hooks Integration
 ```typescript
 // src/cli/hooks/cli-hooks-manager.ts
 interface CLIHookEvent {
  type: 'command_start' | 'command_end' | 'command_error' | 'agent_spawn' | 'task_complete';
  command: string;
  args: string[];
  context: ExecutionContext;
  timestamp: Date;
 }
 export class CLIHooksManager {
  private hooks: Map<string, HookHandler[]> = new Map();
  private learningIntegration: LearningHooksIntegration;
  constructor() {
    this.learningIntegration = new LearningHooksIntegration();
    this.setupDefaultHooks();
  }
  private setupDefaultHooks(): void {
    // Learning integration hooks
    this.registerHook('command_start', async (event: CLIHookEvent) => {
      await this.learningIntegration.recordCommandStart(event);
    });
    this.registerHook('command_end', async (event: CLIHookEvent) => {
      await this.learningIntegration.recordCommandSuccess(event);
    });
    this.registerHook('command_error', async (event: CLIHookEvent) => {
      await this.learningIntegration.recordCommandError(event);
    });
    // Intelligent suggestions
    this.registerHook('command_start', async (event: CLIHookEvent) => {
      const suggestions = await this.generateIntelligentSuggestions(event);
      if (suggestions.length > 0) {
        this.displaySuggestions(suggestions);
      }
    });
    // Performance monitoring
    this.registerHook('command_end', async (event: CLIHookEvent) => {
      await this.recordPerformanceMetrics(event);
    });
  }
  async executeHooks(type: string, event: CLIHookEvent): Promise<void> {
    const handlers = this.hooks.get(type) || [];
    await Promise.all(handlers.map(handler =>
      this.executeHookSafely(handler, event)
    ));
  }
  private async generateIntelligentSuggestions(event: CLIHookEvent): Promise<Suggestion[]> {
    const context = await this.learningIntegration.getExecutionContext(event);
    const patterns = await this.learningIntegration.findSimilarPatterns(context);
    return patterns.map(pattern => ({
      type: 'optimization',
      message: `Based on similar executions, consider: ${pattern.suggestion}`,
      confidence: pattern.confidence
    }));
  }
 }
 ```
 ### Learning Integration
 ```typescript
 // src/cli/hooks/learning-hooks-integration.ts
 export class LearningHooksIntegration {
  constructor(
    private agenticFlowHooks: AgenticFlowHooksClient,
    private agentDBLearning: AgentDBLearningClient
  ) {}
  async recordCommandStart(event: CLIHookEvent): Promise<void> {
    // Start trajectory tracking
    await this.agenticFlowHooks.trajectoryStart({
      sessionId: event.context.sessionId,
      command: event.command,
      args: event.args,
      context: event.context
    });
    // Record experience in AgentDB
    await this.agentDBLearning.recordExperience({
      type: 'command_execution',
      state: this.encodeCommandState(event),
      action: event.command,
      timestamp: event.timestamp
    });
  }
  async recordCommandSuccess(event: CLIHookEvent): Promise<void> {
    const executionTime = Date.now() - event.timestamp.getTime();
    const reward = this.calculateReward(event, executionTime, true);
    // Complete trajectory
    await this.agenticFlowHooks.trajectoryEnd({
      sessionId: event.context.sessionId,
      success: true,
      reward,
      verdict: 'positive'
    });
    // Submit feedback to learning system
    await this.agentDBLearning.submitFeedback({
      sessionId: event.context.learningSessionId,
      reward,
      success: true,
      latencyMs: executionTime
    });
    // Store successful pattern
    if (reward > 0.8) {
      await this.agenticFlowHooks.storePattern({
        pattern: event.command,
        solution: event.context.result,
        confidence: reward
      });
    }
  }
  async recordCommandError(event: CLIHookEvent): Promise<void> {
    const executionTime = Date.now() - event.timestamp.getTime();
    const reward = this.calculateReward(event, executionTime, false);
    // Complete trajectory with error
    await this.agenticFlowHooks.trajectoryEnd({
      sessionId: event.context.sessionId,
      success: false,
      reward,
      verdict: 'negative',
      error: event.context.error
    });
    // Learn from failure
    await this.agentDBLearning.submitFeedback({
      sessionId: event.context.learningSessionId,
      reward,
      success: false,
      latencyMs: executionTime,
      error: event.context.error
    });
  }
  private calculateReward(event: CLIHookEvent, executionTime: number, success: boolean): number {
    if (!success) return 0;
    // Base reward for success
    let reward = 0.5;
    // Performance bonus (faster execution)
    const expectedTime = this.getExpectedExecutionTime(event.command);
    if (executionTime < expectedTime) {
      reward += 0.3 * (1 - executionTime / expectedTime);
    }
    // Complexity bonus
    const complexity = this.calculateCommandComplexity(event);
    reward += complexity * 0.2;
    return Math.min(reward, 1.0);
  }
 }
 ```
 ## Intelligent Workflow Automation
 ### Workflow Orchestrator
 ```typescript
 // src/cli/workflows/workflow-orchestrator.ts
 interface WorkflowStep {
  id: string;
  command: string;
  args: string[];
  dependsOn: string[];
  condition?: WorkflowCondition;
  retryPolicy?: RetryPolicy;
 }
 export class WorkflowOrchestrator {
  constructor(
    private commandRegistry: ModularCommandRegistry,
    private promptService: InteractivePromptService
  ) {}
  async executeWorkflow(workflow: Workflow): Promise<WorkflowResult> {
    const context = new WorkflowExecutionContext(workflow);
    // Display workflow overview
    await this.displayWorkflowOverview(workflow);
    const confirmed = await this.promptService.confirm(
      'Execute this workflow?'
    );
    if (!confirmed) {
      return WorkflowResult.cancelled();
    }
    // Execute steps
    return this.promptService.progressTask(
      async ({ updateProgress }) => {
        const steps = this.sortStepsByDependencies(workflow.steps);
        for (let i = 0; i < steps.length; i++) {
          const step = steps[i];
          updateProgress((i / steps.length) * 100, `Executing ${step.command}`);
          await this.executeStep(step, context);
        }
        return WorkflowResult.success(context.getResults());
      },
      { title: `Workflow: ${workflow.name}` }
    );
  }
  async generateWorkflowFromIntent(intent: string): Promise<Workflow> {
    // Use learning system to generate workflow
    const patterns = await this.findWorkflowPatterns(intent);
    if (patterns.length === 0) {
      throw new Error('Could not generate workflow for intent');
    }
    // Select best pattern or let user choose
    const selectedPattern = patterns.length === 1
      ? patterns[0]
      : await this.promptService.select({
          message: 'Select workflow template:',
          choices: patterns.map(p => ({
            name: `${p.name} (${p.confidence}% match)`,
            value: p
          }))
        });
    return this.customizeWorkflow(selectedPattern, intent);
  }
  private async executeStep(step: WorkflowStep, context: WorkflowExecutionContext): Promise<void> {
    // Check conditions
    if (step.condition && !this.evaluateCondition(step.condition, context)) {
      context.skipStep(step.id, 'Condition not met');
      return;
    }
    // Check dependencies
    const missingDeps = step.dependsOn.filter(dep => !context.isStepCompleted(dep));
    if (missingDeps.length > 0) {
      throw new WorkflowError(`Step ${step.id} has unmet dependencies: ${missingDeps.join(', ')}`);
    }
    // Execute with retry policy
    const retryPolicy = step.retryPolicy || { maxAttempts: 1 };
    let lastError: Error | null = null;
    for (let attempt = 1; attempt <= retryPolicy.maxAttempts; attempt++) {
      try {
        const result = await this.commandRegistry.executeCommand(step.command, step.args);
        context.completeStep(step.id, result);
        return;
      } catch (error) {
        lastError = error as Error;
        if (attempt < retryPolicy.maxAttempts) {
          await this.delay(retryPolicy.backoffMs || 1000);
        }
      }
    }
    throw new WorkflowError(`Step ${step.id} failed after ${retryPolicy.maxAttempts} attempts: ${lastError?.message}`);
  }
 }
 ```
 ## Performance Optimization
 ### Command Performance Monitoring
 ```typescript
 // src/cli/performance/command-performance.ts
 export class CommandPerformanceMonitor {
  private metrics = new Map<string, CommandMetrics>();
  async measureCommand<T>(
    commandName: string,
    executor: () => Promise<T>
  ): Promise<T> {
    const start = performance.now();
    const memBefore = process.memoryUsage();
    try {
      const result = await executor();
      const end = performance.now();
      const memAfter = process.memoryUsage();
      this.recordMetrics(commandName, {
        executionTime: end - start,
        memoryDelta: memAfter.heapUsed - memBefore.heapUsed,
        success: true
      });
      return result;
    } catch (error) {
      const end = performance.now();
      this.recordMetrics(commandName, {
        executionTime: end - start,
        memoryDelta: 0,
        success: false,
        error: error as Error
      });
      throw error;
    }
  }
  private recordMetrics(command: string, measurement: PerformanceMeasurement): void {
    if (!this.metrics.has(command)) {
      this.metrics.set(command, new CommandMetrics(command));
    }
    const metrics = this.metrics.get(command)!;
    metrics.addMeasurement(measurement);
    // Alert if performance degrades
    if (metrics.getP95ExecutionTime() > 5000) { // 5 seconds
      console.warn(`⚠️  Command '${command}' is performing slowly (P95: ${metrics.getP95ExecutionTime()}ms)`);
    }
  }
  getCommandReport(command: string): PerformanceReport {
    const metrics = this.metrics.get(command);
    if (!metrics) {
      throw new Error(`No metrics found for command: ${command}`);
    }
    return {
      command,
      totalExecutions: metrics.getTotalExecutions(),
      successRate: metrics.getSuccessRate(),
      avgExecutionTime: metrics.getAverageExecutionTime(),
      p95ExecutionTime: metrics.getP95ExecutionTime(),
      avgMemoryUsage: metrics.getAverageMemoryUsage(),
      recommendations: this.generateRecommendations(metrics)
    };
  }
 }
 ```
 ## Smart Auto-completion
 ### Intelligent Command Completion
 ```typescript
 // src/cli/completion/intelligent-completion.ts
 export class IntelligentCompletion {
  constructor(
    private learningService: LearningService,
    private commandRegistry: ModularCommandRegistry
  ) {}
  async generateCompletions(
    partial: string,
    context: CompletionContext
  ): Promise<Completion[]> {
    const completions: Completion[] = [];
    // 1. Exact command matches
    const exactMatches = this.commandRegistry.findCommandsByPrefix(partial);
    completions.push(...exactMatches.map(cmd => ({
      value: cmd.name,
      description: cmd.description,
      type: 'command',
      confidence: 1.0
    })));
    // 2. Learning-based suggestions
    const learnedSuggestions = await this.learningService.suggestCommands(
      partial,
      context
    );
    completions.push(...learnedSuggestions);
    // 3. Context-aware suggestions
    const contextualSuggestions = await this.generateContextualSuggestions(
      partial,
      context
    );
    completions.push(...contextualSuggestions);
    // Sort by confidence and relevance
    return completions
      .sort((a, b) => b.confidence - a.confidence)
      .slice(0, 10); // Top 10 suggestions
  }
  private async generateContextualSuggestions(
    partial: string,
    context: CompletionContext
  ): Promise<Completion[]> {
    const suggestions: Completion[] = [];
    // If in git repository, suggest git-related commands
    if (context.isGitRepository) {
      if (partial.startsWith('git')) {
        suggestions.push({
          value: 'git commit',
          description: 'Create git commit with generated message',
          type: 'workflow',
          confidence: 0.8
        });
      }
    }
    // If package.json exists, suggest npm commands
    if (context.hasPackageJson) {
      if (partial.startsWith('npm') || partial.startsWith('swarm')) {
        suggestions.push({
          value: 'swarm init',
          description: 'Initialize swarm for this project',
          type: 'workflow',
          confidence: 0.9
        });
      }
    }
    return suggestions;
  }
 }
 ```
 ## Success Metrics
 ### CLI Performance Targets
 - [ ] **Command Response**: <200ms average command execution time
 - [ ] **File Decomposition**: index.ts (108KB) → <10KB per command module
 - [ ] **Interactive UX**: Smart prompts with context awareness
 - [ ] **Hook Integration**: Deep lifecycle integration with learning
 - [ ] **Workflow Automation**: Intelligent multi-step command orchestration
 - [ ] **Auto-completion**: >90% accuracy for command suggestions
 ### User Experience Improvements
 ```typescript
 const cliImprovements = {
  before: {
    commandResponse: '~500ms',
    interactivity: 'Basic command parsing',
    workflows: 'Manual command chaining',
    suggestions: 'Static help text'
  },
  after: {
    commandResponse: '<200ms with caching',
    interactivity: 'Smart context-aware prompts',
    workflows: 'Automated multi-step execution',
    suggestions: 'Learning-based intelligent completion'
  }
 };
 ```
 ## Related V3 Skills
 - `v3-core-implementation` - Core domain integration
 - `v3-memory-unification` - Memory-backed command caching
 - `v3-swarm-coordination` - CLI swarm management integration
 - `v3-performance-optimization` - CLI performance monitoring
 ## Usage Examples
 ### Complete CLI Modernization
 ```bash
 # Full CLI modernization implementation
 Task("CLI modernization implementation",
     "Implement modular commands, interactive prompts, and intelligent workflows",
     "cli-hooks-developer")
 ```
 ### Interactive Command Enhancement
 ```bash
 # Enhanced interactive commands
 Codex-flow swarm init --interactive
 Codex-flow learning start --guided
 Codex-flow workflow create --from-intent "setup new project"
 ```
@@ -1,797 +0,0 @@
 ---
 name: "V3 Core Implementation"
 description: "Core module implementation for Codex-flow v3. Implements DDD domains, clean architecture patterns, dependency injection, and modular TypeScript codebase with comprehensive testing."
 ---
 # V3 Core Implementation
 ## What This Skill Does
 Implements the core TypeScript modules for Codex-flow v3 following Domain-Driven Design principles, clean architecture patterns, and modern TypeScript best practices with comprehensive test coverage.
 ## Quick Start
 ```bash
 # Initialize core implementation
 Task("Core foundation", "Set up DDD domain structure and base classes", "core-implementer")
 # Domain implementation (parallel)
 Task("Task domain", "Implement task management domain with entities and services", "core-implementer")
 Task("Session domain", "Implement session management domain", "core-implementer")
 Task("Health domain", "Implement health monitoring domain", "core-implementer")
 ```
 ## Core Implementation Architecture
 ### Domain Structure
 ```
 src/
 ├── core/
 │   ├── kernel/                     # Microkernel pattern
 │   │   ├── Codex-flow-kernel.ts
 │   │   ├── domain-registry.ts
 │   │   └── plugin-loader.ts
 │   │
 │   ├── domains/                    # DDD Bounded Contexts
 │   │   ├── task-management/
 │   │   │   ├── entities/
 │   │   │   ├── value-objects/
 │   │   │   ├── services/
 │   │   │   ├── repositories/
 │   │   │   └── events/
 │   │   │
 │   │   ├── session-management/
 │   │   ├── health-monitoring/
 │   │   ├── lifecycle-management/
 │   │   └── event-coordination/
 │   │
 │   ├── shared/                     # Shared kernel
 │   │   ├── domain/
 │   │   │   ├── entity.ts
 │   │   │   ├── value-object.ts
 │   │   │   ├── domain-event.ts
 │   │   │   └── aggregate-root.ts
 │   │   │
 │   │   ├── infrastructure/
 │   │   │   ├── event-bus.ts
 │   │   │   ├── dependency-container.ts
 │   │   │   └── logger.ts
 │   │   │
 │   │   └── types/
 │   │       ├── common.ts
 │   │       ├── errors.ts
 │   │       └── interfaces.ts
 │   │
 │   └── application/                # Application services
 │       ├── use-cases/
 │       ├── commands/
 │       ├── queries/
 │       └── handlers/
 ```
 ## Base Domain Classes
 ### Entity Base Class
 ```typescript
 // src/core/shared/domain/entity.ts
 export abstract class Entity<T> {
  protected readonly _id: T;
  private _domainEvents: DomainEvent[] = [];
  constructor(id: T) {
    this._id = id;
  }
  get id(): T {
    return this._id;
  }
  public equals(object?: Entity<T>): boolean {
    if (object == null || object == undefined) {
      return false;
    }
    if (this === object) {
      return true;
    }
    if (!(object instanceof Entity)) {
      return false;
    }
    return this._id === object._id;
  }
  protected addDomainEvent(domainEvent: DomainEvent): void {
    this._domainEvents.push(domainEvent);
  }
  public getUncommittedEvents(): DomainEvent[] {
    return this._domainEvents;
  }
  public markEventsAsCommitted(): void {
    this._domainEvents = [];
  }
 }
 ```
 ### Value Object Base Class
 ```typescript
 // src/core/shared/domain/value-object.ts
 export abstract class ValueObject<T> {
  protected readonly props: T;
  constructor(props: T) {
    this.props = Object.freeze(props);
  }
  public equals(object?: ValueObject<T>): boolean {
    if (object == null || object == undefined) {
      return false;
    }
    if (this === object) {
      return true;
    }
    return JSON.stringify(this.props) === JSON.stringify(object.props);
  }
  get value(): T {
    return this.props;
  }
 }
 ```
 ### Aggregate Root
 ```typescript
 // src/core/shared/domain/aggregate-root.ts
 export abstract class AggregateRoot<T> extends Entity<T> {
  private _version: number = 0;
  get version(): number {
    return this._version;
  }
  protected incrementVersion(): void {
    this._version++;
  }
  public applyEvent(event: DomainEvent): void {
    this.addDomainEvent(event);
    this.incrementVersion();
  }
 }
 ```
 ## Task Management Domain Implementation
 ### Task Entity
 ```typescript
 // src/core/domains/task-management/entities/task.entity.ts
 import { AggregateRoot } from '../../../shared/domain/aggregate-root';
 import { TaskId } from '../value-objects/task-id.vo';
 import { TaskStatus } from '../value-objects/task-status.vo';
 import { Priority } from '../value-objects/priority.vo';
 import { TaskAssignedEvent } from '../events/task-assigned.event';
 interface TaskProps {
  id: TaskId;
  description: string;
  priority: Priority;
  status: TaskStatus;
  assignedAgentId?: string;
  createdAt: Date;
  updatedAt: Date;
 }
 export class Task extends AggregateRoot<TaskId> {
  private props: TaskProps;
  private constructor(props: TaskProps) {
    super(props.id);
    this.props = props;
  }
  static create(description: string, priority: Priority): Task {
    const task = new Task({
      id: TaskId.create(),
      description,
      priority,
      status: TaskStatus.pending(),
      createdAt: new Date(),
      updatedAt: new Date()
    });
    return task;
  }
  static reconstitute(props: TaskProps): Task {
    return new Task(props);
  }
  public assignTo(agentId: string): void {
    if (this.props.status.equals(TaskStatus.completed())) {
      throw new Error('Cannot assign completed task');
    }
    this.props.assignedAgentId = agentId;
    this.props.status = TaskStatus.assigned();
    this.props.updatedAt = new Date();
    this.applyEvent(new TaskAssignedEvent(
      this.id.value,
      agentId,
      this.props.priority
    ));
  }
  public complete(result: TaskResult): void {
    if (!this.props.assignedAgentId) {
      throw new Error('Cannot complete unassigned task');
    }
    this.props.status = TaskStatus.completed();
    this.props.updatedAt = new Date();
    this.applyEvent(new TaskCompletedEvent(
      this.id.value,
      result,
      this.calculateDuration()
    ));
  }
  // Getters
  get description(): string { return this.props.description; }
  get priority(): Priority { return this.props.priority; }
  get status(): TaskStatus { return this.props.status; }
  get assignedAgentId(): string | undefined { return this.props.assignedAgentId; }
  get createdAt(): Date { return this.props.createdAt; }
  get updatedAt(): Date { return this.props.updatedAt; }
  private calculateDuration(): number {
    return this.props.updatedAt.getTime() - this.props.createdAt.getTime();
  }
 }
 ```
 ### Task Value Objects
 ```typescript
 // src/core/domains/task-management/value-objects/task-id.vo.ts
 export class TaskId extends ValueObject<string> {
  private constructor(value: string) {
    super({ value });
  }
  static create(): TaskId {
    return new TaskId(crypto.randomUUID());
  }
  static fromString(id: string): TaskId {
    if (!id || id.length === 0) {
      throw new Error('TaskId cannot be empty');
    }
    return new TaskId(id);
  }
  get value(): string {
    return this.props.value;
  }
 }
 // src/core/domains/task-management/value-objects/task-status.vo.ts
 type TaskStatusType = 'pending' | 'assigned' | 'in_progress' | 'completed' | 'failed';
 export class TaskStatus extends ValueObject<TaskStatusType> {
  private constructor(status: TaskStatusType) {
    super({ value: status });
  }
  static pending(): TaskStatus { return new TaskStatus('pending'); }
  static assigned(): TaskStatus { return new TaskStatus('assigned'); }
  static inProgress(): TaskStatus { return new TaskStatus('in_progress'); }
  static completed(): TaskStatus { return new TaskStatus('completed'); }
  static failed(): TaskStatus { return new TaskStatus('failed'); }
  get value(): TaskStatusType {
    return this.props.value;
  }
  public isPending(): boolean { return this.value === 'pending'; }
  public isAssigned(): boolean { return this.value === 'assigned'; }
  public isInProgress(): boolean { return this.value === 'in_progress'; }
  public isCompleted(): boolean { return this.value === 'completed'; }
  public isFailed(): boolean { return this.value === 'failed'; }
 }
 // src/core/domains/task-management/value-objects/priority.vo.ts
 type PriorityLevel = 'low' | 'medium' | 'high' | 'critical';
 export class Priority extends ValueObject<PriorityLevel> {
  private constructor(level: PriorityLevel) {
    super({ value: level });
  }
  static low(): Priority { return new Priority('low'); }
  static medium(): Priority { return new Priority('medium'); }
  static high(): Priority { return new Priority('high'); }
  static critical(): Priority { return new Priority('critical'); }
  get value(): PriorityLevel {
    return this.props.value;
  }
  public getNumericValue(): number {
    const priorities = { low: 1, medium: 2, high: 3, critical: 4 };
    return priorities[this.value];
  }
 }
 ```
 ## Domain Services
 ### Task Scheduling Service
 ```typescript
 // src/core/domains/task-management/services/task-scheduling.service.ts
 import { Injectable } from '../../../shared/infrastructure/dependency-container';
 import { Task } from '../entities/task.entity';
 import { Priority } from '../value-objects/priority.vo';
@Injectable()
 export class TaskSchedulingService {
  public prioritizeTasks(tasks: Task[]): Task[] {
    return tasks.sort((a, b) =>
      b.priority.getNumericValue() - a.priority.getNumericValue()
    );
  }
  public canSchedule(task: Task, agentCapacity: number): boolean {
    if (agentCapacity <= 0) return false;
    // Critical tasks always schedulable
    if (task.priority.equals(Priority.critical())) return true;
    // Other logic based on capacity
    return true;
  }
  public calculateEstimatedDuration(task: Task): number {
    // Simple heuristic - would use ML in real implementation
    const baseTime = 300000; // 5 minutes
    const priorityMultiplier = {
      low: 0.5,
      medium: 1.0,
      high: 1.5,
      critical: 2.0
    };
    return baseTime * priorityMultiplier[task.priority.value];
  }
 }
 ```
 ## Repository Interfaces & Implementations
 ### Task Repository Interface
 ```typescript
 // src/core/domains/task-management/repositories/task.repository.ts
 export interface ITaskRepository {
  save(task: Task): Promise<void>;
  findById(id: TaskId): Promise<Task | null>;
  findByAgentId(agentId: string): Promise<Task[]>;
  findByStatus(status: TaskStatus): Promise<Task[]>;
  findPendingTasks(): Promise<Task[]>;
  delete(id: TaskId): Promise<void>;
 }
 ```
 ### SQLite Implementation
 ```typescript
 // src/core/domains/task-management/repositories/sqlite-task.repository.ts
@Injectable()
 export class SqliteTaskRepository implements ITaskRepository {
  constructor(
    @Inject('Database') private db: Database,
    @Inject('Logger') private logger: ILogger
  ) {}
  async save(task: Task): Promise<void> {
    const sql = `
      INSERT OR REPLACE INTO tasks (
        id, description, priority, status, assigned_agent_id, created_at, updated_at
      ) VALUES (?, ?, ?, ?, ?, ?, ?)
    `;
    await this.db.run(sql, [
      task.id.value,
      task.description,
      task.priority.value,
      task.status.value,
      task.assignedAgentId,
      task.createdAt.toISOString(),
      task.updatedAt.toISOString()
    ]);
    this.logger.debug(`Task saved: ${task.id.value}`);
  }
  async findById(id: TaskId): Promise<Task | null> {
    const sql = 'SELECT * FROM tasks WHERE id = ?';
    const row = await this.db.get(sql, [id.value]);
    return row ? this.mapRowToTask(row) : null;
  }
  async findPendingTasks(): Promise<Task[]> {
    const sql = 'SELECT * FROM tasks WHERE status = ? ORDER BY priority DESC, created_at ASC';
    const rows = await this.db.all(sql, ['pending']);
    return rows.map(row => this.mapRowToTask(row));
  }
  private mapRowToTask(row: any): Task {
    return Task.reconstitute({
      id: TaskId.fromString(row.id),
      description: row.description,
      priority: Priority.fromString(row.priority),
      status: TaskStatus.fromString(row.status),
      assignedAgentId: row.assigned_agent_id,
      createdAt: new Date(row.created_at),
      updatedAt: new Date(row.updated_at)
    });
  }
 }
 ```
 ## Application Layer
 ### Use Case Implementation
 ```typescript
 // src/core/application/use-cases/assign-task.use-case.ts
@Injectable()
 export class AssignTaskUseCase {
  constructor(
    @Inject('TaskRepository') private taskRepository: ITaskRepository,
    @Inject('AgentRepository') private agentRepository: IAgentRepository,
    @Inject('DomainEventBus') private eventBus: DomainEventBus,
    @Inject('Logger') private logger: ILogger
  ) {}
  async execute(command: AssignTaskCommand): Promise<AssignTaskResult> {
    try {
      // 1. Validate command
      await this.validateCommand(command);
      // 2. Load aggregates
      const task = await this.taskRepository.findById(command.taskId);
      if (!task) {
        throw new TaskNotFoundError(command.taskId);
      }
      const agent = await this.agentRepository.findById(command.agentId);
      if (!agent) {
        throw new AgentNotFoundError(command.agentId);
      }
      // 3. Business logic
      if (!agent.canAcceptTask(task)) {
        throw new AgentCannotAcceptTaskError(command.agentId, command.taskId);
      }
      task.assignTo(command.agentId);
      agent.acceptTask(task.id);
      // 4. Persist changes
      await Promise.all([
        this.taskRepository.save(task),
        this.agentRepository.save(agent)
      ]);
      // 5. Publish domain events
      const events = [
        ...task.getUncommittedEvents(),
        ...agent.getUncommittedEvents()
      ];
      for (const event of events) {
        await this.eventBus.publish(event);
      }
      task.markEventsAsCommitted();
      agent.markEventsAsCommitted();
      // 6. Return result
      this.logger.info(`Task ${command.taskId.value} assigned to agent ${command.agentId}`);
      return AssignTaskResult.success({
        taskId: task.id,
        agentId: command.agentId,
        assignedAt: new Date()
      });
    } catch (error) {
      this.logger.error(`Failed to assign task ${command.taskId.value}:`, error);
      return AssignTaskResult.failure(error);
    }
  }
  private async validateCommand(command: AssignTaskCommand): Promise<void> {
    if (!command.taskId) {
      throw new ValidationError('Task ID is required');
    }
    if (!command.agentId) {
      throw new ValidationError('Agent ID is required');
    }
  }
 }
 ```
 ## Dependency Injection Setup
 ### Container Configuration
 ```typescript
 // src/core/shared/infrastructure/dependency-container.ts
 import { Container } from 'inversify';
 import { TYPES } from './types';
 export class DependencyContainer {
  private container: Container;
  constructor() {
    this.container = new Container();
    this.setupBindings();
  }
  private setupBindings(): void {
    // Repositories
    this.container.bind<ITaskRepository>(TYPES.TaskRepository)
      .to(SqliteTaskRepository)
      .inSingletonScope();
    this.container.bind<IAgentRepository>(TYPES.AgentRepository)
      .to(SqliteAgentRepository)
      .inSingletonScope();
    // Services
    this.container.bind<TaskSchedulingService>(TYPES.TaskSchedulingService)
      .to(TaskSchedulingService)
      .inSingletonScope();
    // Use Cases
    this.container.bind<AssignTaskUseCase>(TYPES.AssignTaskUseCase)
      .to(AssignTaskUseCase)
      .inSingletonScope();
    // Infrastructure
    this.container.bind<ILogger>(TYPES.Logger)
      .to(ConsoleLogger)
      .inSingletonScope();
    this.container.bind<DomainEventBus>(TYPES.DomainEventBus)
      .to(InMemoryDomainEventBus)
      .inSingletonScope();
  }
  get<T>(serviceIdentifier: symbol): T {
    return this.container.get<T>(serviceIdentifier);
  }
  bind<T>(serviceIdentifier: symbol): BindingToSyntax<T> {
    return this.container.bind<T>(serviceIdentifier);
  }
 }
 ```
 ## Modern TypeScript Configuration
 ### Strict TypeScript Setup
 ```json
 // tsconfig.json
 {
  "compilerOptions": {
    "target": "ES2022",
    "lib": ["ES2022"],
    "module": "NodeNext",
    "moduleResolution": "NodeNext",
    "declaration": true,
    "outDir": "./dist",
    "strict": true,
    "exactOptionalPropertyTypes": true,
    "noImplicitReturns": true,
    "noFallthroughCasesInSwitch": true,
    "noUncheckedIndexedAccess": true,
    "noImplicitOverride": true,
    "experimentalDecorators": true,
    "emitDecoratorMetadata": true,
    "skipLibCheck": true,
    "forceConsistentCasingInFileNames": true,
    "resolveJsonModule": true,
    "esModuleInterop": true,
    "allowSyntheticDefaultImports": true,
    "baseUrl": ".",
    "paths": {
      "@/*": ["src/*"],
      "@core/*": ["src/core/*"],
      "@shared/*": ["src/core/shared/*"],
      "@domains/*": ["src/core/domains/*"]
    }
  },
  "include": ["src/**/*"],
  "exclude": ["node_modules", "dist", "**/*.test.ts", "**/*.spec.ts"]
 }
 ```
 ## Testing Implementation
 ### Domain Unit Tests
 ```typescript
 // src/core/domains/task-management/__tests__/entities/task.entity.test.ts
 describe('Task Entity', () => {
  let task: Task;
  beforeEach(() => {
    task = Task.create('Test task', Priority.medium());
  });
  describe('creation', () => {
    it('should create task with pending status', () => {
      expect(task.status.isPending()).toBe(true);
      expect(task.description).toBe('Test task');
      expect(task.priority.equals(Priority.medium())).toBe(true);
    });
    it('should generate unique ID', () => {
      const task1 = Task.create('Task 1', Priority.low());
      const task2 = Task.create('Task 2', Priority.low());
      expect(task1.id.equals(task2.id)).toBe(false);
    });
  });
  describe('assignment', () => {
    it('should assign to agent and change status', () => {
      const agentId = 'agent-123';
      task.assignTo(agentId);
      expect(task.assignedAgentId).toBe(agentId);
      expect(task.status.isAssigned()).toBe(true);
    });
    it('should emit TaskAssignedEvent when assigned', () => {
      const agentId = 'agent-123';
      task.assignTo(agentId);
      const events = task.getUncommittedEvents();
      expect(events).toHaveLength(1);
      expect(events[0]).toBeInstanceOf(TaskAssignedEvent);
    });
    it('should not allow assignment of completed task', () => {
      task.assignTo('agent-123');
      task.complete(TaskResult.success('done'));
      expect(() => task.assignTo('agent-456'))
        .toThrow('Cannot assign completed task');
    });
  });
 });
 ```
 ### Integration Tests
 ```typescript
 // src/core/domains/task-management/__tests__/integration/task-repository.integration.test.ts
 describe('TaskRepository Integration', () => {
  let repository: SqliteTaskRepository;
  let db: Database;
  beforeEach(async () => {
    db = new Database(':memory:');
    await setupTasksTable(db);
    repository = new SqliteTaskRepository(db, new ConsoleLogger());
  });
  afterEach(async () => {
    await db.close();
  });
  it('should save and retrieve task', async () => {
    const task = Task.create('Test task', Priority.high());
    await repository.save(task);
    const retrieved = await repository.findById(task.id);
    expect(retrieved).toBeDefined();
    expect(retrieved!.id.equals(task.id)).toBe(true);
    expect(retrieved!.description).toBe('Test task');
    expect(retrieved!.priority.equals(Priority.high())).toBe(true);
  });
  it('should find pending tasks ordered by priority', async () => {
    const lowTask = Task.create('Low priority', Priority.low());
    const highTask = Task.create('High priority', Priority.high());
    await repository.save(lowTask);
    await repository.save(highTask);
    const pending = await repository.findPendingTasks();
    expect(pending).toHaveLength(2);
    expect(pending[0].id.equals(highTask.id)).toBe(true); // High priority first
    expect(pending[1].id.equals(lowTask.id)).toBe(true);
  });
 });
 ```
 ## Performance Optimizations
 ### Entity Caching
 ```typescript
 // src/core/shared/infrastructure/entity-cache.ts
@Injectable()
 export class EntityCache<T extends Entity<any>> {
  private cache = new Map<string, { entity: T; timestamp: number }>();
  private readonly ttl: number = 300000; // 5 minutes
  set(id: string, entity: T): void {
    this.cache.set(id, { entity, timestamp: Date.now() });
  }
  get(id: string): T | null {
    const cached = this.cache.get(id);
    if (!cached) return null;
    // Check TTL
    if (Date.now() - cached.timestamp > this.ttl) {
      this.cache.delete(id);
      return null;
    }
    return cached.entity;
  }
  invalidate(id: string): void {
    this.cache.delete(id);
  }
  clear(): void {
    this.cache.clear();
  }
 }
 ```
 ## Success Metrics
 - [ ] **Domain Isolation**: 100% clean dependency boundaries
 - [ ] **Test Coverage**: >90% unit test coverage for domain logic
 - [ ] **Type Safety**: Strict TypeScript compilation with zero any types
 - [ ] **Performance**: <50ms average use case execution time
 - [ ] **Memory Efficiency**: <100MB heap usage for core domains
 - [ ] **Plugin Architecture**: Modular domain loading capability
 ## Related V3 Skills
 - `v3-ddd-architecture` - DDD architectural design
 - `v3-mcp-optimization` - MCP server integration
 - `v3-memory-unification` - AgentDB repository integration
 - `v3-swarm-coordination` - Swarm domain implementation
 ## Usage Examples
 ### Complete Core Implementation
 ```bash
 # Full core module implementation
 Task("Core implementation",
     "Implement all core domains with DDD patterns and comprehensive testing",
     "core-implementer")
 ```
 ### Domain-Specific Implementation
 ```bash
 # Single domain implementation
 Task("Task domain implementation",
     "Implement task management domain with entities, services, and repositories",
     "core-implementer")
 ```
@@ -1,442 +0,0 @@
 ---
 name: "V3 DDD Architecture"
 description: "Domain-Driven Design architecture for Codex-flow v3. Implements modular, bounded context architecture with clean separation of concerns and microkernel pattern."
 ---
 # V3 DDD Architecture
 ## What This Skill Does
 Designs and implements Domain-Driven Design (DDD) architecture for Codex-flow v3, decomposing god objects into bounded contexts, implementing clean architecture patterns, and enabling modular, testable code structure.
 ## Quick Start
 ```bash
 # Initialize DDD architecture analysis
 Task("Architecture analysis", "Analyze current architecture and design DDD boundaries", "core-architect")
 # Domain modeling (parallel)
 Task("Domain decomposition", "Break down orchestrator god object into domains", "core-architect")
 Task("Context mapping", "Map bounded contexts and relationships", "core-architect")
 Task("Interface design", "Design clean domain interfaces", "core-architect")
 ```
 ## DDD Implementation Strategy
 ### Current Architecture Analysis
 ```
 ├── PROBLEMATIC: core/orchestrator.ts (1,440 lines - GOD OBJECT)
 │   ├── Task management responsibilities
 │   ├── Session management responsibilities
 │   ├── Health monitoring responsibilities
 │   ├── Lifecycle management responsibilities
 │   └── Event coordination responsibilities
 │
 └── TARGET: Modular DDD Architecture
    ├── core/domains/
    │   ├── task-management/
    │   ├── session-management/
    │   ├── health-monitoring/
    │   ├── lifecycle-management/
    │   └── event-coordination/
    └── core/shared/
        ├── interfaces/
        ├── value-objects/
        └── domain-events/
 ```
 ### Domain Boundaries
 #### 1. Task Management Domain
 ```typescript
 // core/domains/task-management/
 interface TaskManagementDomain {
  // Entities
  Task: TaskEntity;
  TaskQueue: TaskQueueEntity;
  // Value Objects
  TaskId: TaskIdVO;
  TaskStatus: TaskStatusVO;
  Priority: PriorityVO;
  // Services
  TaskScheduler: TaskSchedulingService;
  TaskValidator: TaskValidationService;
  // Repository
  TaskRepository: ITaskRepository;
 }
 ```
 #### 2. Session Management Domain
 ```typescript
 // core/domains/session-management/
 interface SessionManagementDomain {
  // Entities
  Session: SessionEntity;
  SessionState: SessionStateEntity;
  // Value Objects
  SessionId: SessionIdVO;
  SessionStatus: SessionStatusVO;
  // Services
  SessionLifecycle: SessionLifecycleService;
  SessionPersistence: SessionPersistenceService;
  // Repository
  SessionRepository: ISessionRepository;
 }
 ```
 #### 3. Health Monitoring Domain
 ```typescript
 // core/domains/health-monitoring/
 interface HealthMonitoringDomain {
  // Entities
  HealthCheck: HealthCheckEntity;
  Metric: MetricEntity;
  // Value Objects
  HealthStatus: HealthStatusVO;
  Threshold: ThresholdVO;
  // Services
  HealthCollector: HealthCollectionService;
  AlertManager: AlertManagementService;
  // Repository
  MetricsRepository: IMetricsRepository;
 }
 ```
 ## Microkernel Architecture Pattern
 ### Core Kernel
 ```typescript
 // core/kernel/Codex-flow-kernel.ts
 export class ClaudeFlowKernel {
  private domains: Map<string, Domain> = new Map();
  private eventBus: DomainEventBus;
  private dependencyContainer: Container;
  async initialize(): Promise<void> {
    // Load core domains
    await this.loadDomain('task-management', new TaskManagementDomain());
    await this.loadDomain('session-management', new SessionManagementDomain());
    await this.loadDomain('health-monitoring', new HealthMonitoringDomain());
    // Wire up domain events
    this.setupDomainEventHandlers();
  }
  async loadDomain(name: string, domain: Domain): Promise<void> {
    await domain.initialize(this.dependencyContainer);
    this.domains.set(name, domain);
  }
  getDomain<T extends Domain>(name: string): T {
    const domain = this.domains.get(name);
    if (!domain) {
      throw new DomainNotLoadedError(name);
    }
    return domain as T;
  }
 }
 ```
 ### Plugin Architecture
 ```typescript
 // core/plugins/
 interface DomainPlugin {
  name: string;
  version: string;
  dependencies: string[];
  initialize(kernel: ClaudeFlowKernel): Promise<void>;
  shutdown(): Promise<void>;
 }
 // Example: Swarm Coordination Plugin
 export class SwarmCoordinationPlugin implements DomainPlugin {
  name = 'swarm-coordination';
  version = '3.0.0';
  dependencies = ['task-management', 'session-management'];
  async initialize(kernel: ClaudeFlowKernel): Promise<void> {
    const taskDomain = kernel.getDomain<TaskManagementDomain>('task-management');
    const sessionDomain = kernel.getDomain<SessionManagementDomain>('session-management');
    // Register swarm coordination services
    this.swarmCoordinator = new UnifiedSwarmCoordinator(taskDomain, sessionDomain);
    kernel.registerService('swarm-coordinator', this.swarmCoordinator);
  }
 }
 ```
 ## Domain Events & Integration
 ### Event-Driven Communication
 ```typescript
 // core/shared/domain-events/
 abstract class DomainEvent {
  public readonly eventId: string;
  public readonly aggregateId: string;
  public readonly occurredOn: Date;
  public readonly eventVersion: number;
  constructor(aggregateId: string) {
    this.eventId = crypto.randomUUID();
    this.aggregateId = aggregateId;
    this.occurredOn = new Date();
    this.eventVersion = 1;
  }
 }
 // Task domain events
 export class TaskAssignedEvent extends DomainEvent {
  constructor(
    taskId: string,
    public readonly agentId: string,
    public readonly priority: Priority
  ) {
    super(taskId);
  }
 }
 export class TaskCompletedEvent extends DomainEvent {
  constructor(
    taskId: string,
    public readonly result: TaskResult,
    public readonly duration: number
  ) {
    super(taskId);
  }
 }
 // Event handlers
@EventHandler(TaskCompletedEvent)
 export class TaskCompletedHandler {
  constructor(
    private metricsRepository: IMetricsRepository,
    private sessionService: SessionLifecycleService
  ) {}
  async handle(event: TaskCompletedEvent): Promise<void> {
    // Update metrics
    await this.metricsRepository.recordTaskCompletion(
      event.aggregateId,
      event.duration
    );
    // Update session state
    await this.sessionService.markTaskCompleted(
      event.aggregateId,
      event.result
    );
  }
 }
 ```
 ## Clean Architecture Layers
 ```typescript
 // Architecture layers
 ┌─────────────────────────────────────────┐
 │              Presentation               │  ← CLI, API, UI
 ├─────────────────────────────────────────┤
 │              Application                │  ← Use Cases, Commands
 ├─────────────────────────────────────────┤
 │               Domain                    │  ← Entities, Services, Events
 ├─────────────────────────────────────────┤
 │            Infrastructure               │  ← DB, MCP, External APIs
 └─────────────────────────────────────────┘
 // Dependency direction: Outside → Inside
 // Domain layer has NO external dependencies
 ```
 ### Application Layer (Use Cases)
 ```typescript
 // core/application/use-cases/
 export class AssignTaskUseCase {
  constructor(
    private taskRepository: ITaskRepository,
    private agentRepository: IAgentRepository,
    private eventBus: DomainEventBus
  ) {}
  async execute(command: AssignTaskCommand): Promise<TaskResult> {
    // 1. Validate command
    await this.validateCommand(command);
    // 2. Load aggregates
    const task = await this.taskRepository.findById(command.taskId);
    const agent = await this.agentRepository.findById(command.agentId);
    // 3. Business logic (in domain)
    task.assignTo(agent);
    // 4. Persist changes
    await this.taskRepository.save(task);
    // 5. Publish domain events
    task.getUncommittedEvents().forEach(event =>
      this.eventBus.publish(event)
    );
    // 6. Return result
    return TaskResult.success(task);
  }
 }
 ```
 ## Module Configuration
 ### Bounded Context Modules
 ```typescript
 // core/domains/task-management/module.ts
 export const taskManagementModule = {
  name: 'task-management',
  entities: [
    TaskEntity,
    TaskQueueEntity
  ],
  valueObjects: [
    TaskIdVO,
    TaskStatusVO,
    PriorityVO
  ],
  services: [
    TaskSchedulingService,
    TaskValidationService
  ],
  repositories: [
    { provide: ITaskRepository, useClass: SqliteTaskRepository }
  ],
  eventHandlers: [
    TaskAssignedHandler,
    TaskCompletedHandler
  ]
 };
 ```
 ## Migration Strategy
 ### Phase 1: Extract Domain Services
 ```typescript
 // Extract services from orchestrator.ts
 const extractionPlan = {
  week1: [
    'TaskManager → task-management domain',
    'SessionManager → session-management domain'
  ],
  week2: [
    'HealthMonitor → health-monitoring domain',
    'LifecycleManager → lifecycle-management domain'
  ],
  week3: [
    'EventCoordinator → event-coordination domain',
    'Wire up domain events'
  ]
 };
 ```
 ### Phase 2: Implement Clean Interfaces
 ```typescript
 // Clean separation with dependency injection
 export class TaskController {
  constructor(
    @Inject('AssignTaskUseCase') private assignTask: AssignTaskUseCase,
    @Inject('CompleteTaskUseCase') private completeTask: CompleteTaskUseCase
  ) {}
  async assign(request: AssignTaskRequest): Promise<TaskResponse> {
    const command = AssignTaskCommand.fromRequest(request);
    const result = await this.assignTask.execute(command);
    return TaskResponse.fromResult(result);
  }
 }
 ```
 ### Phase 3: Plugin System
 ```typescript
 // Enable plugin-based extensions
 const pluginSystem = {
  core: ['task-management', 'session-management', 'health-monitoring'],
  optional: ['swarm-coordination', 'learning-integration', 'performance-monitoring']
 };
 ```
 ## Testing Strategy
 ### Domain Testing (London School TDD)
 ```typescript
 // Pure domain logic testing
 describe('Task Entity', () => {
  let task: TaskEntity;
  let mockAgent: jest.Mocked<AgentEntity>;
  beforeEach(() => {
    task = new TaskEntity(TaskId.create(), 'Test task');
    mockAgent = createMock<AgentEntity>();
  });
  it('should assign to agent when valid', () => {
    mockAgent.canAcceptTask.mockReturnValue(true);
    task.assignTo(mockAgent);
    expect(task.assignedAgent).toBe(mockAgent);
    expect(task.status.value).toBe('assigned');
  });
  it('should emit TaskAssignedEvent when assigned', () => {
    mockAgent.canAcceptTask.mockReturnValue(true);
    task.assignTo(mockAgent);
    const events = task.getUncommittedEvents();
    expect(events).toHaveLength(1);
    expect(events[0]).toBeInstanceOf(TaskAssignedEvent);
  });
 });
 ```
 ## Success Metrics
 - [ ] **God Object Elimination**: orchestrator.ts (1,440 lines) → 5 focused domains (<300 lines each)
 - [ ] **Bounded Context Isolation**: 100% domain independence
 - [ ] **Plugin Architecture**: Core + optional modules loading
 - [ ] **Clean Architecture**: Dependency inversion maintained
 - [ ] **Event-Driven Communication**: Loose coupling between domains
 - [ ] **Test Coverage**: >90% domain logic coverage
 ## Related V3 Skills
 - `v3-core-implementation` - Implementation of DDD domains
 - `v3-memory-unification` - AgentDB integration within bounded contexts
 - `v3-swarm-coordination` - Swarm coordination as domain plugin
 - `v3-performance-optimization` - Performance optimization across domains
 ## Usage Examples
 ### Complete Domain Extraction
 ```bash
 # Full DDD architecture implementation
 Task("DDD architecture implementation",
     "Extract orchestrator into DDD domains with clean architecture",
     "core-architect")
 ```
 ### Plugin Development
 ```bash
 # Create domain plugin
 npm run create:plugin -- --name swarm-coordination --template domain
 ```
@@ -1,241 +0,0 @@
 ---
 name: "V3 Deep Integration"
 description: "Deep agentic-flow@alpha integration implementing ADR-001. Eliminates 10,000+ duplicate lines by building Codex-flow as specialized extension rather than parallel implementation."
 ---
 # V3 Deep Integration
 ## What This Skill Does
 Transforms Codex-flow from parallel implementation to specialized extension of agentic-flow@alpha, eliminating massive code duplication while achieving performance improvements and feature parity.
 ## Quick Start
 ```bash
 # Initialize deep integration
 Task("Integration architecture", "Design agentic-flow@alpha adapter layer", "v3-integration-architect")
 # Feature integration (parallel)
 Task("SONA integration", "Integrate 5 SONA learning modes", "v3-integration-architect")
 Task("Flash Attention", "Implement 2.49x-7.47x speedup", "v3-integration-architect")
 Task("AgentDB coordination", "Setup 150x-12,500x search", "v3-integration-architect")
 ```
 ## Code Deduplication Strategy
 ### Current Overlap → Integration
 ```
 ┌─────────────────────────────────────────┐
 │  Codex-flow          agentic-flow      │
 ├─────────────────────────────────────────┤
 │ SwarmCoordinator  →   Swarm System      │ 80% overlap (eliminate)
 │ AgentManager      →   Agent Lifecycle   │ 70% overlap (eliminate)
 │ TaskScheduler     →   Task Execution    │ 60% overlap (eliminate)
 │ SessionManager    →   Session Mgmt      │ 50% overlap (eliminate)
 └─────────────────────────────────────────┘
 TARGET: <5,000 lines (vs 15,000+ currently)
 ```
 ## agentic-flow@alpha Feature Integration
 ### SONA Learning Modes
 ```typescript
 class SONAIntegration {
  async initializeMode(mode: SONAMode): Promise<void> {
    switch(mode) {
      case 'real-time':   // ~0.05ms adaptation
      case 'balanced':    // general purpose
      case 'research':    // deep exploration
      case 'edge':        // resource-constrained
      case 'batch':       // high-throughput
    }
    await this.agenticFlow.sona.setMode(mode);
  }
 }
 ```
 ### Flash Attention Integration
 ```typescript
 class FlashAttentionIntegration {
  async optimizeAttention(): Promise<AttentionResult> {
    return this.agenticFlow.attention.flashAttention({
      speedupTarget: '2.49x-7.47x',
      memoryReduction: '50-75%',
      mechanisms: ['multi-head', 'linear', 'local', 'global']
    });
  }
 }
 ```
 ### AgentDB Coordination
 ```typescript
 class AgentDBIntegration {
  async setupCrossAgentMemory(): Promise<void> {
    await this.agentdb.enableCrossAgentSharing({
      indexType: 'HNSW',
      speedupTarget: '150x-12500x',
      dimensions: 1536
    });
  }
 }
 ```
 ### MCP Tools Integration
 ```typescript
 class MCPToolsIntegration {
  async integrateBuiltinTools(): Promise<void> {
    // Leverage 213 pre-built tools
    const tools = await this.agenticFlow.mcp.getAvailableTools();
    await this.registerClaudeFlowSpecificTools(tools);
    // Use 19 hook types
    const hookTypes = await this.agenticFlow.hooks.getTypes();
    await this.configureClaudeFlowHooks(hookTypes);
  }
 }
 ```
 ## Migration Implementation
 ### Phase 1: Adapter Layer
 ```typescript
 import { Agent as AgenticFlowAgent } from 'agentic-flow@alpha';
 export class ClaudeFlowAgent extends AgenticFlowAgent {
  async handleClaudeFlowTask(task: ClaudeTask): Promise<TaskResult> {
    return this.executeWithSONA(task);
  }
  // Backward compatibility
  async legacyCompatibilityLayer(oldAPI: any): Promise<any> {
    return this.adaptToNewAPI(oldAPI);
  }
 }
 ```
 ### Phase 2: System Migration
 ```typescript
 class SystemMigration {
  async migrateSwarmCoordination(): Promise<void> {
    // Replace SwarmCoordinator (800+ lines) with agentic-flow Swarm
    const swarmConfig = await this.extractSwarmConfig();
    await this.agenticFlow.swarm.initialize(swarmConfig);
  }
  async migrateAgentManagement(): Promise<void> {
    // Replace AgentManager (1,736+ lines) with agentic-flow lifecycle
    const agents = await this.extractActiveAgents();
    for (const agent of agents) {
      await this.agenticFlow.agent.create(agent);
    }
  }
  async migrateTaskExecution(): Promise<void> {
    // Replace TaskScheduler with agentic-flow task graph
    const tasks = await this.extractTasks();
    await this.agenticFlow.task.executeGraph(this.buildTaskGraph(tasks));
  }
 }
 ```
 ### Phase 3: Cleanup
 ```typescript
 class CodeCleanup {
  async removeDeprecatedCode(): Promise<void> {
    // Remove massive duplicate implementations
    await this.removeFile('src/core/SwarmCoordinator.ts');    // 800+ lines
    await this.removeFile('src/agents/AgentManager.ts');      // 1,736+ lines
    await this.removeFile('src/task/TaskScheduler.ts');       // 500+ lines
    // Total reduction: 10,000+ → <5,000 lines
  }
 }
 ```
 ## RL Algorithm Integration
 ```typescript
 class RLIntegration {
  algorithms = [
    'PPO', 'DQN', 'A2C', 'MCTS', 'Q-Learning',
    'SARSA', 'Actor-Critic', 'Decision-Transformer'
  ];
  async optimizeAgentBehavior(): Promise<void> {
    for (const algorithm of this.algorithms) {
      await this.agenticFlow.rl.train(algorithm, {
        episodes: 1000,
        rewardFunction: this.claudeFlowRewardFunction
      });
    }
  }
 }
 ```
 ## Performance Integration
 ### Flash Attention Targets
 ```typescript
 const attentionBenchmark = {
  baseline: 'current attention mechanism',
  target: '2.49x-7.47x improvement',
  memoryReduction: '50-75%',
  implementation: 'agentic-flow@alpha Flash Attention'
 };
 ```
 ### AgentDB Search Performance
 ```typescript
 const searchBenchmark = {
  baseline: 'linear search in current systems',
  target: '150x-12,500x via HNSW indexing',
  implementation: 'agentic-flow@alpha AgentDB'
 };
 ```
 ## Backward Compatibility
 ### Gradual Migration
 ```typescript
 class BackwardCompatibility {
  // Phase 1: Dual operation
  async enableDualOperation(): Promise<void> {
    this.oldSystem.continue();
    this.newSystem.initialize();
    this.syncState(this.oldSystem, this.newSystem);
  }
  // Phase 2: Feature-by-feature migration
  async migrateGradually(): Promise<void> {
    const features = this.getAllFeatures();
    for (const feature of features) {
      await this.migrateFeature(feature);
      await this.validateFeatureParity(feature);
    }
  }
  // Phase 3: Complete transition
  async completeTransition(): Promise<void> {
    await this.validateFullParity();
    await this.deprecateOldSystem();
  }
 }
 ```
 ## Success Metrics
 - **Code Reduction**: <5,000 lines orchestration (vs 15,000+)
 - **Performance**: 2.49x-7.47x Flash Attention speedup
 - **Search**: 150x-12,500x AgentDB improvement
 - **Memory**: 50-75% usage reduction
 - **Feature Parity**: 100% v2 functionality maintained
 - **SONA**: <0.05ms adaptation time
 - **Integration**: All 213 MCP tools + 19 hook types available
 ## Related V3 Skills
 - `v3-memory-unification` - Memory system integration
 - `v3-performance-optimization` - Performance target validation
 - `v3-swarm-coordination` - Swarm system migration
 - `v3-security-overhaul` - Secure integration patterns
@@ -1,777 +0,0 @@
 ---
 name: "V3 MCP Optimization"
 description: "MCP server optimization and transport layer enhancement for Codex-flow v3. Implements connection pooling, load balancing, tool registry optimization, and performance monitoring for sub-100ms response times."
 ---
 # V3 MCP Optimization
 ## What This Skill Does
 Optimizes Codex-flow v3 MCP (Model Context Protocol) server implementation with advanced transport layer optimizations, connection pooling, load balancing, and comprehensive performance monitoring to achieve sub-100ms response times.
 ## Quick Start
 ```bash
 # Initialize MCP optimization analysis
 Task("MCP architecture", "Analyze current MCP server performance and bottlenecks", "mcp-specialist")
 # Optimization implementation (parallel)
 Task("Connection pooling", "Implement MCP connection pooling and reuse", "mcp-specialist")
 Task("Load balancing", "Add dynamic load balancing for MCP tools", "mcp-specialist")
 Task("Transport optimization", "Optimize transport layer performance", "mcp-specialist")
 ```
 ## MCP Performance Architecture
 ### Current State Analysis
 ```
 Current MCP Issues:
 ├── Cold Start Latency: ~1.8s MCP server init
 ├── Connection Overhead: New connection per request
 ├── Tool Registry: Linear search O(n) for 213+ tools
 ├── Transport Layer: No connection reuse
 └── Memory Usage: No cleanup of idle connections
 Target Performance:
 ├── Startup Time: <400ms (4.5x improvement)
 ├── Tool Lookup: <5ms (O(1) hash table)
 ├── Connection Reuse: 90%+ connection pool hits
 ├── Response Time: <100ms p95
 └── Memory Efficiency: 50% reduction
 ```
 ### MCP Server Architecture
 ```typescript
 // src/core/mcp/mcp-server.ts
 import { Server } from '@modelcontextprotocol/sdk/server/index.js';
 import { StdioServerTransport } from '@modelcontextprotocol/sdk/server/stdio.js';
 interface OptimizedMCPConfig {
  // Connection pooling
  maxConnections: number;
  idleTimeoutMs: number;
  connectionReuseEnabled: boolean;
  // Tool registry
  toolCacheEnabled: boolean;
  toolIndexType: 'hash' | 'trie';
  // Performance
  requestTimeoutMs: number;
  batchingEnabled: boolean;
  compressionEnabled: boolean;
  // Monitoring
  metricsEnabled: boolean;
  healthCheckIntervalMs: number;
 }
 export class OptimizedMCPServer {
  private server: Server;
  private connectionPool: ConnectionPool;
  private toolRegistry: FastToolRegistry;
  private loadBalancer: MCPLoadBalancer;
  private metrics: MCPMetrics;
  constructor(config: OptimizedMCPConfig) {
    this.server = new Server({
      name: 'Codex-flow-v3',
      version: '3.0.0'
    }, {
      capabilities: {
        tools: { listChanged: true },
        resources: { subscribe: true, listChanged: true },
        prompts: { listChanged: true }
      }
    });
    this.connectionPool = new ConnectionPool(config);
    this.toolRegistry = new FastToolRegistry(config.toolIndexType);
    this.loadBalancer = new MCPLoadBalancer();
    this.metrics = new MCPMetrics(config.metricsEnabled);
  }
  async start(): Promise<void> {
    // Pre-warm connection pool
    await this.connectionPool.preWarm();
    // Pre-build tool index
    await this.toolRegistry.buildIndex();
    // Setup request handlers with optimizations
    this.setupOptimizedHandlers();
    // Start health monitoring
    this.startHealthMonitoring();
    // Start server
    const transport = new StdioServerTransport();
    await this.server.connect(transport);
    this.metrics.recordStartup();
  }
 }
 ```
 ## Connection Pool Implementation
 ### Advanced Connection Pooling
 ```typescript
 // src/core/mcp/connection-pool.ts
 interface PooledConnection {
  id: string;
  connection: MCPConnection;
  lastUsed: number;
  usageCount: number;
  isHealthy: boolean;
 }
 export class ConnectionPool {
  private pool: Map<string, PooledConnection> = new Map();
  private readonly config: ConnectionPoolConfig;
  private healthChecker: HealthChecker;
  constructor(config: ConnectionPoolConfig) {
    this.config = {
      maxConnections: 50,
      minConnections: 5,
      idleTimeoutMs: 300000, // 5 minutes
      maxUsageCount: 1000,
      healthCheckIntervalMs: 30000,
      ...config
    };
    this.healthChecker = new HealthChecker(this.config.healthCheckIntervalMs);
  }
  async getConnection(endpoint: string): Promise<MCPConnection> {
    const start = performance.now();
    // Try to get from pool first
    const pooled = this.findAvailableConnection(endpoint);
    if (pooled) {
      pooled.lastUsed = Date.now();
      pooled.usageCount++;
      this.recordMetric('pool_hit', performance.now() - start);
      return pooled.connection;
    }
    // Check pool capacity
    if (this.pool.size >= this.config.maxConnections) {
      await this.evictLeastUsedConnection();
    }
    // Create new connection
    const connection = await this.createConnection(endpoint);
    const pooledConn: PooledConnection = {
      id: this.generateConnectionId(),
      connection,
      lastUsed: Date.now(),
      usageCount: 1,
      isHealthy: true
    };
    this.pool.set(pooledConn.id, pooledConn);
    this.recordMetric('pool_miss', performance.now() - start);
    return connection;
  }
  async releaseConnection(connection: MCPConnection): Promise<void> {
    // Mark connection as available for reuse
    const pooled = this.findConnectionById(connection.id);
    if (pooled) {
      // Check if connection should be retired
      if (pooled.usageCount >= this.config.maxUsageCount) {
        await this.removeConnection(pooled.id);
      }
    }
  }
  async preWarm(): Promise<void> {
    const connections: Promise<MCPConnection>[] = [];
    for (let i = 0; i < this.config.minConnections; i++) {
      connections.push(this.createConnection('default'));
    }
    await Promise.all(connections);
  }
  private async evictLeastUsedConnection(): Promise<void> {
    let oldestConn: PooledConnection | null = null;
    let oldestTime = Date.now();
    for (const conn of this.pool.values()) {
      if (conn.lastUsed < oldestTime) {
        oldestTime = conn.lastUsed;
        oldestConn = conn;
      }
    }
    if (oldestConn) {
      await this.removeConnection(oldestConn.id);
    }
  }
  private findAvailableConnection(endpoint: string): PooledConnection | null {
    for (const conn of this.pool.values()) {
      if (conn.isHealthy &&
          conn.connection.endpoint === endpoint &&
          Date.now() - conn.lastUsed < this.config.idleTimeoutMs) {
        return conn;
      }
    }
    return null;
  }
 }
 ```
 ## Fast Tool Registry
 ### O(1) Tool Lookup Implementation
 ```typescript
 // src/core/mcp/fast-tool-registry.ts
 interface ToolIndexEntry {
  name: string;
  handler: ToolHandler;
  metadata: ToolMetadata;
  usageCount: number;
  avgLatencyMs: number;
 }
 export class FastToolRegistry {
  private toolIndex: Map<string, ToolIndexEntry> = new Map();
  private categoryIndex: Map<string, string[]> = new Map();
  private fuzzyMatcher: FuzzyMatcher;
  private cache: LRUCache<string, ToolIndexEntry>;
  constructor(indexType: 'hash' | 'trie' = 'hash') {
    this.fuzzyMatcher = new FuzzyMatcher();
    this.cache = new LRUCache<string, ToolIndexEntry>(1000); // Cache 1000 most used tools
  }
  async buildIndex(): Promise<void> {
    const start = performance.now();
    // Load all available tools
    const tools = await this.loadAllTools();
    // Build hash index for O(1) lookup
    for (const tool of tools) {
      const entry: ToolIndexEntry = {
        name: tool.name,
        handler: tool.handler,
        metadata: tool.metadata,
        usageCount: 0,
        avgLatencyMs: 0
      };
      this.toolIndex.set(tool.name, entry);
      // Build category index
      const category = tool.metadata.category || 'general';
      if (!this.categoryIndex.has(category)) {
        this.categoryIndex.set(category, []);
      }
      this.categoryIndex.get(category)!.push(tool.name);
    }
    // Build fuzzy search index
    await this.fuzzyMatcher.buildIndex(tools.map(t => t.name));
    console.log(`Tool index built in ${(performance.now() - start).toFixed(2)}ms for ${tools.length} tools`);
  }
  findTool(name: string): ToolIndexEntry | null {
    // Try cache first
    const cached = this.cache.get(name);
    if (cached) return cached;
    // Try exact match
    const exact = this.toolIndex.get(name);
    if (exact) {
      this.cache.set(name, exact);
      return exact;
    }
    // Try fuzzy match
    const fuzzyMatches = this.fuzzyMatcher.search(name, 1);
    if (fuzzyMatches.length > 0) {
      const match = this.toolIndex.get(fuzzyMatches[0]);
      if (match) {
        this.cache.set(name, match);
        return match;
      }
    }
    return null;
  }
  findToolsByCategory(category: string): ToolIndexEntry[] {
    const toolNames = this.categoryIndex.get(category) || [];
    return toolNames
      .map(name => this.toolIndex.get(name))
      .filter(entry => entry !== undefined) as ToolIndexEntry[];
  }
  getMostUsedTools(limit: number = 10): ToolIndexEntry[] {
    return Array.from(this.toolIndex.values())
      .sort((a, b) => b.usageCount - a.usageCount)
      .slice(0, limit);
  }
  recordToolUsage(toolName: string, latencyMs: number): void {
    const entry = this.toolIndex.get(toolName);
    if (entry) {
      entry.usageCount++;
      // Moving average for latency
      entry.avgLatencyMs = (entry.avgLatencyMs + latencyMs) / 2;
    }
  }
 }
 ```
 ## Load Balancing & Request Distribution
 ### Intelligent Load Balancer
 ```typescript
 // src/core/mcp/load-balancer.ts
 interface ServerInstance {
  id: string;
  endpoint: string;
  load: number;
  responseTime: number;
  isHealthy: boolean;
  maxConnections: number;
  currentConnections: number;
 }
 export class MCPLoadBalancer {
  private servers: Map<string, ServerInstance> = new Map();
  private routingStrategy: RoutingStrategy = 'least-connections';
  addServer(server: ServerInstance): void {
    this.servers.set(server.id, server);
  }
  selectServer(toolCategory?: string): ServerInstance | null {
    const healthyServers = Array.from(this.servers.values())
      .filter(server => server.isHealthy);
    if (healthyServers.length === 0) return null;
    switch (this.routingStrategy) {
      case 'round-robin':
        return this.roundRobinSelection(healthyServers);
      case 'least-connections':
        return this.leastConnectionsSelection(healthyServers);
      case 'response-time':
        return this.responseTimeSelection(healthyServers);
      case 'weighted':
        return this.weightedSelection(healthyServers, toolCategory);
      default:
        return healthyServers[0];
    }
  }
  private leastConnectionsSelection(servers: ServerInstance[]): ServerInstance {
    return servers.reduce((least, current) =>
      current.currentConnections < least.currentConnections ? current : least
    );
  }
  private responseTimeSelection(servers: ServerInstance[]): ServerInstance {
    return servers.reduce((fastest, current) =>
      current.responseTime < fastest.responseTime ? current : fastest
    );
  }
  private weightedSelection(servers: ServerInstance[], category?: string): ServerInstance {
    // Prefer servers with lower load and better response time
    const scored = servers.map(server => ({
      server,
      score: this.calculateServerScore(server, category)
    }));
    scored.sort((a, b) => b.score - a.score);
    return scored[0].server;
  }
  private calculateServerScore(server: ServerInstance, category?: string): number {
    const loadFactor = 1 - (server.currentConnections / server.maxConnections);
    const responseFactor = 1 / (server.responseTime + 1);
    const categoryBonus = this.getCategoryBonus(server, category);
    return loadFactor * 0.4 + responseFactor * 0.4 + categoryBonus * 0.2;
  }
  updateServerMetrics(serverId: string, metrics: Partial<ServerInstance>): void {
    const server = this.servers.get(serverId);
    if (server) {
      Object.assign(server, metrics);
    }
  }
 }
 ```
 ## Transport Layer Optimization
 ### High-Performance Transport
 ```typescript
 // src/core/mcp/optimized-transport.ts
 export class OptimizedTransport {
  private compression: boolean = true;
  private batching: boolean = true;
  private batchBuffer: MCPMessage[] = [];
  private batchTimeout: NodeJS.Timeout | null = null;
  constructor(private config: TransportConfig) {}
  async send(message: MCPMessage): Promise<void> {
    if (this.batching && this.canBatch(message)) {
      this.addToBatch(message);
      return;
    }
    await this.sendImmediate(message);
  }
  private async sendImmediate(message: MCPMessage): Promise<void> {
    const start = performance.now();
    // Compress if enabled
    const payload = this.compression
      ? await this.compress(message)
      : message;
    // Send through transport
    await this.transport.send(payload);
    // Record metrics
    this.recordLatency(performance.now() - start);
  }
  private addToBatch(message: MCPMessage): void {
    this.batchBuffer.push(message);
    // Start batch timeout if not already running
    if (!this.batchTimeout) {
      this.batchTimeout = setTimeout(
        () => this.flushBatch(),
        this.config.batchTimeoutMs || 10
      );
    }
    // Flush if batch is full
    if (this.batchBuffer.length >= this.config.maxBatchSize) {
      this.flushBatch();
    }
  }
  private async flushBatch(): Promise<void> {
    if (this.batchBuffer.length === 0) return;
    const batch = this.batchBuffer.splice(0);
    this.batchTimeout = null;
    // Send as single batched message
    await this.sendImmediate({
      type: 'batch',
      messages: batch
    });
  }
  private canBatch(message: MCPMessage): boolean {
    // Don't batch urgent messages or responses
    return message.type !== 'response' &&
           message.priority !== 'high' &&
           message.type !== 'error';
  }
  private async compress(data: any): Promise<Buffer> {
    // Use fast compression for smaller messages
    return gzipSync(JSON.stringify(data));
  }
 }
 ```
 ## Performance Monitoring
 ### Real-time MCP Metrics
 ```typescript
 // src/core/mcp/metrics.ts
 interface MCPMetrics {
  requestCount: number;
  errorCount: number;
  avgResponseTime: number;
  p95ResponseTime: number;
  connectionPoolHits: number;
  connectionPoolMisses: number;
  toolLookupTime: number;
  startupTime: number;
 }
 export class MCPMetricsCollector {
  private metrics: MCPMetrics;
  private responseTimeBuffer: number[] = [];
  private readonly bufferSize = 1000;
  constructor() {
    this.metrics = this.createInitialMetrics();
  }
  recordRequest(latencyMs: number): void {
    this.metrics.requestCount++;
    this.updateResponseTimes(latencyMs);
  }
  recordError(): void {
    this.metrics.errorCount++;
  }
  recordConnectionPoolHit(): void {
    this.metrics.connectionPoolHits++;
  }
  recordConnectionPoolMiss(): void {
    this.metrics.connectionPoolMisses++;
  }
  recordToolLookup(latencyMs: number): void {
    this.metrics.toolLookupTime = this.updateMovingAverage(
      this.metrics.toolLookupTime,
      latencyMs
    );
  }
  recordStartup(latencyMs: number): void {
    this.metrics.startupTime = latencyMs;
  }
  getMetrics(): MCPMetrics {
    return { ...this.metrics };
  }
  getHealthStatus(): HealthStatus {
    const errorRate = this.metrics.errorCount / this.metrics.requestCount;
    const poolHitRate = this.metrics.connectionPoolHits /
      (this.metrics.connectionPoolHits + this.metrics.connectionPoolMisses);
    return {
      status: this.determineHealthStatus(errorRate, poolHitRate),
      errorRate,
      poolHitRate,
      avgResponseTime: this.metrics.avgResponseTime,
      p95ResponseTime: this.metrics.p95ResponseTime
    };
  }
  private updateResponseTimes(latency: number): void {
    this.responseTimeBuffer.push(latency);
    if (this.responseTimeBuffer.length > this.bufferSize) {
      this.responseTimeBuffer.shift();
    }
    this.metrics.avgResponseTime = this.calculateAverage(this.responseTimeBuffer);
    this.metrics.p95ResponseTime = this.calculatePercentile(this.responseTimeBuffer, 95);
  }
  private calculatePercentile(arr: number[], percentile: number): number {
    const sorted = arr.slice().sort((a, b) => a - b);
    const index = Math.ceil((percentile / 100) * sorted.length) - 1;
    return sorted[index] || 0;
  }
  private determineHealthStatus(errorRate: number, poolHitRate: number): 'healthy' | 'warning' | 'critical' {
    if (errorRate > 0.1 || poolHitRate < 0.5) return 'critical';
    if (errorRate > 0.05 || poolHitRate < 0.7) return 'warning';
    return 'healthy';
  }
 }
 ```
 ## Tool Registry Optimization
 ### Pre-compiled Tool Index
 ```typescript
 // src/core/mcp/tool-precompiler.ts
 export class ToolPrecompiler {
  async precompileTools(): Promise<CompiledToolRegistry> {
    const tools = await this.loadAllTools();
    // Create optimized lookup structures
    const nameIndex = new Map<string, Tool>();
    const categoryIndex = new Map<string, Tool[]>();
    const fuzzyIndex = new Map<string, string[]>();
    for (const tool of tools) {
      // Exact name index
      nameIndex.set(tool.name, tool);
      // Category index
      const category = tool.metadata.category || 'general';
      if (!categoryIndex.has(category)) {
        categoryIndex.set(category, []);
      }
      categoryIndex.get(category)!.push(tool);
      // Pre-compute fuzzy variations
      const variations = this.generateFuzzyVariations(tool.name);
      for (const variation of variations) {
        if (!fuzzyIndex.has(variation)) {
          fuzzyIndex.set(variation, []);
        }
        fuzzyIndex.get(variation)!.push(tool.name);
      }
    }
    return {
      nameIndex,
      categoryIndex,
      fuzzyIndex,
      totalTools: tools.length,
      compiledAt: new Date()
    };
  }
  private generateFuzzyVariations(name: string): string[] {
    const variations: string[] = [];
    // Common typos and abbreviations
    variations.push(name.toLowerCase());
    variations.push(name.replace(/[-_]/g, ''));
    variations.push(name.replace(/[aeiou]/gi, '')); // Consonants only
    // Add more fuzzy matching logic as needed
    return variations;
  }
 }
 ```
 ## Advanced Caching Strategy
 ### Multi-Level Caching
 ```typescript
 // src/core/mcp/multi-level-cache.ts
 export class MultiLevelCache {
  private l1Cache: Map<string, any> = new Map(); // In-memory, fastest
  private l2Cache: LRUCache<string, any>; // LRU cache, larger capacity
  private l3Cache: DiskCache; // Persistent disk cache
  constructor(config: CacheConfig) {
    this.l2Cache = new LRUCache<string, any>({
      max: config.l2MaxEntries || 10000,
      ttl: config.l2TTL || 300000 // 5 minutes
    });
    this.l3Cache = new DiskCache(config.l3Path || './.cache/mcp');
  }
  async get(key: string): Promise<any | null> {
    // Try L1 cache first (fastest)
    if (this.l1Cache.has(key)) {
      return this.l1Cache.get(key);
    }
    // Try L2 cache
    const l2Value = this.l2Cache.get(key);
    if (l2Value) {
      // Promote to L1
      this.l1Cache.set(key, l2Value);
      return l2Value;
    }
    // Try L3 cache (disk)
    const l3Value = await this.l3Cache.get(key);
    if (l3Value) {
      // Promote to L2 and L1
      this.l2Cache.set(key, l3Value);
      this.l1Cache.set(key, l3Value);
      return l3Value;
    }
    return null;
  }
  async set(key: string, value: any, options?: CacheOptions): Promise<void> {
    // Set in all levels
    this.l1Cache.set(key, value);
    this.l2Cache.set(key, value);
    if (options?.persistent) {
      await this.l3Cache.set(key, value);
    }
    // Manage L1 cache size
    if (this.l1Cache.size > 1000) {
      const firstKey = this.l1Cache.keys().next().value;
      this.l1Cache.delete(firstKey);
    }
  }
 }
 ```
 ## Success Metrics
 ### Performance Targets
 - [ ] **Startup Time**: <400ms MCP server initialization (4.5x improvement)
 - [ ] **Response Time**: <100ms p95 for tool execution
 - [ ] **Tool Lookup**: <5ms average lookup time
 - [ ] **Connection Pool**: >90% hit rate
 - [ ] **Memory Usage**: 50% reduction in idle memory
 - [ ] **Error Rate**: <1% failed requests
 - [ ] **Throughput**: >1000 requests/second
 ### Monitoring Dashboards
 ```typescript
 const mcpDashboard = {
  metrics: [
    'Request latency (p50, p95, p99)',
    'Error rate by tool category',
    'Connection pool utilization',
    'Tool lookup performance',
    'Memory usage trends',
    'Cache hit rates (L1, L2, L3)'
  ],
  alerts: [
    'Response time >200ms for 5 minutes',
    'Error rate >5% for 1 minute',
    'Pool hit rate <70% for 10 minutes',
    'Memory usage >500MB for 5 minutes'
  ]
 };
 ```
 ## Related V3 Skills
 - `v3-core-implementation` - Core domain integration with MCP
 - `v3-performance-optimization` - Overall performance optimization
 - `v3-swarm-coordination` - MCP integration with swarm coordination
 - `v3-memory-unification` - Memory sharing via MCP tools
 ## Usage Examples
 ### Complete MCP Optimization
 ```bash
 # Full MCP server optimization
 Task("MCP optimization implementation",
     "Implement all MCP performance optimizations with monitoring",
     "mcp-specialist")
 ```
 ### Specific Optimization
 ```bash
 # Connection pool optimization
 Task("MCP connection pooling",
     "Implement advanced connection pooling with health monitoring",
     "mcp-specialist")
 ```
@@ -1,174 +0,0 @@
 ---
 name: "V3 Memory Unification"
 description: "Unify 6+ memory systems into AgentDB with HNSW indexing for 150x-12,500x search improvements. Implements ADR-006 (Unified Memory Service) and ADR-009 (Hybrid Memory Backend)."
 ---
 # V3 Memory Unification
 ## What This Skill Does
 Consolidates disparate memory systems into unified AgentDB backend with HNSW vector search, achieving 150x-12,500x search performance improvements while maintaining backward compatibility.
 ## Quick Start
 ```bash
 # Initialize memory unification
 Task("Memory architecture", "Design AgentDB unification strategy", "v3-memory-specialist")
 # AgentDB integration
 Task("AgentDB setup", "Configure HNSW indexing and vector search", "v3-memory-specialist")
 # Data migration
 Task("Memory migration", "Migrate SQLite/Markdown to AgentDB", "v3-memory-specialist")
 ```
 ## Systems to Unify
 ### Legacy Systems → AgentDB
 ```
 ┌─────────────────────────────────────────┐
 │  • MemoryManager (basic operations)     │
 │  • DistributedMemorySystem (clustering) │
 │  • SwarmMemory (agent-specific)         │
 │  • AdvancedMemoryManager (features)     │
 │  • SQLiteBackend (structured)           │
 │  • MarkdownBackend (file-based)         │
 │  • HybridBackend (combination)          │
 └─────────────────────────────────────────┘
                    ↓
 ┌─────────────────────────────────────────┐
 │       🚀 AgentDB with HNSW             │
 │  • 150x-12,500x faster search          │
 │  • Unified query interface             │
 │  • Cross-agent memory sharing          │
 │  • SONA learning integration           │
 └─────────────────────────────────────────┘
 ```
 ## Implementation Architecture
 ### Unified Memory Service
 ```typescript
 class UnifiedMemoryService implements IMemoryBackend {
  constructor(
    private agentdb: AgentDBAdapter,
    private indexer: HNSWIndexer,
    private migrator: DataMigrator
  ) {}
  async store(entry: MemoryEntry): Promise<void> {
    await this.agentdb.store(entry);
    await this.indexer.index(entry);
  }
  async query(query: MemoryQuery): Promise<MemoryEntry[]> {
    if (query.semantic) {
      return this.indexer.search(query); // 150x-12,500x faster
    }
    return this.agentdb.query(query);
  }
 }
 ```
 ### HNSW Vector Search
 ```typescript
 class HNSWIndexer {
  constructor(dimensions: number = 1536) {
    this.index = new HNSWIndex({
      dimensions,
      efConstruction: 200,
      M: 16,
      speedupTarget: '150x-12500x'
    });
  }
  async search(query: MemoryQuery): Promise<MemoryEntry[]> {
    const embedding = await this.embedContent(query.content);
    const results = this.index.search(embedding, query.limit || 10);
    return this.retrieveEntries(results);
  }
 }
 ```
 ## Migration Strategy
 ### Phase 1: Foundation
 ```typescript
 // AgentDB adapter setup
 const agentdb = new AgentDBAdapter({
  dimensions: 1536,
  indexType: 'HNSW',
  speedupTarget: '150x-12500x'
 });
 ```
 ### Phase 2: Data Migration
 ```typescript
 // SQLite → AgentDB
 const migrateFromSQLite = async () => {
  const entries = await sqlite.getAll();
  for (const entry of entries) {
    const embedding = await generateEmbedding(entry.content);
    await agentdb.store({ ...entry, embedding });
  }
 };
 // Markdown → AgentDB
 const migrateFromMarkdown = async () => {
  const files = await glob('**/*.md');
  for (const file of files) {
    const content = await fs.readFile(file, 'utf-8');
    await agentdb.store({
      id: generateId(),
      content,
      embedding: await generateEmbedding(content),
      metadata: { originalFile: file }
    });
  }
 };
 ```
 ## SONA Integration
 ### Learning Pattern Storage
 ```typescript
 class SONAMemoryIntegration {
  async storePattern(pattern: LearningPattern): Promise<void> {
    await this.memory.store({
      id: pattern.id,
      content: pattern.data,
      metadata: {
        sonaMode: pattern.mode,
        reward: pattern.reward,
        adaptationTime: pattern.adaptationTime
      },
      embedding: await this.generateEmbedding(pattern.data)
    });
  }
  async retrieveSimilarPatterns(query: string): Promise<LearningPattern[]> {
    return this.memory.query({
      type: 'semantic',
      content: query,
      filters: { type: 'learning_pattern' }
    });
  }
 }
 ```
 ## Performance Targets
 - **Search Speed**: 150x-12,500x improvement via HNSW
 - **Memory Usage**: 50-75% reduction through optimization
 - **Query Latency**: <100ms for 1M+ entries
 - **Cross-Agent Sharing**: Real-time memory synchronization
 - **SONA Integration**: <0.05ms adaptation time
 ## Success Metrics
 - [ ] All 7 legacy memory systems migrated to AgentDB
 - [ ] 150x-12,500x search performance validated
 - [ ] 50-75% memory usage reduction achieved
 - [ ] Backward compatibility maintained
 - [ ] SONA learning patterns integrated
 - [ ] Cross-agent memory sharing operational
@@ -1,390 +0,0 @@
 ---
 name: "V3 Performance Optimization"
 description: "Achieve aggressive v3 performance targets: 2.49x-7.47x Flash Attention speedup, 150x-12,500x search improvements, 50-75% memory reduction. Comprehensive benchmarking and optimization suite."
 ---
 # V3 Performance Optimization
 ## What This Skill Does
 Validates and optimizes Codex-flow v3 to achieve industry-leading performance through Flash Attention, AgentDB HNSW indexing, and comprehensive system optimization with continuous benchmarking.
 ## Quick Start
 ```bash
 # Initialize performance optimization
 Task("Performance baseline", "Establish v2 performance benchmarks", "v3-performance-engineer")
 # Target validation (parallel)
 Task("Flash Attention", "Validate 2.49x-7.47x speedup target", "v3-performance-engineer")
 Task("Search optimization", "Validate 150x-12,500x search improvement", "v3-performance-engineer")
 Task("Memory optimization", "Achieve 50-75% memory reduction", "v3-performance-engineer")
 ```
 ## Performance Target Matrix
 ### Flash Attention Revolution
 ```
 ┌─────────────────────────────────────────┐
 │           FLASH ATTENTION               │
 ├─────────────────────────────────────────┤
 │  Baseline: Standard attention           │
 │  Target:   2.49x - 7.47x speedup       │
 │  Memory:   50-75% reduction             │
 │  Latency:  Sub-millisecond processing   │
 └─────────────────────────────────────────┘
 ```
 ### Search Performance Revolution
 ```
 ┌─────────────────────────────────────────┐
 │            SEARCH OPTIMIZATION         │
 ├─────────────────────────────────────────┤
 │  Current:  O(n) linear search           │
 │  Target:   150x - 12,500x improvement   │
 │  Method:   HNSW indexing                │
 │  Latency:  <100ms for 1M+ entries       │
 └─────────────────────────────────────────┘
 ```
 ## Comprehensive Benchmark Suite
 ### Startup Performance
 ```typescript
 class StartupBenchmarks {
  async benchmarkColdStart(): Promise<BenchmarkResult> {
    const startTime = performance.now();
    await this.initializeCLI();
    await this.initializeMCPServer();
    await this.spawnTestAgent();
    const totalTime = performance.now() - startTime;
    return {
      total: totalTime,
      target: 500, // ms
      achieved: totalTime < 500
    };
  }
 }
 ```
 ### Memory Operation Benchmarks
 ```typescript
 class MemoryBenchmarks {
  async benchmarkVectorSearch(): Promise<SearchBenchmark> {
    const queries = this.generateTestQueries(10000);
    // Baseline: Current linear search
    const baselineTime = await this.timeOperation(() =>
      this.currentMemory.searchAll(queries)
    );
    // Target: HNSW search
    const hnswTime = await this.timeOperation(() =>
      this.agentDBMemory.hnswSearchAll(queries)
    );
    const improvement = baselineTime / hnswTime;
    return {
      baseline: baselineTime,
      hnsw: hnswTime,
      improvement,
      targetRange: [150, 12500],
      achieved: improvement >= 150
    };
  }
  async benchmarkMemoryUsage(): Promise<MemoryBenchmark> {
    const baseline = process.memoryUsage().heapUsed;
    await this.loadTestDataset();
    const withData = process.memoryUsage().heapUsed;
    await this.enableOptimization();
    const optimized = process.memoryUsage().heapUsed;
    const reduction = (withData - optimized) / withData;
    return {
      baseline,
      withData,
      optimized,
      reductionPercent: reduction * 100,
      targetReduction: [50, 75],
      achieved: reduction >= 0.5
    };
  }
 }
 ```
 ### Swarm Coordination Benchmarks
 ```typescript
 class SwarmBenchmarks {
  async benchmark15AgentCoordination(): Promise<SwarmBenchmark> {
    const agents = await this.spawn15Agents();
    // Coordination latency
    const coordinationTime = await this.timeOperation(() =>
      this.coordinateSwarmTask(agents)
    );
    // Task decomposition
    const decompositionTime = await this.timeOperation(() =>
      this.decomposeComplexTask()
    );
    // Consensus achievement
    const consensusTime = await this.timeOperation(() =>
      this.achieveSwarmConsensus(agents)
    );
    return {
      coordination: coordinationTime,
      decomposition: decompositionTime,
      consensus: consensusTime,
      agentCount: 15,
      efficiency: this.calculateEfficiency(agents)
    };
  }
 }
 ```
 ### Flash Attention Benchmarks
 ```typescript
 class AttentionBenchmarks {
  async benchmarkFlashAttention(): Promise<AttentionBenchmark> {
    const sequences = this.generateSequences([512, 1024, 2048, 4096]);
    const results = [];
    for (const sequence of sequences) {
      // Baseline attention
      const baselineResult = await this.benchmarkStandardAttention(sequence);
      // Flash attention
      const flashResult = await this.benchmarkFlashAttention(sequence);
      results.push({
        sequenceLength: sequence.length,
        speedup: baselineResult.time / flashResult.time,
        memoryReduction: (baselineResult.memory - flashResult.memory) / baselineResult.memory,
        targetSpeedup: [2.49, 7.47],
        achieved: this.checkTarget(flashResult, [2.49, 7.47])
      });
    }
    return {
      results,
      averageSpeedup: this.calculateAverage(results, 'speedup'),
      averageMemoryReduction: this.calculateAverage(results, 'memoryReduction')
    };
  }
 }
 ```
 ### SONA Learning Benchmarks
 ```typescript
 class SONABenchmarks {
  async benchmarkAdaptationTime(): Promise<SONABenchmark> {
    const scenarios = [
      'pattern_recognition',
      'task_optimization',
      'error_correction',
      'performance_tuning'
    ];
    const results = [];
    for (const scenario of scenarios) {
      const startTime = performance.hrtime.bigint();
      await this.sona.adapt(scenario);
      const endTime = performance.hrtime.bigint();
      const adaptationTimeMs = Number(endTime - startTime) / 1000000;
      results.push({
        scenario,
        adaptationTime: adaptationTimeMs,
        target: 0.05, // ms
        achieved: adaptationTimeMs <= 0.05
      });
    }
    return {
      scenarios: results,
      averageTime: results.reduce((sum, r) => sum + r.adaptationTime, 0) / results.length,
      successRate: results.filter(r => r.achieved).length / results.length
    };
  }
 }
 ```
 ## Performance Monitoring Dashboard
 ### Real-time Metrics
 ```typescript
 class PerformanceMonitor {
  async collectMetrics(): Promise<PerformanceSnapshot> {
    return {
      timestamp: Date.now(),
      flashAttention: await this.measureFlashAttention(),
      searchPerformance: await this.measureSearchSpeed(),
      memoryUsage: await this.measureMemoryEfficiency(),
      startupTime: await this.measureStartupLatency(),
      sonaAdaptation: await this.measureSONASpeed(),
      swarmCoordination: await this.measureSwarmEfficiency()
    };
  }
  async generateReport(): Promise<PerformanceReport> {
    const snapshot = await this.collectMetrics();
    return {
      summary: this.generateSummary(snapshot),
      achievements: this.checkTargetAchievements(snapshot),
      trends: this.analyzeTrends(),
      recommendations: this.generateOptimizations(),
      regressions: await this.detectRegressions()
    };
  }
 }
 ```
 ### Continuous Regression Detection
 ```typescript
 class PerformanceRegression {
  async detectRegressions(): Promise<RegressionReport> {
    const current = await this.runFullBenchmark();
    const baseline = await this.getBaseline();
    const regressions = [];
    for (const [metric, currentValue] of Object.entries(current)) {
      const baselineValue = baseline[metric];
      const change = (currentValue - baselineValue) / baselineValue;
      if (change < -0.05) { // 5% regression threshold
        regressions.push({
          metric,
          baseline: baselineValue,
          current: currentValue,
          regressionPercent: change * 100,
          severity: this.classifyRegression(change)
        });
      }
    }
    return {
      hasRegressions: regressions.length > 0,
      regressions,
      recommendations: this.generateRegressionFixes(regressions)
    };
  }
 }
 ```
 ## Optimization Strategies
 ### Memory Optimization
 ```typescript
 class MemoryOptimization {
  async optimizeMemoryUsage(): Promise<OptimizationResult> {
    // Implement memory pooling
    await this.setupMemoryPools();
    // Enable garbage collection tuning
    await this.optimizeGarbageCollection();
    // Implement object reuse patterns
    await this.setupObjectPools();
    // Enable memory compression
    await this.enableMemoryCompression();
    return this.validateMemoryReduction();
  }
 }
 ```
 ### CPU Optimization
 ```typescript
 class CPUOptimization {
  async optimizeCPUUsage(): Promise<OptimizationResult> {
    // Implement worker thread pools
    await this.setupWorkerThreads();
    // Enable CPU-specific optimizations
    await this.enableSIMDInstructions();
    // Implement task batching
    await this.optimizeTaskBatching();
    return this.validateCPUImprovement();
  }
 }
 ```
 ## Target Validation Framework
 ### Performance Gates
 ```typescript
 class PerformanceGates {
  async validateAllTargets(): Promise<ValidationReport> {
    const results = await Promise.all([
      this.validateFlashAttention(),     // 2.49x-7.47x
      this.validateSearchPerformance(),  // 150x-12,500x
      this.validateMemoryReduction(),    // 50-75%
      this.validateStartupTime(),        // <500ms
      this.validateSONAAdaptation()      // <0.05ms
    ]);
    return {
      allTargetsAchieved: results.every(r => r.achieved),
      results,
      overallScore: this.calculateOverallScore(results),
      recommendations: this.generateRecommendations(results)
    };
  }
 }
 ```
 ## Success Metrics
 ### Primary Targets
 - [ ] **Flash Attention**: 2.49x-7.47x speedup validated
 - [ ] **Search Performance**: 150x-12,500x improvement confirmed
 - [ ] **Memory Reduction**: 50-75% usage optimization achieved
 - [ ] **Startup Time**: <500ms cold start consistently
 - [ ] **SONA Adaptation**: <0.05ms learning response time
 - [ ] **15-Agent Coordination**: Efficient parallel execution
 ### Continuous Monitoring
 - [ ] **Performance Dashboard**: Real-time metrics collection
 - [ ] **Regression Testing**: Automated performance validation
 - [ ] **Trend Analysis**: Performance evolution tracking
 - [ ] **Alert System**: Immediate regression notification
 ## Related V3 Skills
 - `v3-integration-deep` - Performance integration with agentic-flow
 - `v3-memory-unification` - Memory performance optimization
 - `v3-swarm-coordination` - Swarm performance coordination
 - `v3-security-overhaul` - Secure performance patterns
 ## Usage Examples
 ### Complete Performance Validation
 ```bash
 # Full performance suite
 npm run benchmark:v3
 # Specific target validation
 npm run benchmark:flash-attention
 npm run benchmark:agentdb-search
 npm run benchmark:memory-optimization
 # Continuous monitoring
 npm run monitor:performance
 ```
@@ -1,82 +0,0 @@
 ---
 name: "V3 Security Overhaul"
 description: "Complete security architecture overhaul for Codex-flow v3. Addresses critical CVEs (CVE-1, CVE-2, CVE-3) and implements secure-by-default patterns. Use for security-first v3 implementation."
 ---
 # V3 Security Overhaul
 ## What This Skill Does
 Orchestrates comprehensive security overhaul for Codex-flow v3, addressing critical vulnerabilities and establishing security-first development practices using specialized v3 security agents.
 ## Quick Start
 ```bash
 # Initialize V3 security domain (parallel)
 Task("Security architecture", "Design v3 threat model and security boundaries", "v3-security-architect")
 Task("CVE remediation", "Fix CVE-1, CVE-2, CVE-3 critical vulnerabilities", "security-auditor")
 Task("Security testing", "Implement TDD London School security framework", "test-architect")
 ```
 ## Critical Security Fixes
 ### CVE-1: Vulnerable Dependencies
 ```bash
 npm update @anthropic-ai/Codex@^2.0.31
 npm audit --audit-level high
 ```
 ### CVE-2: Weak Password Hashing
 ```typescript
 // ❌ Old: SHA-256 with hardcoded salt
 const hash = crypto.createHash('sha256').update(password + salt).digest('hex');
 // ✅ New: bcrypt with 12 rounds
 import bcrypt from 'bcrypt';
 const hash = await bcrypt.hash(password, 12);
 ```
 ### CVE-3: Hardcoded Credentials
 ```typescript
 // ✅ Generate secure random credentials
 const apiKey = crypto.randomBytes(32).toString('hex');
 ```
 ## Security Patterns
 ### Input Validation (Zod)
 ```typescript
 import { z } from 'zod';
 const TaskSchema = z.object({
  taskId: z.string().uuid(),
  content: z.string().max(10000),
  agentType: z.enum(['security', 'core', 'integration'])
 });
 ```
 ### Path Sanitization
 ```typescript
 function securePath(userPath: string, allowedPrefix: string): string {
  const resolved = path.resolve(allowedPrefix, userPath);
  if (!resolved.startsWith(path.resolve(allowedPrefix))) {
    throw new SecurityError('Path traversal detected');
  }
  return resolved;
 }
 ```
 ### Safe Command Execution
 ```typescript
 import { execFile } from 'child_process';
 // ✅ Safe: No shell interpretation
 const { stdout } = await execFile('git', [userInput], { shell: false });
 ```
 ## Success Metrics
 - **Security Score**: 90/100 (npm audit + custom scans)
 - **CVE Resolution**: 100% of critical vulnerabilities fixed
 - **Test Coverage**: >95% security-critical code
 - **Implementation**: All secure patterns documented and tested
@@ -1,340 +0,0 @@
 ---
 name: "V3 Swarm Coordination"
 description: "15-agent hierarchical mesh coordination for v3 implementation. Orchestrates parallel execution across security, core, and integration domains following 10 ADRs with 14-week timeline."
 ---
 # V3 Swarm Coordination
 ## What This Skill Does
 Orchestrates the complete 15-agent hierarchical mesh swarm for Codex-flow v3 implementation, coordinating parallel execution across domains while maintaining dependencies and timeline adherence.
 ## Quick Start
 ```bash
 # Initialize 15-agent v3 swarm
 Task("Swarm initialization", "Initialize hierarchical mesh for v3 implementation", "v3-queen-coordinator")
 # Security domain (Phase 1 - Critical priority)
 Task("Security architecture", "Design v3 threat model and security boundaries", "v3-security-architect")
 Task("CVE remediation", "Fix CVE-1, CVE-2, CVE-3 vulnerabilities", "security-auditor")
 Task("Security testing", "Implement TDD security framework", "test-architect")
 # Core domain (Phase 2 - Parallel execution)
 Task("Memory unification", "Implement AgentDB 150x improvement", "v3-memory-specialist")
 Task("Integration architecture", "Deep agentic-flow@alpha integration", "v3-integration-architect")
 Task("Performance validation", "Validate 2.49x-7.47x targets", "v3-performance-engineer")
 ```
 ## 15-Agent Swarm Architecture
 ### Hierarchical Mesh Topology
 ```
                    👑 QUEEN COORDINATOR
                         (Agent #1)
                             │
        ┌────────────────────┼────────────────────┐
        │                   │                    │
   🛡️ SECURITY         🧠 CORE              🔗 INTEGRATION
   (Agents #2-4)       (Agents #5-9)        (Agents #10-12)
        │                   │                    │
        └────────────────────┼────────────────────┘
                             │
        ┌────────────────────┼────────────────────┐
        │                   │                    │
   🧪 QUALITY          ⚡ PERFORMANCE        🚀 DEPLOYMENT
   (Agent #13)         (Agent #14)          (Agent #15)
 ```
 ### Agent Roster
 | ID | Agent | Domain | Phase | Responsibility |
 |----|-------|--------|-------|----------------|
 | 1 | Queen Coordinator | Orchestration | All | GitHub issues, dependencies, timeline |
 | 2 | Security Architect | Security | Foundation | Threat modeling, CVE planning |
 | 3 | Security Implementer | Security | Foundation | CVE fixes, secure patterns |
 | 4 | Security Tester | Security | Foundation | TDD security testing |
 | 5 | Core Architect | Core | Systems | DDD architecture, coordination |
 | 6 | Core Implementer | Core | Systems | Core module implementation |
 | 7 | Memory Specialist | Core | Systems | AgentDB unification |
 | 8 | Swarm Specialist | Core | Systems | Unified coordination engine |
 | 9 | MCP Specialist | Core | Systems | MCP server optimization |
 | 10 | Integration Architect | Integration | Integration | agentic-flow@alpha deep integration |
 | 11 | CLI/Hooks Developer | Integration | Integration | CLI modernization |
 | 12 | Neural/Learning Dev | Integration | Integration | SONA integration |
 | 13 | TDD Test Engineer | Quality | All | London School TDD |
 | 14 | Performance Engineer | Performance | Optimization | Benchmarking validation |
 | 15 | Release Engineer | Deployment | Release | CI/CD and v3.0.0 release |
 ## Implementation Phases
 ### Phase 1: Foundation (Week 1-2)
 **Active Agents**: #1, #2-4, #5-6
 ```typescript
 const phase1 = async () => {
  // Parallel security and architecture foundation
  await Promise.all([
    // Security domain (critical priority)
    Task("Security architecture", "Complete threat model and security boundaries", "v3-security-architect"),
    Task("CVE-1 fix", "Update vulnerable dependencies", "security-implementer"),
    Task("CVE-2 fix", "Replace weak password hashing", "security-implementer"),
    Task("CVE-3 fix", "Remove hardcoded credentials", "security-implementer"),
    Task("Security testing", "TDD London School security framework", "test-architect"),
    // Core architecture foundation
    Task("DDD architecture", "Design domain boundaries and structure", "core-architect"),
    Task("Type modernization", "Update type system for v3", "core-implementer")
  ]);
 };
 ```
 ### Phase 2: Core Systems (Week 3-6)
 **Active Agents**: #1, #5-9, #13
 ```typescript
 const phase2 = async () => {
  // Parallel core system implementation
  await Promise.all([
    Task("Memory unification", "Implement AgentDB with 150x-12,500x improvement", "v3-memory-specialist"),
    Task("Swarm coordination", "Merge 4 coordination systems into unified engine", "swarm-specialist"),
    Task("MCP optimization", "Optimize MCP server performance", "mcp-specialist"),
    Task("Core implementation", "Implement DDD modular architecture", "core-implementer"),
    Task("TDD core tests", "Comprehensive test coverage for core systems", "test-architect")
  ]);
 };
 ```
 ### Phase 3: Integration (Week 7-10)
 **Active Agents**: #1, #10-12, #13-14
 ```typescript
 const phase3 = async () => {
  // Parallel integration and optimization
  await Promise.all([
    Task("agentic-flow integration", "Eliminate 10,000+ duplicate lines", "v3-integration-architect"),
    Task("CLI modernization", "Enhance CLI with hooks system", "cli-hooks-developer"),
    Task("SONA integration", "Implement <0.05ms learning adaptation", "neural-learning-developer"),
    Task("Performance benchmarking", "Validate 2.49x-7.47x targets", "v3-performance-engineer"),
    Task("Integration testing", "End-to-end system validation", "test-architect")
  ]);
 };
 ```
 ### Phase 4: Release (Week 11-14)
 **Active Agents**: All 15
 ```typescript
 const phase4 = async () => {
  // Full swarm final optimization
  await Promise.all([
    Task("Performance optimization", "Final optimization pass", "v3-performance-engineer"),
    Task("Release preparation", "CI/CD pipeline and v3.0.0 release", "release-engineer"),
    Task("Final testing", "Complete test coverage validation", "test-architect"),
    // All agents: Final polish and optimization
    ...agents.map(agent =>
      Task("Final polish", `Agent ${agent.id} final optimization`, agent.name)
    )
  ]);
 };
 ```
 ## Coordination Patterns
 ### Dependency Management
 ```typescript
 class DependencyCoordination {
  private dependencies = new Map([
    // Security first (no dependencies)
    [2, []], [3, [2]], [4, [2, 3]],
    // Core depends on security foundation
    [5, [2]], [6, [5]], [7, [5]], [8, [5, 7]], [9, [5]],
    // Integration depends on core systems
    [10, [5, 7, 8]], [11, [5, 10]], [12, [7, 10]],
    // Quality and performance cross-cutting
    [13, [2, 5]], [14, [5, 7, 8, 10]], [15, [13, 14]]
  ]);
  async coordinateExecution(): Promise<void> {
    const completed = new Set<number>();
    while (completed.size < 15) {
      const ready = this.getReadyAgents(completed);
      if (ready.length === 0) {
        throw new Error('Deadlock detected in dependency chain');
      }
      // Execute ready agents in parallel
      await Promise.all(ready.map(agentId => this.executeAgent(agentId)));
      ready.forEach(id => completed.add(id));
    }
  }
 }
 ```
 ### GitHub Integration
 ```typescript
 class GitHubCoordination {
  async initializeV3Milestone(): Promise<void> {
    await gh.createMilestone({
      title: 'Codex-Flow v3.0.0 Implementation',
      description: '15-agent swarm implementation of 10 ADRs',
      dueDate: this.calculate14WeekDeadline()
    });
  }
  async createEpicIssues(): Promise<void> {
    const epics = [
      { title: 'Security Overhaul (CVE-1,2,3)', agents: [2, 3, 4] },
      { title: 'Memory Unification (AgentDB)', agents: [7] },
      { title: 'agentic-flow Integration', agents: [10] },
      { title: 'Performance Optimization', agents: [14] },
      { title: 'DDD Architecture', agents: [5, 6] }
    ];
    for (const epic of epics) {
      await gh.createIssue({
        title: epic.title,
        labels: ['epic', 'v3', ...epic.agents.map(id => `agent-${id}`)],
        assignees: epic.agents.map(id => this.getAgentGithubUser(id))
      });
    }
  }
  async trackProgress(): Promise<void> {
    // Hourly progress updates from each agent
    setInterval(async () => {
      for (const agent of this.agents) {
        await this.postAgentProgress(agent);
      }
    }, 3600000); // 1 hour
  }
 }
 ```
 ### Communication Bus
 ```typescript
 class SwarmCommunication {
  private bus = new QuicSwarmBus({
    maxAgents: 15,
    messageTimeout: 30000,
    retryAttempts: 3
  });
  async broadcastToSecurityDomain(message: SwarmMessage): Promise<void> {
    await this.bus.broadcast(message, {
      targetAgents: [2, 3, 4],
      priority: 'critical'
    });
  }
  async coordinateCoreSystems(message: SwarmMessage): Promise<void> {
    await this.bus.broadcast(message, {
      targetAgents: [5, 6, 7, 8, 9],
      priority: 'high'
    });
  }
  async notifyIntegrationTeam(message: SwarmMessage): Promise<void> {
    await this.bus.broadcast(message, {
      targetAgents: [10, 11, 12],
      priority: 'medium'
    });
  }
 }
 ```
 ## Performance Coordination
 ### Parallel Efficiency Monitoring
 ```typescript
 class EfficiencyMonitor {
  async measureParallelEfficiency(): Promise<EfficiencyReport> {
    const agentUtilization = await this.measureAgentUtilization();
    const coordinationOverhead = await this.measureCoordinationCost();
    return {
      totalEfficiency: agentUtilization.average,
      target: 0.85, // >85% utilization
      achieved: agentUtilization.average > 0.85,
      bottlenecks: this.identifyBottlenecks(agentUtilization),
      recommendations: this.generateOptimizations()
    };
  }
 }
 ```
 ### Load Balancing
 ```typescript
 class SwarmLoadBalancer {
  async balanceWorkload(): Promise<void> {
    const workloads = await this.analyzeAgentWorkloads();
    for (const [agentId, load] of workloads.entries()) {
      if (load > this.getCapacityThreshold(agentId)) {
        await this.redistributeWork(agentId);
      }
    }
  }
  async redistributeWork(overloadedAgent: number): Promise<void> {
    const availableAgents = this.getAvailableAgents();
    const tasks = await this.getAgentTasks(overloadedAgent);
    // Redistribute tasks to available agents
    for (const task of tasks) {
      const bestAgent = this.selectOptimalAgent(task, availableAgents);
      await this.reassignTask(task, bestAgent);
    }
  }
 }
 ```
 ## Success Metrics
 ### Swarm Coordination
 - [ ] **Parallel Efficiency**: >85% agent utilization time
 - [ ] **Dependency Resolution**: Zero deadlocks or blocking issues
 - [ ] **Communication Latency**: <100ms inter-agent messaging
 - [ ] **Timeline Adherence**: 14-week delivery maintained
 - [ ] **GitHub Integration**: <4h automated issue response
 ### Implementation Targets
 - [ ] **ADR Coverage**: All 10 ADRs implemented successfully
 - [ ] **Performance**: 2.49x-7.47x Flash Attention achieved
 - [ ] **Search**: 150x-12,500x AgentDB improvement validated
 - [ ] **Code Reduction**: <5,000 lines (vs 15,000+)
 - [ ] **Security**: 90/100 security score achieved
 ## Related V3 Skills
 - `v3-security-overhaul` - Security domain coordination
 - `v3-memory-unification` - Memory system coordination
 - `v3-integration-deep` - Integration domain coordination
 - `v3-performance-optimization` - Performance domain coordination
 ## Usage Examples
 ### Initialize Complete V3 Swarm
 ```bash
 # Queen Coordinator initializes full swarm
 Task("V3 swarm initialization",
     "Initialize 15-agent hierarchical mesh for complete v3 implementation",
     "v3-queen-coordinator")
 ```
 ### Phase-based Execution
 ```bash
 # Phase 1: Security-first foundation
 npm run v3:phase1:security
 # Phase 2: Core systems parallel
 npm run v3:phase2:core-systems
 # Phase 3: Integration and optimization
 npm run v3:phase3:integration
 # Phase 4: Release preparation
 npm run v3:phase4:release
 ```
@@ -1,649 +0,0 @@
 ---
 name: "Verification & Quality Assurance"
 description: "Comprehensive truth scoring, code quality verification, and automatic rollback system with 0.95 accuracy threshold for ensuring high-quality agent outputs and codebase reliability."
 version: "2.0.0"
 category: "quality-assurance"
 tags: ["verification", "truth-scoring", "quality", "rollback", "metrics", "ci-cd"]
 ---
 # Verification & Quality Assurance Skill
 ## What This Skill Does
 This skill provides a comprehensive verification and quality assurance system that ensures code quality and correctness through:
 - **Truth Scoring**: Real-time reliability metrics (0.0-1.0 scale) for code, agents, and tasks
 - **Verification Checks**: Automated code correctness, security, and best practices validation
 - **Automatic Rollback**: Instant reversion of changes that fail verification (default threshold: 0.95)
 - **Quality Metrics**: Statistical analysis with trends, confidence intervals, and improvement tracking
 - **CI/CD Integration**: Export capabilities for continuous integration pipelines
 - **Real-time Monitoring**: Live dashboards and watch modes for ongoing verification
 ## Prerequisites
 - Codex Flow installed (`npx Codex-flow@alpha`)
 - Git repository (for rollback features)
 - Node.js 18+ (for dashboard features)
 ## Quick Start
 ```bash
 # View current truth scores
 npx Codex-flow@alpha truth
 # Run verification check
 npx Codex-flow@alpha verify check
 # Verify specific file with custom threshold
 npx Codex-flow@alpha verify check --file src/app.js --threshold 0.98
 # Rollback last failed verification
 npx Codex-flow@alpha verify rollback --last-good
 ```
 ---
 ## Complete Guide
 ### Truth Scoring System
 #### View Truth Metrics
 Display comprehensive quality and reliability metrics for your codebase and agent tasks.
 **Basic Usage:**
 ```bash
 # View current truth scores (default: table format)
 npx Codex-flow@alpha truth
 # View scores for specific time period
 npx Codex-flow@alpha truth --period 7d
 # View scores for specific agent
 npx Codex-flow@alpha truth --agent coder --period 24h
 # Find files/tasks below threshold
 npx Codex-flow@alpha truth --threshold 0.8
 ```
 **Output Formats:**
 ```bash
 # Table format (default)
 npx Codex-flow@alpha truth --format table
 # JSON for programmatic access
 npx Codex-flow@alpha truth --format json
 # CSV for spreadsheet analysis
 npx Codex-flow@alpha truth --format csv
 # HTML report with visualizations
 npx Codex-flow@alpha truth --format html --export report.html
 ```
 **Real-time Monitoring:**
 ```bash
 # Watch mode with live updates
 npx Codex-flow@alpha truth --watch
 # Export metrics automatically
 npx Codex-flow@alpha truth --export .Codex-flow/metrics/truth-$(date +%Y%m%d).json
 ```
 #### Truth Score Dashboard
 Example dashboard output:
 ```
 📊 Truth Metrics Dashboard
 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
 Overall Truth Score: 0.947 ✅
 Trend: ↗️ +2.3% (7d)
 Top Performers:
  verification-agent   0.982 ⭐
  code-analyzer       0.971 ⭐
  test-generator      0.958 ✅
 Needs Attention:
  refactor-agent      0.821 ⚠️
  docs-generator      0.794 ⚠️
 Recent Tasks:
  task-456  0.991 ✅  "Implement auth"
  task-455  0.967 ✅  "Add tests"
  task-454  0.743 ❌  "Refactor API"
 ```
 #### Metrics Explained
 **Truth Scores (0.0-1.0):**
 - `1.0-0.95`: Excellent ⭐ (production-ready)
 - `0.94-0.85`: Good ✅ (acceptable quality)
 - `0.84-0.75`: Warning ⚠️ (needs attention)
 - `<0.75`: Critical ❌ (requires immediate action)
 **Trend Indicators:**
 - ↗️ Improving (positive trend)
 - → Stable (consistent performance)
 - ↘️ Declining (quality regression detected)
 **Statistics:**
 - **Mean Score**: Average truth score across all measurements
 - **Median Score**: Middle value (less affected by outliers)
 - **Standard Deviation**: Consistency of scores (lower = more consistent)
 - **Confidence Interval**: Statistical reliability of measurements
 ### Verification Checks
 #### Run Verification
 Execute comprehensive verification checks on code, tasks, or agent outputs.
 **File Verification:**
 ```bash
 # Verify single file
 npx Codex-flow@alpha verify check --file src/app.js
 # Verify directory recursively
 npx Codex-flow@alpha verify check --directory src/
 # Verify with auto-fix enabled
 npx Codex-flow@alpha verify check --file src/utils.js --auto-fix
 # Verify current working directory
 npx Codex-flow@alpha verify check
 ```
 **Task Verification:**
 ```bash
 # Verify specific task output
 npx Codex-flow@alpha verify check --task task-123
 # Verify with custom threshold
 npx Codex-flow@alpha verify check --task task-456 --threshold 0.99
 # Verbose output for debugging
 npx Codex-flow@alpha verify check --task task-789 --verbose
 ```
 **Batch Verification:**
 ```bash
 # Verify multiple files in parallel
 npx Codex-flow@alpha verify batch --files "*.js" --parallel
 # Verify with pattern matching
 npx Codex-flow@alpha verify batch --pattern "src/**/*.ts"
 # Integration test suite
 npx Codex-flow@alpha verify integration --test-suite full
 ```
 #### Verification Criteria
 The verification system evaluates:
 1. **Code Correctness**
   - Syntax validation
   - Type checking (TypeScript)
   - Logic flow analysis
   - Error handling completeness
 2. **Best Practices**
   - Code style adherence
   - SOLID principles
   - Design patterns usage
   - Modularity and reusability
 3. **Security**
   - Vulnerability scanning
   - Secret detection
   - Input validation
   - Authentication/authorization checks
 4. **Performance**
   - Algorithmic complexity
   - Memory usage patterns
   - Database query optimization
   - Bundle size impact
 5. **Documentation**
   - JSDoc/TypeDoc completeness
   - README accuracy
   - API documentation
   - Code comments quality
 #### JSON Output for CI/CD
 ```bash
 # Get structured JSON output
 npx Codex-flow@alpha verify check --json > verification.json
 # Example JSON structure:
 {
  "overallScore": 0.947,
  "passed": true,
  "threshold": 0.95,
  "checks": [
    {
      "name": "code-correctness",
      "score": 0.98,
      "passed": true
    },
    {
      "name": "security",
      "score": 0.91,
      "passed": false,
      "issues": [...]
    }
  ]
 }
 ```
 ### Automatic Rollback
 #### Rollback Failed Changes
 Automatically revert changes that fail verification checks.
 **Basic Rollback:**
 ```bash
 # Rollback to last known good state
 npx Codex-flow@alpha verify rollback --last-good
 # Rollback to specific commit
 npx Codex-flow@alpha verify rollback --to-commit abc123
 # Interactive rollback with preview
 npx Codex-flow@alpha verify rollback --interactive
 ```
 **Smart Rollback:**
 ```bash
 # Rollback only failed files (preserve good changes)
 npx Codex-flow@alpha verify rollback --selective
 # Rollback with automatic backup
 npx Codex-flow@alpha verify rollback --backup-first
 # Dry-run mode (preview without executing)
 npx Codex-flow@alpha verify rollback --dry-run
 ```
 **Rollback Performance:**
 - Git-based rollback: <1 second
 - Selective file rollback: <500ms
 - Backup creation: Automatic before rollback
 ### Verification Reports
 #### Generate Reports
 Create detailed verification reports with metrics and visualizations.
 **Report Formats:**
 ```bash
 # JSON report
 npx Codex-flow@alpha verify report --format json
 # HTML report with charts
 npx Codex-flow@alpha verify report --export metrics.html --format html
 # CSV for data analysis
 npx Codex-flow@alpha verify report --format csv --export metrics.csv
 # Markdown summary
 npx Codex-flow@alpha verify report --format markdown
 ```
 **Time-based Reports:**
 ```bash
 # Last 24 hours
 npx Codex-flow@alpha verify report --period 24h
 # Last 7 days
 npx Codex-flow@alpha verify report --period 7d
 # Last 30 days with trends
 npx Codex-flow@alpha verify report --period 30d --include-trends
 # Custom date range
 npx Codex-flow@alpha verify report --from 2025-01-01 --to 2025-01-31
 ```
 **Report Content:**
 - Overall truth scores
 - Per-agent performance metrics
 - Task completion quality
 - Verification pass/fail rates
 - Rollback frequency
 - Quality improvement trends
 - Statistical confidence intervals
 ### Interactive Dashboard
 #### Launch Dashboard
 Run interactive web-based verification dashboard with real-time updates.
 ```bash
 # Launch dashboard on default port (3000)
 npx Codex-flow@alpha verify dashboard
 # Custom port
 npx Codex-flow@alpha verify dashboard --port 8080
 # Export dashboard data
 npx Codex-flow@alpha verify dashboard --export
 # Dashboard with auto-refresh
 npx Codex-flow@alpha verify dashboard --refresh 5s
 ```
 **Dashboard Features:**
 - Real-time truth score updates (WebSocket)
 - Interactive charts and graphs
 - Agent performance comparison
 - Task history timeline
 - Rollback history viewer
 - Export to PDF/HTML
 - Filter by time period/agent/score
 ### Configuration
 #### Default Configuration
 Set verification preferences in `.Codex-flow/config.json`:
 ```json
 {
  "verification": {
    "threshold": 0.95,
    "autoRollback": true,
    "gitIntegration": true,
    "hooks": {
      "preCommit": true,
      "preTask": true,
      "postEdit": true
    },
    "checks": {
      "codeCorrectness": true,
      "security": true,
      "performance": true,
      "documentation": true,
      "bestPractices": true
    }
  },
  "truth": {
    "defaultFormat": "table",
    "defaultPeriod": "24h",
    "warningThreshold": 0.85,
    "criticalThreshold": 0.75,
    "autoExport": {
      "enabled": true,
      "path": ".Codex-flow/metrics/truth-daily.json"
    }
  }
 }
 ```
 #### Threshold Configuration
 **Adjust verification strictness:**
 ```bash
 # Strict mode (99% accuracy required)
 npx Codex-flow@alpha verify check --threshold 0.99
 # Lenient mode (90% acceptable)
 npx Codex-flow@alpha verify check --threshold 0.90
 # Set default threshold
 npx Codex-flow@alpha config set verification.threshold 0.98
 ```
 **Per-environment thresholds:**
 ```json
 {
  "verification": {
    "thresholds": {
      "production": 0.99,
      "staging": 0.95,
      "development": 0.90
    }
  }
 }
 ```
 ### Integration Examples
 #### CI/CD Integration
 **GitHub Actions:**
 ```yaml
 name: Quality Verification
 on: [push, pull_request]
 jobs:
  verify:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - name: Install Dependencies
        run: npm install
      - name: Run Verification
        run: |
          npx Codex-flow@alpha verify check --json > verification.json
      - name: Check Truth Score
        run: |
          score=$(jq '.overallScore' verification.json)
          if (( $(echo "$score < 0.95" | bc -l) )); then
            echo "Truth score too low: $score"
            exit 1
          fi
      - name: Upload Report
        uses: actions/upload-artifact@v3
        with:
          name: verification-report
          path: verification.json
 ```
 **GitLab CI:**
 ```yaml
 verify:
  stage: test
  script:
    - npx Codex-flow@alpha verify check --threshold 0.95 --json > verification.json
    - |
      score=$(jq '.overallScore' verification.json)
      if [ $(echo "$score < 0.95" | bc) -eq 1 ]; then
        echo "Verification failed with score: $score"
        exit 1
      fi
  artifacts:
    paths:
      - verification.json
    reports:
      junit: verification.json
 ```
 #### Swarm Integration
 Run verification automatically during swarm operations:
 ```bash
 # Swarm with verification enabled
 npx Codex-flow@alpha swarm --verify --threshold 0.98
 # Hive Mind with auto-rollback
 npx Codex-flow@alpha hive-mind --verify --rollback-on-fail
 # Training pipeline with verification
 npx Codex-flow@alpha train --verify --threshold 0.99
 ```
 #### Pair Programming Integration
 Enable real-time verification during collaborative development:
 ```bash
 # Pair with verification
 npx Codex-flow@alpha pair --verify --real-time
 # Pair with custom threshold
 npx Codex-flow@alpha pair --verify --threshold 0.97 --auto-fix
 ```
 ### Advanced Workflows
 #### Continuous Verification
 Monitor codebase continuously during development:
 ```bash
 # Watch directory for changes
 npx Codex-flow@alpha verify watch --directory src/
 # Watch with auto-fix
 npx Codex-flow@alpha verify watch --directory src/ --auto-fix
 # Watch with notifications
 npx Codex-flow@alpha verify watch --notify --threshold 0.95
 ```
 #### Monitoring Integration
 Send metrics to external monitoring systems:
 ```bash
 # Export to Prometheus
 npx Codex-flow@alpha truth --format json | \
  curl -X POST https://pushgateway.example.com/metrics/job/Codex-flow \
  -d @-
 # Send to DataDog
 npx Codex-flow@alpha verify report --format json | \
  curl -X POST "https://api.datadoghq.com/api/v1/series?api_key=${DD_API_KEY}" \
  -H "Content-Type: application/json" \
  -d @-
 # Custom webhook
 npx Codex-flow@alpha truth --format json | \
  curl -X POST https://metrics.example.com/api/truth \
  -H "Content-Type: application/json" \
  -d @-
 ```
 #### Pre-commit Hooks
 Automatically verify before commits:
 ```bash
 # Install pre-commit hook
 npx Codex-flow@alpha verify install-hook --pre-commit
 # .git/hooks/pre-commit example:
 #!/bin/bash
 npx Codex-flow@alpha verify check --threshold 0.95 --json > /tmp/verify.json
 score=$(jq '.overallScore' /tmp/verify.json)
 if (( $(echo "$score < 0.95" | bc -l) )); then
  echo "❌ Verification failed with score: $score"
  echo "Run 'npx Codex-flow@alpha verify check --verbose' for details"
  exit 1
 fi
 echo "✅ Verification passed with score: $score"
 ```
 ### Performance Metrics
 **Verification Speed:**
 - Single file check: <100ms
 - Directory scan: <500ms (per 100 files)
 - Full codebase analysis: <5s (typical project)
 - Truth score calculation: <50ms
 **Rollback Speed:**
 - Git-based rollback: <1s
 - Selective file rollback: <500ms
 - Backup creation: <2s
 **Dashboard Performance:**
 - Initial load: <1s
 - Real-time updates: <100ms latency (WebSocket)
 - Chart rendering: 60 FPS
 ### Troubleshooting
 #### Common Issues
 **Low Truth Scores:**
 ```bash
 # Get detailed breakdown
 npx Codex-flow@alpha truth --verbose --threshold 0.0
 # Check specific criteria
 npx Codex-flow@alpha verify check --verbose
 # View agent-specific issues
 npx Codex-flow@alpha truth --agent <agent-name> --format json
 ```
 **Rollback Failures:**
 ```bash
 # Check git status
 git status
 # View rollback history
 npx Codex-flow@alpha verify rollback --history
 # Manual rollback
 git reset --hard HEAD~1
 ```
 **Verification Timeouts:**
 ```bash
 # Increase timeout
 npx Codex-flow@alpha verify check --timeout 60s
 # Verify in batches
 npx Codex-flow@alpha verify batch --batch-size 10
 ```
 ### Exit Codes
 Verification commands return standard exit codes:
 - `0`: Verification passed (score ≥ threshold)
 - `1`: Verification failed (score < threshold)
 - `2`: Error during verification (invalid input, system error)
 ### Related Commands
 - `npx Codex-flow@alpha pair` - Collaborative development with verification
 - `npx Codex-flow@alpha train` - Training with verification feedback
 - `npx Codex-flow@alpha swarm` - Multi-agent coordination with quality checks
 - `npx Codex-flow@alpha report` - Generate comprehensive project reports
 ### Best Practices
 1. **Set Appropriate Thresholds**: Use 0.99 for critical code, 0.95 for standard, 0.90 for experimental
 2. **Enable Auto-rollback**: Prevent bad code from persisting
 3. **Monitor Trends**: Track improvement over time, not just current scores
 4. **Integrate with CI/CD**: Make verification part of your pipeline
 5. **Use Watch Mode**: Get immediate feedback during development
 6. **Export Metrics**: Track quality metrics in your monitoring system
 7. **Review Rollbacks**: Understand why changes were rejected
 8. **Train Agents**: Use verification feedback to improve agent performance
 ### Additional Resources
 - Truth Scoring Algorithm: See `/docs/truth-scoring.md`
 - Verification Criteria: See `/docs/verification-criteria.md`
 - Integration Examples: See `/examples/verification/`
 - API Reference: See `/docs/api/verification.md`
@@ -0,0 +1,25 @@
 package main
 import (
  "fmt"
  "github.com/perfect-panel/server/internal/config"
  "github.com/perfect-panel/server/internal/svc"
  "github.com/perfect-panel/server/pkg/conf"
 )
 func main() {
  var c config.Config
  conf.MustLoad("/private/tmp/ppanel-local-upload.yaml", &c)
  ctx := svc.NewServiceContext(c)
  const key = "cache:auth:method:device"
  before, _ := ctx.Redis.Get(ctx.DB.Statement.Context, key).Result()
  fmt.Printf("cache before=%q\n", before)
  m, err := ctx.AuthModel.FindOneByMethod(ctx.DB.Statement.Context, "device")
  fmt.Printf("model err=%v enabled_nil=%v", err, m == nil || m.Enabled == nil)
  if m != nil && m.Enabled != nil { fmt.Printf(" enabled=%v", *m.Enabled) }
  if m != nil { fmt.Printf(" config=%s", m.Config) }
  fmt.Println()
  after, _ := ctx.Redis.Get(ctx.DB.Statement.Context, key).Result()
  fmt.Printf("cache after=%q\n", after)
 }
@@ -0,0 +1,23 @@
 package main
 import (
  "fmt"
  initpkg "github.com/perfect-panel/server/initialize"
  "github.com/perfect-panel/server/internal/config"
  "github.com/perfect-panel/server/internal/svc"
  "github.com/perfect-panel/server/pkg/conf"
 )
 func main() {
  var c config.Config
  conf.MustLoad("/private/tmp/ppanel-local-upload.yaml", &c)
  ctx := svc.NewServiceContext(c)
  method, err := ctx.AuthModel.FindOneByMethod(ctx.DB.Statement.Context, "device")
  if err != nil {
    panic(err)
  }
  fmt.Printf("db auth_method.enabled=%v config=%s\n", *method.Enabled, method.Config)
  initpkg.Device(ctx)
  fmt.Printf("ctx.Config.Device.Enable=%v SecuritySecret=%q EnableSecurity=%v\n", ctx.Config.Device.Enable, ctx.Config.Device.SecuritySecret, ctx.Config.Device.EnableSecurity)
 }
@@ -0,0 +1,36 @@
 package main
 import (
  "fmt"
  "github.com/perfect-panel/server/internal/config"
  "github.com/perfect-panel/server/internal/svc"
  "github.com/perfect-panel/server/pkg/conf"
 )
 type row struct {
  ID int64
  Method string
  Enabled int
  Config string
 }
 func main() {
  var c config.Config
  conf.MustLoad("/private/tmp/ppanel-local-upload.yaml", &c)
  ctx := svc.NewServiceContext(c)
  var rows []row
  if err := ctx.DB.Raw("SELECT id, method, enabled, config FROM auth_method WHERE method = ?", "device").Scan(&rows).Error; err != nil {
    panic(err)
  }
  fmt.Printf("raw rows: %+v\n", rows)
  m, err := ctx.AuthModel.FindOneByMethod(ctx.DB.Statement.Context, "device")
  fmt.Printf("model err=%v\n", err)
  if err == nil && m != nil && m.Enabled != nil {
    fmt.Printf("model row: id=%d method=%s enabled=%v config=%s\n", m.Id, m.Method, *m.Enabled, m.Config)
  } else {
    fmt.Printf("model row nil or no enabled ptr: %#v\n", m)
  }
 }
@@ -0,0 +1,28 @@
 package main
 import (
  "fmt"
  "github.com/perfect-panel/server/internal/config"
  authmodel "github.com/perfect-panel/server/internal/model/auth"
  "github.com/perfect-panel/server/internal/svc"
  "github.com/perfect-panel/server/pkg/conf"
 )
 func main() {
  var c config.Config
  conf.MustLoad("/private/tmp/ppanel-local-upload.yaml", &c)
  ctx := svc.NewServiceContext(c)
  var a1 authmodel.Auth
  err1 := ctx.DB.Model(&authmodel.Auth{}).Where("method = ?", "device").First(&a1).Error
  fmt.Printf("gorm direct err=%v enabled_nil=%v", err1, a1.Enabled == nil)
  if a1.Enabled != nil { fmt.Printf(" enabled=%v", *a1.Enabled) }
  fmt.Printf(" config=%s\n", a1.Config)
  var a2 authmodel.Auth
  err2 := ctx.DB.Table("auth_method").Where("method = ?", "device").First(&a2).Error
  fmt.Printf("gorm table err=%v enabled_nil=%v", err2, a2.Enabled == nil)
  if a2.Enabled != nil { fmt.Printf(" enabled=%v", *a2.Enabled) }
  fmt.Printf(" config=%s\n", a2.Config)
 }
@@ -1,579 +0,0 @@
 package main
 import (
 	"context"
 	"database/sql"
 	"encoding/json"
 	"fmt"
 	"log"
 	"os"
 	"sort"
 	"strings"
 	"time"
 	"github.com/redis/go-redis/v9"
 	_ "github.com/go-sql-driver/mysql"
 	"gopkg.in/yaml.v3"
 )
 type appConfig struct {
 	MySQL struct {
 		Addr     string `yaml:"Addr"`
 		Username string `yaml:"Username"`
 		Password string `yaml:"Password"`
 		Dbname   string `yaml:"Dbname"`
 		Config   string `yaml:"Config"`
 	} `yaml:"MySQL"`
 	Redis struct {
 		Host string `yaml:"Host"`
 		Pass string `yaml:"Pass"`
 		DB   int    `yaml:"DB"`
 	} `yaml:"Redis"`
 }
 type userRow struct {
 	ID           int64          `json:"id"`
 	ReferCode    string         `json:"refer_code"`
 	Balance      int64          `json:"balance"`
 	Commission   int64          `json:"commission"`
 	GiftAmount   int64          `json:"gift_amount"`
 	Enable       bool           `json:"enable"`
 	IsAdmin      bool           `json:"is_admin"`
 	ValidEmail   bool           `json:"valid_email"`
 	MemberStatus string         `json:"member_status"`
 	CreatedAt    time.Time      `json:"created_at"`
 	DeletedAt    sql.NullTime   `json:"-"`
 }
 type authMethod struct {
 	ID         int64     `json:"id"`
 	UserID     int64     `json:"user_id"`
 	AuthType   string    `json:"auth_type"`
 	Identifier string    `json:"identifier"`
 	Verified   bool      `json:"verified"`
 	CreatedAt  time.Time `json:"created_at"`
 }
 type deviceInfo struct {
 	ID         int64     `json:"id"`
 	UserID     int64     `json:"user_id"`
 	IP         string    `json:"ip"`
 	UserAgent  string    `json:"user_agent"`
 	Identifier string    `json:"identifier"`
 	ShortCode  string    `json:"short_code"`
 	Online     bool      `json:"online"`
 	Enabled    bool      `json:"enabled"`
 	CreatedAt  time.Time `json:"created_at"`
 }
 type subscribeInfo struct {
 	ID          int64     `json:"id"`
 	UserID      int64     `json:"user_id"`
 	OrderID     int64     `json:"order_id"`
 	SubscribeID int64     `json:"subscribe_id"`
 	Token       string    `json:"token"`
 	UUID        string    `json:"uuid"`
 	Status      uint8     `json:"status"`
 	StartTime   time.Time `json:"start_time"`
 	ExpireTime  time.Time `json:"expire_time"`
 }
 type familyInfo struct {
 	FamilyID     int64 `json:"family_id"`
 	OwnerUserID  int64 `json:"owner_user_id"`
 	IsOwner      bool  `json:"is_owner"`
 	MemberCount  int64 `json:"member_count"`
 }
 type userSummary struct {
 	User                userRow         `json:"user"`
 	AuthMethods         []authMethod    `json:"auth_methods"`
 	Devices             []deviceInfo    `json:"devices"`
 	Subscriptions       []subscribeInfo `json:"subscriptions"`
 	Family              *familyInfo     `json:"family,omitempty"`
 	OrderCount          int64           `json:"order_count"`
 	TicketCount         int64           `json:"ticket_count"`
 	TrafficLogCount     int64           `json:"traffic_log_count"`
 	SystemLogCount      int64           `json:"system_log_count"`
 	WithdrawalCount     int64           `json:"withdrawal_count"`
 	IAPTransactionCount int64           `json:"iap_transaction_count"`
 	LogMessageCount     int64           `json:"log_message_count"`
 	OnlineRecordCount   int64           `json:"online_record_count"`
 }
 type deleteResult struct {
 	UserID          int64    `json:"user_id"`
 	DeletedDBRows   []string `json:"deleted_db_rows"`
 	DeletedRedisKeys int     `json:"deleted_redis_keys"`
 }
 func must(err error) {
 	if err != nil {
 		log.Fatal(err)
 	}
 }
 func main() {
 	ctx := context.Background()
 	cfg := loadConfig("/Users/Apple/code_vpn/vpn/ppanel-server/etc/ppanel.yaml")
 	dsn := fmt.Sprintf("%s:%s@tcp(%s)/%s?%s", cfg.MySQL.Username, cfg.MySQL.Password, cfg.MySQL.Addr, cfg.MySQL.Dbname, cfg.MySQL.Config)
 	db, err := sql.Open("mysql", dsn)
 	must(err)
 	defer db.Close()
 	must(db.PingContext(ctx))
 	rdb := redis.NewClient(&redis.Options{
 		Addr:     cfg.Redis.Host,
 		Password: cfg.Redis.Pass,
 		DB:       cfg.Redis.DB,
 	})
 	defer rdb.Close()
 	must(rdb.Ping(ctx).Err())
 	targetUserIDs, err := findTargetUsers(ctx, db)
 	must(err)
 	if len(targetUserIDs) == 0 {
 		fmt.Println(`{"matched_users":[],"deleted":[]}`)
 		return
 	}
 	summaries := make([]userSummary, 0, len(targetUserIDs))
 	for _, userID := range targetUserIDs {
 		summary, sumErr := collectSummary(ctx, db, userID)
 		must(sumErr)
 		summaries = append(summaries, summary)
 	}
 	before, err := json.MarshalIndent(map[string]interface{}{
 		"matched_users": summaries,
 	}, "", "  ")
 	must(err)
 	fmt.Println(string(before))
 	results := make([]deleteResult, 0, len(targetUserIDs))
 	for _, summary := range summaries {
 		result, delErr := deleteUser(ctx, db, rdb, summary)
 		must(delErr)
 		results = append(results, result)
 	}
 	after, err := json.MarshalIndent(map[string]interface{}{
 		"deleted": results,
 	}, "", "  ")
 	must(err)
 	fmt.Println(string(after))
 }
 func loadConfig(path string) appConfig {
 	content, err := os.ReadFile(path)
 	must(err)
 	var cfg appConfig
 	must(yaml.Unmarshal(content, &cfg))
 	return cfg
 }
 func findTargetUsers(ctx context.Context, db *sql.DB) ([]int64, error) {
 	rows, err := db.QueryContext(ctx, `
 		SELECT DISTINCT user_id
 		FROM user_device
 		WHERE user_agent LIKE ?
 		ORDER BY user_id ASC
 	`, "%999%")
 	if err != nil {
 		return nil, err
 	}
 	defer rows.Close()
 	var ids []int64
 	for rows.Next() {
 		var id int64
 		if err := rows.Scan(&id); err != nil {
 			return nil, err
 		}
 		ids = append(ids, id)
 	}
 	return ids, rows.Err()
 }
 func collectSummary(ctx context.Context, db *sql.DB, userID int64) (userSummary, error) {
 	var summary userSummary
 	summary.User.ID = userID
 	err := db.QueryRowContext(ctx, `
 		SELECT id, refer_code, balance, commission, gift_amount, enable, is_admin, valid_email, member_status, created_at, deleted_at
 		FROM user
 		WHERE id = ?
 	`, userID).Scan(
 		&summary.User.ID,
 		&summary.User.ReferCode,
 		&summary.User.Balance,
 		&summary.User.Commission,
 		&summary.User.GiftAmount,
 		&summary.User.Enable,
 		&summary.User.IsAdmin,
 		&summary.User.ValidEmail,
 		&summary.User.MemberStatus,
 		&summary.User.CreatedAt,
 		&summary.User.DeletedAt,
 	)
 	if err != nil {
 		return summary, err
 	}
 	summary.AuthMethods, err = queryAuthMethods(ctx, db, userID)
 	if err != nil {
 		return summary, err
 	}
 	summary.Devices, err = queryDevices(ctx, db, userID)
 	if err != nil {
 		return summary, err
 	}
 	summary.Subscriptions, err = querySubscriptions(ctx, db, userID)
 	if err != nil {
 		return summary, err
 	}
 	summary.Family, err = queryFamily(ctx, db, userID)
 	if err != nil {
 		return summary, err
 	}
 	if summary.OrderCount, err = queryCount(ctx, db, "SELECT COUNT(*) FROM `order` WHERE user_id = ?", userID); err != nil {
 		return summary, err
 	}
 	if summary.TicketCount, err = queryCount(ctx, db, "SELECT COUNT(*) FROM ticket WHERE user_id = ?", userID); err != nil {
 		return summary, err
 	}
 	if summary.TrafficLogCount, err = queryCount(ctx, db, "SELECT COUNT(*) FROM traffic_log WHERE user_id = ?", userID); err != nil {
 		return summary, err
 	}
 	if summary.SystemLogCount, err = queryCount(ctx, db, "SELECT COUNT(*) FROM system_logs WHERE object_id = ?", userID); err != nil {
 		return summary, err
 	}
 	if summary.WithdrawalCount, err = queryCount(ctx, db, "SELECT COUNT(*) FROM user_withdrawal WHERE user_id = ?", userID); err != nil {
 		return summary, err
 	}
 	if summary.IAPTransactionCount, err = queryCount(ctx, db, "SELECT COUNT(*) FROM apple_iap_transactions WHERE user_id = ?", userID); err != nil {
 		return summary, err
 	}
 	if summary.LogMessageCount, err = queryCount(ctx, db, "SELECT COUNT(*) FROM log_message WHERE user_id = ?", userID); err != nil {
 		return summary, err
 	}
 	if summary.OnlineRecordCount, err = queryCount(ctx, db, "SELECT COUNT(*) FROM user_device_online_record WHERE user_id = ?", userID); err != nil {
 		return summary, err
 	}
 	return summary, nil
 }
 func queryAuthMethods(ctx context.Context, db *sql.DB, userID int64) ([]authMethod, error) {
 	rows, err := db.QueryContext(ctx, `
 		SELECT id, user_id, auth_type, auth_identifier, verified, created_at
 		FROM user_auth_methods
 		WHERE user_id = ?
 		ORDER BY id ASC
 	`, userID)
 	if err != nil {
 		return nil, err
 	}
 	defer rows.Close()
 	var items []authMethod
 	for rows.Next() {
 		var item authMethod
 		if err := rows.Scan(&item.ID, &item.UserID, &item.AuthType, &item.Identifier, &item.Verified, &item.CreatedAt); err != nil {
 			return nil, err
 		}
 		items = append(items, item)
 	}
 	return items, rows.Err()
 }
 func queryDevices(ctx context.Context, db *sql.DB, userID int64) ([]deviceInfo, error) {
 	rows, err := db.QueryContext(ctx, `
 		SELECT id, user_id, ip, user_agent, identifier, short_code, online, enabled, created_at
 		FROM user_device
 		WHERE user_id = ?
 		ORDER BY id ASC
 	`, userID)
 	if err != nil {
 		return nil, err
 	}
 	defer rows.Close()
 	var items []deviceInfo
 	for rows.Next() {
 		var item deviceInfo
 		if err := rows.Scan(&item.ID, &item.UserID, &item.IP, &item.UserAgent, &item.Identifier, &item.ShortCode, &item.Online, &item.Enabled, &item.CreatedAt); err != nil {
 			return nil, err
 		}
 		items = append(items, item)
 	}
 	return items, rows.Err()
 }
 func querySubscriptions(ctx context.Context, db *sql.DB, userID int64) ([]subscribeInfo, error) {
 	rows, err := db.QueryContext(ctx, `
 		SELECT id, user_id, order_id, subscribe_id, token, uuid, status, start_time, expire_time
 		FROM user_subscribe
 		WHERE user_id = ?
 		ORDER BY id ASC
 	`, userID)
 	if err != nil {
 		return nil, err
 	}
 	defer rows.Close()
 	var items []subscribeInfo
 	for rows.Next() {
 		var item subscribeInfo
 		if err := rows.Scan(&item.ID, &item.UserID, &item.OrderID, &item.SubscribeID, &item.Token, &item.UUID, &item.Status, &item.StartTime, &item.ExpireTime); err != nil {
 			return nil, err
 		}
 		items = append(items, item)
 	}
 	return items, rows.Err()
 }
 func queryFamily(ctx context.Context, db *sql.DB, userID int64) (*familyInfo, error) {
 	var info familyInfo
 	err := db.QueryRowContext(ctx, `
 		SELECT ufm.family_id, uf.owner_user_id
 		FROM user_family_member ufm
 		JOIN user_family uf ON uf.id = ufm.family_id AND uf.deleted_at IS NULL
 		WHERE ufm.user_id = ? AND ufm.deleted_at IS NULL
 		LIMIT 1
 	`, userID).Scan(&info.FamilyID, &info.OwnerUserID)
 	if err == sql.ErrNoRows {
 		return nil, nil
 	}
 	if err != nil {
 		return nil, err
 	}
 	info.IsOwner = info.OwnerUserID == userID
 	memberCount, err := queryCount(ctx, db, `
 		SELECT COUNT(*)
 		FROM user_family_member
 		WHERE family_id = ? AND deleted_at IS NULL
 	`, info.FamilyID)
 	if err != nil {
 		return nil, err
 	}
 	info.MemberCount = memberCount
 	return &info, nil
 }
 func queryCount(ctx context.Context, db *sql.DB, q string, arg interface{}) (int64, error) {
 	var count int64
 	err := db.QueryRowContext(ctx, q, arg).Scan(&count)
 	return count, err
 }
 func deleteUser(ctx context.Context, db *sql.DB, rdb *redis.Client, summary userSummary) (deleteResult, error) {
 	result := deleteResult{UserID: summary.User.ID}
 	tx, err := db.BeginTx(ctx, nil)
 	if err != nil {
 		return result, err
 	}
 	defer tx.Rollback()
 	if summary.Family != nil {
 		if summary.Family.IsOwner {
 			if res, err := tx.ExecContext(ctx, `DELETE FROM user_family_member WHERE family_id = ?`, summary.Family.FamilyID); err != nil {
 				return result, err
 			} else {
 				result.DeletedDBRows = append(result.DeletedDBRows, fmt.Sprintf("user_family_member=%d", rowsAffected(res)))
 			}
 			if res, err := tx.ExecContext(ctx, `DELETE FROM user_family WHERE id = ?`, summary.Family.FamilyID); err != nil {
 				return result, err
 			} else {
 				result.DeletedDBRows = append(result.DeletedDBRows, fmt.Sprintf("user_family=%d", rowsAffected(res)))
 			}
 		} else {
 			if res, err := tx.ExecContext(ctx, `DELETE FROM user_family_member WHERE user_id = ? AND family_id = ?`, summary.User.ID, summary.Family.FamilyID); err != nil {
 				return result, err
 			} else {
 				result.DeletedDBRows = append(result.DeletedDBRows, fmt.Sprintf("user_family_member=%d", rowsAffected(res)))
 			}
 		}
 	}
 	if res, err := tx.ExecContext(ctx, `DELETE FROM user_auth_methods WHERE user_id = ?`, summary.User.ID); err != nil {
 		return result, err
 	} else { result.DeletedDBRows = append(result.DeletedDBRows, fmt.Sprintf("user_auth_methods=%d", rowsAffected(res))) }
 	if res, err := tx.ExecContext(ctx, `DELETE FROM user_subscribe WHERE user_id = ?`, summary.User.ID); err != nil {
 		return result, err
 	} else { result.DeletedDBRows = append(result.DeletedDBRows, fmt.Sprintf("user_subscribe=%d", rowsAffected(res))) }
 	if res, err := tx.ExecContext(ctx, `DELETE FROM user_device WHERE user_id = ?`, summary.User.ID); err != nil {
 		return result, err
 	} else { result.DeletedDBRows = append(result.DeletedDBRows, fmt.Sprintf("user_device=%d", rowsAffected(res))) }
 	if res, err := tx.ExecContext(ctx, `DELETE FROM user_device_online_record WHERE user_id = ?`, summary.User.ID); err != nil {
 		return result, err
 	} else { result.DeletedDBRows = append(result.DeletedDBRows, fmt.Sprintf("user_device_online_record=%d", rowsAffected(res))) }
 	if res, err := tx.ExecContext(ctx, `DELETE FROM user_withdrawal WHERE user_id = ?`, summary.User.ID); err != nil {
 		return result, err
 	} else { result.DeletedDBRows = append(result.DeletedDBRows, fmt.Sprintf("user_withdrawal=%d", rowsAffected(res))) }
 	if res, err := tx.ExecContext(ctx, "DELETE FROM `order` WHERE user_id = ?", summary.User.ID); err != nil {
 		return result, err
 	} else { result.DeletedDBRows = append(result.DeletedDBRows, fmt.Sprintf("order=%d", rowsAffected(res))) }
 	if res, err := tx.ExecContext(ctx, `DELETE FROM traffic_log WHERE user_id = ?`, summary.User.ID); err != nil {
 		return result, err
 	} else { result.DeletedDBRows = append(result.DeletedDBRows, fmt.Sprintf("traffic_log=%d", rowsAffected(res))) }
 	if res, err := tx.ExecContext(ctx, `DELETE FROM system_logs WHERE object_id = ?`, summary.User.ID); err != nil {
 		return result, err
 	} else { result.DeletedDBRows = append(result.DeletedDBRows, fmt.Sprintf("system_logs=%d", rowsAffected(res))) }
 	var ticketIDs []int64
 	ticketRows, err := tx.QueryContext(ctx, `SELECT id FROM ticket WHERE user_id = ?`, summary.User.ID)
 	if err != nil {
 		return result, err
 	}
 	for ticketRows.Next() {
 		var id int64
 		if err := ticketRows.Scan(&id); err != nil {
 			ticketRows.Close()
 			return result, err
 		}
 		ticketIDs = append(ticketIDs, id)
 	}
 	ticketRows.Close()
 	if len(ticketIDs) > 0 {
 		holders := strings.TrimSuffix(strings.Repeat("?,", len(ticketIDs)), ",")
 		args := make([]interface{}, 0, len(ticketIDs))
 		for _, id := range ticketIDs {
 			args = append(args, id)
 		}
 		if res, err := tx.ExecContext(ctx, "DELETE FROM ticket_follow WHERE ticket_id IN ("+holders+")", args...); err != nil {
 			return result, err
 		} else { result.DeletedDBRows = append(result.DeletedDBRows, fmt.Sprintf("ticket_follow=%d", rowsAffected(res))) }
 	}
 	if res, err := tx.ExecContext(ctx, `DELETE FROM ticket WHERE user_id = ?`, summary.User.ID); err != nil {
 		return result, err
 	} else { result.DeletedDBRows = append(result.DeletedDBRows, fmt.Sprintf("ticket=%d", rowsAffected(res))) }
 	if res, err := tx.ExecContext(ctx, `DELETE FROM apple_iap_transactions WHERE user_id = ?`, summary.User.ID); err != nil {
 		return result, err
 	} else { result.DeletedDBRows = append(result.DeletedDBRows, fmt.Sprintf("apple_iap_transactions=%d", rowsAffected(res))) }
 	if res, err := tx.ExecContext(ctx, `DELETE FROM log_message WHERE user_id = ?`, summary.User.ID); err != nil {
 		return result, err
 	} else { result.DeletedDBRows = append(result.DeletedDBRows, fmt.Sprintf("log_message=%d", rowsAffected(res))) }
 	if res, err := tx.ExecContext(ctx, `DELETE FROM user WHERE id = ?`, summary.User.ID); err != nil {
 		return result, err
 	} else { result.DeletedDBRows = append(result.DeletedDBRows, fmt.Sprintf("user=%d", rowsAffected(res))) }
 	if err := tx.Commit(); err != nil {
 		return result, err
 	}
 	redisKeys, err := cleanupRedis(ctx, rdb, summary)
 	if err != nil {
 		return result, err
 	}
 	result.DeletedRedisKeys = len(redisKeys)
 	sort.Strings(result.DeletedDBRows)
 	return result, nil
 }
 func cleanupRedis(ctx context.Context, rdb *redis.Client, summary userSummary) ([]string, error) {
 	keySet := map[string]struct{}{
 		fmt.Sprintf("cache:user:id:%d", summary.User.ID):                      {},
 		fmt.Sprintf("cache:user:subscribe:user:%d", summary.User.ID):          {},
 		fmt.Sprintf("cache:user:subscribe:user:%d:all", summary.User.ID):      {},
 		fmt.Sprintf("auth:user_sessions:%d", summary.User.ID):                 {},
 	}
 	for _, am := range summary.AuthMethods {
 		if am.AuthType == "email" && am.Identifier != "" {
 			keySet[fmt.Sprintf("cache:user:email:%s", am.Identifier)] = struct{}{}
 		}
 	}
 	for _, sub := range summary.Subscriptions {
 		keySet[fmt.Sprintf("cache:user:subscribe:id:%d", sub.ID)] = struct{}{}
 		if sub.Token != "" {
 			keySet[fmt.Sprintf("cache:user:subscribe:token:%s", sub.Token)] = struct{}{}
 		}
 	}
 	for _, device := range summary.Devices {
 		keySet[fmt.Sprintf("cache:user:device:id:%d", device.ID)] = struct{}{}
 		if device.Identifier != "" {
 			keySet[fmt.Sprintf("cache:user:device:number:%s", device.Identifier)] = struct{}{}
 			keySet[fmt.Sprintf("auth:device_identifier:%s", device.Identifier)] = struct{}{}
 		}
 	}
 	sessionsKey := fmt.Sprintf("auth:user_sessions:%d", summary.User.ID)
 	sessionIDs, err := rdb.ZRange(ctx, sessionsKey, 0, -1).Result()
 	if err != nil && err != redis.Nil {
 		return nil, err
 	}
 	for _, sessionID := range sessionIDs {
 		if sessionID == "" {
 			continue
 		}
 		keySet[fmt.Sprintf("auth:session_id:%s", sessionID)] = struct{}{}
 		keySet[fmt.Sprintf("auth:session_id:detail:%s", sessionID)] = struct{}{}
 	}
 	var cursor uint64
 	for {
 		keys, nextCursor, scanErr := rdb.Scan(ctx, cursor, "auth:session_id:*", 200).Result()
 		if scanErr != nil {
 			return nil, scanErr
 		}
 		for _, key := range keys {
 			if strings.Contains(key, ":detail:") {
 				continue
 			}
 			value, getErr := rdb.Get(ctx, key).Result()
 			if getErr != nil {
 				continue
 			}
 			if value == fmt.Sprintf("%d", summary.User.ID) {
 				keySet[key] = struct{}{}
 				sessionID := strings.TrimPrefix(key, "auth:session_id:")
 				if sessionID != "" {
 					keySet[fmt.Sprintf("auth:session_id:detail:%s", sessionID)] = struct{}{}
 				}
 			}
 		}
 		cursor = nextCursor
 		if cursor == 0 {
 			break
 		}
 	}
 	keys := make([]string, 0, len(keySet))
 	for key := range keySet {
 		keys = append(keys, key)
 	}
 	sort.Strings(keys)
 	if len(keys) == 0 {
 		return keys, nil
 	}
 	if err := rdb.Del(ctx, keys...).Err(); err != nil {
 		return nil, err
 	}
 	return keys, nil
 }
 func rowsAffected(res sql.Result) int64 {
 	if res == nil {
 		return 0
 	}
 	n, err := res.RowsAffected()
 	if err != nil {
 		return 0
 	}
 	return n
 }
@@ -1,3 +0,0 @@
 {
  "extends": ["@commitlint/config-conventional"]
 }
@@ -7,5 +7,12 @@ MYSQL_ROOT_PASSWORD=CHANGE_ME_TO_STRONG_PASSWORD
 # Grafana 管理员密码
 GRAFANA_PASSWORD=CHANGE_ME_TO_STRONG_PASSWORD
-# PPanel Server 镜像标签（留空使用 latest）
+# PPanel Server 镜像标签（由 CI/CD 传入不可变 tag，如 git SHA）
-PPANEL_SERVER_TAG=latest
+PPANEL_SERVER_TAG=CHANGE_ME_TO_GIT_SHA
 # AWS 区域（香港）
 AWS_REGION=ap-east-1
 # Grafana 公开域名（如需反代）
 GRAFANA_DOMAIN=logs-new.hifast.biz
 GRAFANA_ROOT_URL=https://logs-new.hifast.biz
@@ -14,14 +14,15 @@ on:
 env:
  # Docker镜像仓库
  REPO: ${{ vars.REPO || 'registry.kxsw.us/vpn-server' }}
-  # SSH连接信息 (根据分支自动选择)
+  # SSH连接信息 (根据分支自动选择服务器和用户)
  SSH_HOST: ${{ github.ref_name == 'main' && vars.SSH_HOST || vars.DEV_SSH_HOST }}
  SSH_PORT: ${{ vars.SSH_PORT }}
-  SSH_USER: ${{ vars.SSH_USER }}
+  SSH_USER: ${{ github.ref_name == 'main' && 'ubuntu' || 'root' }}
-  SSH_PASSWORD: ${{ github.ref_name == 'main' && vars.SSH_PASSWORD || vars.DEV_SSH_PASSWORD }}
+  # SSH私钥（Gitea Secret 名称：AWS）
  SSH_KEY: ${{ secrets.AWS }}
  # TG通知
-  TG_BOT_TOKEN: 8114337882:AAHkEx03HSu7RxN4IHBJJEnsK9aPPzNLIk0
+  TG_BOT_TOKEN: ${{ secrets.TG_BOT_TOKEN }}
-  TG_CHAT_ID: "-4940243803"
+  TG_CHAT_ID: ${{ secrets.TG_CHAT_ID }}
  # Go构建变量
  SERVICE: vpn
  SERVICE_STYLE: vpn
@@ -49,17 +50,20 @@ jobs:
          if [ "${{ github.ref_name }}" = "main" ]; then
            echo "DOCKER_TAG_SUFFIX=latest" >> $GITHUB_ENV
            echo "CONTAINER_NAME=ppanel-server" >> $GITHUB_ENV
-            echo "DEPLOY_PATH=/root/bindbox" >> $GITHUB_ENV
+            echo "DEPLOY_PATH=/opt/ppanel" >> $GITHUB_ENV
            echo "DEPLOY_ENV_LABEL=🚀 服务已成功部署到生产环境" >> $GITHUB_ENV
            echo "为 main 分支设置生产环境变量"
          elif [ "${{ github.ref_name }}" = "internal" ]; then
            echo "DOCKER_TAG_SUFFIX=internal" >> $GITHUB_ENV
            echo "CONTAINER_NAME=ppanel-server-internal" >> $GITHUB_ENV
            echo "DEPLOY_PATH=/root/bindbox" >> $GITHUB_ENV
            echo "DEPLOY_ENV_LABEL=🧪 服务已成功部署到测试环境" >> $GITHUB_ENV
            echo "为 internal 分支设置开发环境变量"
          else
            echo "DOCKER_TAG_SUFFIX=${{ github.ref_name }}" >> $GITHUB_ENV
            echo "CONTAINER_NAME=ppanel-server-${{ github.ref_name }}" >> $GITHUB_ENV
            echo "DEPLOY_PATH=/root/vpn_server_other" >> $GITHUB_ENV
            echo "DEPLOY_ENV_LABEL=🔧 服务已成功部署到其他环境" >> $GITHUB_ENV
            echo "为其他分支 (${{ github.ref_name }}) 设置环境变量"
          fi
@@ -111,24 +115,37 @@ jobs:
          docker version || true
          echo "客户端 API 版本:" $(docker version --format '{{.Client.APIVersion}}')
-      # 步骤4: 构建并发布到镜像仓库
+      # 步骤4: 构建镜像
-      - name: 📤 构建并发布到镜像仓库
+      - name: 🏗️ 构建镜像
        run: |
-          echo "开始构建并推送镜像..."
+          echo "开始构建镜像..."
          echo "仓库: ${{ env.REPO }}"
          echo "版本标签: ${{ env.VERSION }}"
          echo "分支标签: ${{ env.DOCKER_TAG_SUFFIX }}"
-          # 构建镜像，同时打上版本和分支两个标签
+          BUILD_TAG_ARGS="-t ${{ env.REPO }}:${{ env.VERSION }}"
          if [ "${{ github.event_name }}" = "push" ]; then
            BUILD_TAG_ARGS="$BUILD_TAG_ARGS -t ${{ env.REPO }}:${{ env.DOCKER_TAG_SUFFIX }}"
          else
            echo "PR事件仅构建版本标签，不推送镜像、不部署"
          fi
          docker build -f Dockerfile \
            --platform linux/amd64 \
            --build-arg TARGETARCH=amd64 \
            --build-arg VERSION=${{ env.VERSION }} \
            --build-arg BUILDTIME=${{ env.BUILDTIME }} \
-            -t ${{ env.REPO }}:${{ env.VERSION }} \
+            $BUILD_TAG_ARGS \
            -t ${{ env.REPO }}:${{ env.DOCKER_TAG_SUFFIX }} \
            .
          echo "镜像构建完成"
      # 步骤5: 发布到镜像仓库
      - name: 📤 发布到镜像仓库
        if: github.event_name == 'push'
        run: |
          echo "开始推送镜像..."
          echo "推送版本标签镜像: ${{ env.REPO }}:${{ env.VERSION }}"
          docker push ${{ env.REPO }}:${{ env.VERSION }}
@@ -137,68 +154,151 @@ jobs:
          echo "镜像推送完成"
-      # 调试: 打印 SSH 连接信息
+      # 步骤6: 调试 - 打印部署目标（不输出敏感信息）
-      - name: 🔍 调试 - 打印 SSH 连接信息
+      - name: 🔍 调试 - 打印部署目标
        if: github.event_name == 'push'
        run: |
-          echo "========== SSH 连接信息调试 =========="
+          echo "========== 部署目标调试 =========="
          echo "当前分支: ${{ github.ref_name }}"
          echo "SSH_HOST: ${{ env.SSH_HOST }}"
          echo "SSH_PORT: ${{ env.SSH_PORT }}"
          echo "SSH_USER: ${{ env.SSH_USER }}"
-          echo "SSH_PASSWORD 长度: ${#SSH_PASSWORD}"
+          echo "SSH认证方式: 私钥 (AWS)"
          echo "SSH_PASSWORD 前3位: $(echo "$SSH_PASSWORD" | cut -c1-3)***"
          echo "SSH_PASSWORD 完整值: ${{ env.SSH_PASSWORD }}"
          echo "DEPLOY_PATH: ${{ env.DEPLOY_PATH }}"
          echo "====================================="
-      # 步骤5: 传输配置文件
+      # 步骤7: 传输配置文件
      - name: 📂 传输配置文件
        if: github.event_name == 'push'
        uses: appleboy/scp-action@v0.1.7
        with:
          host: ${{ env.SSH_HOST }}
          username: ${{ env.SSH_USER }}
-          password: ${{ env.SSH_PASSWORD }}
+          key: ${{ env.SSH_KEY }}
          port: ${{ env.SSH_PORT }}
          source: "docker-compose.cloud.yml"
-          target: "${{ env.DEPLOY_PATH }}/"
+          target: "/tmp/ppanel-deploy/"
-      # 步骤6: 连接服务器更新并启动
+      # 步骤8: 连接服务器更新、健康检查并按需回滚
      - name: 🚀 连接服务器更新并启动
        if: github.event_name == 'push'
        uses: appleboy/ssh-action@v1.0.3
        with:
          host: ${{ env.SSH_HOST }}
          username: ${{ env.SSH_USER }}
-          password: ${{ env.SSH_PASSWORD }}
+          key: ${{ env.SSH_KEY }}
          port: ${{ env.SSH_PORT }}
          timeout: 300s
          command_timeout: 600s
          script: |
            set -e
            echo "连接服务器成功，开始部署..."
            echo "部署目录: ${{ env.DEPLOY_PATH }}"
            echo "部署标签: ${{ env.DOCKER_TAG_SUFFIX }}"
            echo "登录用户: ${{ env.SSH_USER }}"
            HEALTHCHECK_URL="http://127.0.0.1:8080/v1/common/heartbeat"
            NEW_TAG="${{ env.DOCKER_TAG_SUFFIX }}"
            ROLLBACK_TAG="rollback-${{ env.VERSION }}"
            SUDO=""
            if [ "${{ github.ref_name }}" = "main" ]; then
              SUDO="sudo"
            fi
            docker_cmd() {
              if [ -n "$SUDO" ]; then
                sudo docker "$@"
              else
                docker "$@"
              fi
            }
            compose_with_tag() {
              tag="$1"
              shift
              if [ -n "$SUDO" ]; then
                sudo env PPANEL_SERVER_TAG="$tag" docker-compose -f docker-compose.cloud.yml "$@"
              else
                PPANEL_SERVER_TAG="$tag" docker-compose -f docker-compose.cloud.yml "$@"
              fi
            }
            write_previous_tag() {
              if [ -n "$SUDO" ]; then
                printf '%s\n' "${PREVIOUS_IMAGE_TAG:-}" | sudo tee .previous-ppanel-image-tag >/dev/null
              else
                printf '%s\n' "${PREVIOUS_IMAGE_TAG:-}" > .previous-ppanel-image-tag
              fi
            }
            health_check() {
              attempt=1
              while [ "$attempt" -le 3 ]; do
                if curl -sf "$HEALTHCHECK_URL"; then
                  echo
                  return 0
                fi
                echo "健康检查第 ${attempt}/3 次失败，10s 后重试..."
                attempt=$((attempt + 1))
                sleep 10
              done
              return 1
            }
            if [ "${{ github.ref_name }}" = "main" ]; then
              sudo mkdir -p ${{ env.DEPLOY_PATH }}
              sudo cp /tmp/ppanel-deploy/docker-compose.cloud.yml ${{ env.DEPLOY_PATH }}/docker-compose.cloud.yml
            else
              mkdir -p ${{ env.DEPLOY_PATH }}
              cp /tmp/ppanel-deploy/docker-compose.cloud.yml ${{ env.DEPLOY_PATH }}/docker-compose.cloud.yml
            fi
            # 进入部署目录
            cd ${{ env.DEPLOY_PATH }}
-            # 创建/更新环境变量文件
+            PREVIOUS_IMAGE_TAG="$(docker_cmd inspect --format '{{.Config.Image}}' ppanel-server 2>/dev/null || true)"
-            # echo "PPANEL_SERVER_TAG=${{ env.DOCKER_TAG_SUFFIX }}" > .env
+            PREVIOUS_IMAGE_ID="$(docker_cmd inspect --format '{{.Image}}' ppanel-server 2>/dev/null || true)"
            echo "上一版本镜像tag: ${PREVIOUS_IMAGE_TAG:-未发现}"
            echo "上一版本镜像ID: ${PREVIOUS_IMAGE_ID:-未发现}"
            write_previous_tag
-            # 拉取最新镜像
+            echo "📥 拉取镜像: ${{ env.REPO }}:${NEW_TAG}"
-            echo "📥 拉取镜像..."
+            compose_with_tag "$NEW_TAG" pull ppanel-server
            docker-compose -f docker-compose.cloud.yml pull ppanel-server
            # 启动服务
            echo "🚀 启动服务..."
-            docker-compose -f docker-compose.cloud.yml up -d ppanel-server
+            compose_with_tag "$NEW_TAG" up -d ppanel-server
            # 清理未使用的镜像
            docker image prune -f || true
            echo "🩺 部署后健康检查: ${HEALTHCHECK_URL}"
            if health_check; then
              docker_cmd image prune -f || true
              echo "✅ 部署后健康检查通过"
              echo "✅ 部署命令执行完成"
              exit 0
            fi
-      # 步骤6: TG通知 (成功)
+            echo "❌ 部署后健康检查连续 3 次失败，开始回滚..."
            if [ -n "$PREVIOUS_IMAGE_ID" ]; then
              docker_cmd tag "$PREVIOUS_IMAGE_ID" "${{ env.REPO }}:${ROLLBACK_TAG}"
              echo "回滚镜像tag: ${{ env.REPO }}:${ROLLBACK_TAG}"
              compose_with_tag "$ROLLBACK_TAG" up -d ppanel-server
              echo "🩺 回滚后健康检查: ${HEALTHCHECK_URL}"
              if health_check; then
                echo "✅ 回滚后健康检查通过"
              else
                echo "❌ 回滚后健康检查仍失败"
              fi
            else
              echo "未找到上一版本镜像ID，无法自动回滚"
            fi
            docker_cmd image prune -f || true
            exit 1
      # 步骤9: TG通知 (成功)
      - name: 📱 发送成功通知到Telegram
-        if: success()
+        if: success() && github.event_name == 'push'
        uses: appleboy/telegram-action@master
        with:
          token: ${{ env.TG_BOT_TOKEN }}
@@ -212,12 +312,13 @@ jobs:
            👤 提交者: ${{ github.actor }}
            🕐 时间: ${{ github.event.head_commit.timestamp }}
-            🚀 服务已成功部署到生产环境
+            ${{ env.DEPLOY_ENV_LABEL }}
            🩺 健康检查: 通过 (http://127.0.0.1:8080/v1/common/heartbeat)
          parse_mode: Markdown
-      # 步骤5: TG通知 (失败)
+      # 步骤10: TG通知 (失败)
      - name: 📱 发送失败通知到Telegram
-        if: failure()
+        if: failure() && github.event_name == 'push'
        uses: appleboy/telegram-action@master
        with:
          token: ${{ env.TG_BOT_TOKEN }}
@@ -231,6 +332,6 @@ jobs:
            👤 提交者: ${{ github.actor }}
            🕐 时间: ${{ github.event.head_commit.timestamp }}
            🩺 健康检查: 失败或未完成；若新版本健康检查连续 3 次失败，已自动尝试回滚并重新检查
            ⚠️ 请检查构建日志获取详细信息
          parse_mode: Markdown
@@ -1,12 +0,0 @@
 <component name="ProjectRunConfigurationManager">
  <configuration default="false" name="go build github.com/perfect-panel/server" type="GoApplicationRunConfiguration" factoryName="Go Application" nameIsGenerated="true">
    <module name="server" />
    <working_directory value="$PROJECT_DIR$" />
    <parameters value="run --config etc/ppanel-dev.yaml" />
    <kind value="PACKAGE" />
    <package value="github.com/perfect-panel/server" />
    <directory value="$PROJECT_DIR$" />
    <filePath value="$PROJECT_DIR$/ppanel.go" />
    <method v="2" />
  </configuration>
 </component>
@@ -1,145 +0,0 @@
 # ppanel-server
 > Multi-agent orchestration framework for agentic coding
 ## Project Overview
 A Claude Flow powered project
 **Tech Stack**: TypeScript, Node.js
 **Architecture**: Domain-Driven Design with bounded contexts
 ## Quick Start
 ### Installation
 ```bash
 npm install
 ```
 ### Build
 ```bash
 npm run build
 ```
 ### Test
 ```bash
 npm test
 ```
 ### Development
 ```bash
 npm run dev
 ```
 ## Agent Coordination
 ### Swarm Configuration
 This project uses hierarchical swarm coordination for complex tasks:
 | Setting | Value | Purpose |
 |---------|-------|---------|
 | Topology | `hierarchical` | Queen-led coordination (anti-drift) |
 | Max Agents | 8 | Optimal team size |
 | Strategy | `specialized` | Clear role boundaries |
 | Consensus | `raft` | Leader-based consistency |
 ### When to Use Swarms
 **Invoke swarm for:**
 - Multi-file changes (3+ files)
 - New feature implementation
 - Cross-module refactoring
 - API changes with tests
 - Security-related changes
 - Performance optimization
 **Skip swarm for:**
 - Single file edits
 - Simple bug fixes (1-2 lines)
 - Documentation updates
 - Configuration changes
 ### Available Skills
 Use `$skill-name` syntax to invoke:
 | Skill | Use Case |
 |-------|----------|
 | `$swarm-orchestration` | Multi-agent task coordination |
 | `$memory-management` | Pattern storage and retrieval |
 | `$sparc-methodology` | Structured development workflow |
 | `$security-audit` | Security scanning and CVE detection |
 ### Agent Types
 | Type | Role | Use Case |
 |------|------|----------|
 | `researcher` | Requirements analysis | Understanding scope |
 | `architect` | System design | Planning structure |
 | `coder` | Implementation | Writing code |
 | `tester` | Test creation | Quality assurance |
 | `reviewer` | Code review | Security and quality |
 ## Code Standards
 ### File Organization
 - **NEVER** save to root folder
 - `/src` - Source code files
 - `/tests` - Test files
 - `/docs` - Documentation
 - `/config` - Configuration files
 ### Quality Rules
 - Files under 500 lines
 - No hardcoded secrets
 - Input validation at boundaries
 - Typed interfaces for public APIs
 - TDD London School (mock-first) preferred
 ### Commit Messages
 ```
 <type>(<scope>): <description>
 [optional body]
 Co-Authored-By: claude-flow <ruv@ruv.net>
 ```
 Types: `feat`, `fix`, `docs`, `style`, `refactor`, `perf`, `test`, `chore`
 ## Security
 ### Critical Rules
 - NEVER commit secrets, credentials, or .env files
 - NEVER hardcode API keys
 - Always validate user input
 - Use parameterized queries for SQL
 - Sanitize output to prevent XSS
 ### Path Security
 - Validate all file paths
 - Prevent directory traversal (../)
 - Use absolute paths internally
 ## Memory System
 ### Storing Patterns
 ```bash
 npx @claude-flow/cli memory store \
  --key "pattern-name" \
  --value "pattern description" \
  --namespace patterns
 ```
 ### Searching Memory
 ```bash
 npx @claude-flow/cli memory search \
  --query "search terms" \
  --namespace patterns
 ```
 ## Links
 - Documentation: https://github.com/ruvnet/claude-flow
 - Issues: https://github.com/ruvnet/claude-flow/issues
@@ -28,7 +28,7 @@ FROM scratch
 # Copy CA certificates and timezone data
 COPY --from=builder /etc/ssl/certs/ca-certificates.crt /etc/ssl/certs/
-COPY --from=builder /usr/share/zoneinfo/Asia/Shanghai /usr/share/zoneinfo/Asia/Shanghai
+COPY --from=builder /usr/share/zoneinfo /usr/share/zoneinfo
 ENV TZ=Asia/Shanghai
@@ -0,0 +1,76 @@
 upstream api_backend {
    server 127.0.0.1:8080;
 }
 server {
    listen 80;
    server_name api.hifast.biz  4d3vsw888xgaen.hifast.biz;
    location /.well-known/acme-challenge/ {
        root /var/www/html;
        allow all;
    }
    location / {
        return 301 https://$host$request_uri;
    }
 }
 server {
    listen 443 ssl http2;
    server_name api.hifast.biz;
    client_max_body_size 150M;
    ssl_certificate /etc/nginx/ssl/hifast.biz/_.hifast.biz.pem; # managed by Certbot
    ssl_certificate_key /etc/nginx/ssl/hifast.biz/_.hifast.biz.key; # managed by Certbot
    add_header Strict-Transport-Security "max-age=31536000; includeSubDomains" always;
    add_header X-Frame-Options "DENY";
    add_header X-Content-Type-Options nosniff;
    if ($http_user_agent ~* '(9999|91\.78)') {
        return 444;
    }
    location ~ ^/v1/common/client/download/file/(?<download_name>Hi快VPN-(?<os>windows|mac|android)-1.0.0-ic-.*\.(?<ext>exe|dmg|apk))$ {
    alias /var/www/download/Hi快VPN-$os-1.0.0.$ext;
    charset utf-8;
    add_header Content-Disposition 'attachment; filename="$download_name"';
    add_header Content-Type application/octet-stream;
    }
    location /v1/common/client/download/file/ {
        alias /var/www/download/;
    }
    location / {
        proxy_pass http://api_backend;
        proxy_set_header Host $host;
        proxy_set_header X-Real-IP $remote_addr;
        proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
        proxy_set_header X-Forwarded-Proto $scheme;
        proxy_http_version 1.1;
        proxy_set_header Upgrade $http_upgrade;
        proxy_set_header Connection "upgrade";
    }
 }
 server {
    listen 443 ssl http2;
    server_name 4d3vsw888xgaen.hifast.biz;
    client_max_body_size 150M;
    ssl_certificate /etc/nginx/ssl/hifast.biz/_.hifast.biz.pem; # managed by Certbot
    ssl_certificate_key /etc/nginx/ssl/hifast.biz/_.hifast.biz.key; # managed by Certbot
    add_header Strict-Transport-Security "max-age=31536000; includeSubDomains" always;
    add_header X-Frame-Options DENY;
    add_header X-Content-Type-Options nosniff;
    gzip on;
    gzip_vary on;
    gzip_min_length 1024;
    gzip_types text/plain text/css text/xml text/javascript application/javascript application/xml+rss application/json image/svg+xml;
    root /var/www/admin;
    location / {
        try_files $uri $uri/ /index.html;
    }
 }
@@ -149,6 +149,35 @@ type (
 		Total int64           `json:"total"`
 		List  []CommissionLog `json:"list"`
 	}
 	OrderRefundLog {
 		OrderId                int64  `json:"order_id"`
 		OrderNo                string `json:"order_no"`
 		OperatorUserId         int64  `json:"operator_user_id"`
 		OperatorAuthIdentifier string `json:"operator_auth_identifier,omitempty"`
 		TargetUserId           int64  `json:"target_user_id"`
 		UserSubscribeId        int64  `json:"user_subscribe_id"`
 		RefererUserId          int64  `json:"referer_user_id,omitempty"`
 		CommissionAmount       int64  `json:"commission_amount"`
 		Reason                 string `json:"reason,omitempty"`
 		OrderStatusBefore      uint8  `json:"order_status_before"`
 		OrderStatusAfter       uint8  `json:"order_status_after"`
 		SubscribeStatusBefore  uint8  `json:"subscribe_status_before"`
 		SubscribeStatusAfter   uint8  `json:"subscribe_status_after"`
 		SubscribeExpireBefore  int64  `json:"subscribe_expire_before"`
 		SubscribeExpireAfter   int64  `json:"subscribe_expire_after"`
 		CommissionBefore       int64  `json:"commission_before"`
 		CommissionAfter        int64  `json:"commission_after"`
 		Timestamp              int64  `json:"timestamp"`
 	}
 	FilterOrderRefundLogRequest {
 		FilterLogParams
 		OrderId int64 `form:"order_id,optional"`
 		UserId  int64 `form:"user_id,optional"`
 	}
 	FilterOrderRefundLogResponse {
 		Total int64            `json:"total"`
 		List  []OrderRefundLog `json:"list"`
 	}
 	GiftLog {
 		Type        uint16 `json:"type"`
 		userId      int64  `json:"user_id"`
@@ -239,6 +268,30 @@ type (
 		OccurredAt int64  `json:"occurred_at"`
 		CreatedAt  int64  `json:"created_at"`
 	}
 	GetLogMessageRawRequest {
 		Id int64 `form:"id" validate:"required"`
 	}
 	GetLogMessageRawResponse {
 		Id         int64       `json:"id"`
 		Platform   string      `json:"platform"`
 		AppVersion string      `json:"app_version"`
 		OsName     string      `json:"os_name"`
 		OsVersion  string      `json:"os_version"`
 		DeviceId   string      `json:"device_id"`
 		UserId     *int64      `json:"user_id"`
 		SessionId  string      `json:"session_id"`
 		Level      uint8       `json:"level"`
 		ErrorCode  string      `json:"error_code"`
 		Message    string      `json:"message"`
 		Stack      string      `json:"stack"`
 		Context    interface{} `json:"context"`
 		ClientIP   string      `json:"client_ip"`
 		UserAgent  string      `json:"user_agent"`
 		Locale     string      `json:"locale"`
 		Digest     string      `json:"digest"`
 		OccurredAt int64       `json:"occurred_at"`
 		CreatedAt  int64       `json:"created_at"`
 	}
 )
@server (
@@ -291,6 +344,10 @@ service ppanel {
 	@handler FilterCommissionLog
 	get /commission/list (FilterCommissionLogRequest) returns (FilterCommissionLogResponse)
 	@doc "Filter order refund log"
 	@handler FilterOrderRefundLog
 	get /order/refund/list (FilterOrderRefundLogRequest) returns (FilterOrderRefundLogResponse)
 	@doc "Filter gift log"
 	@handler FilterGiftLog
 	get /gift/list (FilterGiftLogRequest) returns (FilterGiftLogResponse)
@@ -314,5 +371,9 @@ service ppanel {
 	@doc "Get error log message detail"
 	@handler GetErrorLogMessageDetail
 	get /error_message/detail returns (GetErrorLogMessageDetailResponse)
 	@doc "Get log message raw detail (temporary)"
 	@handler GetLogMessageRaw
 	get /message/detail (GetLogMessageRawRequest) returns (GetLogMessageRawResponse)
 }
@@ -33,6 +33,10 @@ type (
 		PaymentId int64  `json:"payment_id,omitempty"`
 		TradeNo   string `json:"trade_no,omitempty"`
 	}
 	RefundOrderRequest {
 		Id     int64  `json:"id" validate:"required"`
 		Reason string `json:"reason,omitempty" validate:"omitempty,max=500"`
 	}
 	ActivateOrderRequest {
 		OrderNo string `json:"order_no" validate:"required"`
 	}
@@ -68,6 +72,10 @@ service ppanel {
 	@handler UpdateOrderStatus
 	put /status (UpdateOrderStatusRequest)
 	@doc "Refund order"
 	@handler RefundOrder
 	post /refund (RefundOrderRequest)
 	@doc "Manually activate order"
 	@handler ActivateOrder
 	post /activate (ActivateOrderRequest)
@@ -41,17 +41,17 @@ type (
 		UserId             int64   `json:"user_id" validate:"required"`
 		Password           string  `json:"password"`
 		Avatar             string  `json:"avatar"`
-		Balance            int64  `json:"balance"`
+		Balance            *int64  `json:"balance"`
-		Commission         int64  `json:"commission"`
+		Commission         *int64  `json:"commission"`
 		ReferralPercentage uint8   `json:"referral_percentage"`
-		OnlyFirstPurchase  bool   `json:"only_first_purchase"`
+		OnlyFirstPurchase  *bool   `json:"only_first_purchase"`
-		GiftAmount         int64  `json:"gift_amount"`
+		GiftAmount         *int64  `json:"gift_amount"`
 		Telegram           int64   `json:"telegram"`
 		ReferCode          string  `json:"refer_code"`
-		RefererId          int64  `json:"referer_id"`
+		RefererId          *int64  `json:"referer_id"`
-		Enable             bool   `json:"enable"`
+		Enable             *bool   `json:"enable"`
-		IsAdmin            bool   `json:"is_admin"`
+		IsAdmin            *bool   `json:"is_admin"`
-		Remark             string `json:"remark"`
+		Remark             *string `json:"remark"`
 	}
 	UpdateUserNotifySettingRequest {
 		UserId                int64 `json:"user_id" validate:"required"`
@@ -230,6 +230,24 @@ type (
 		FamilyId int64  `json:"family_id" validate:"required,gt=0"`
 		Reason   string `json:"reason,omitempty"`
 	}
 	GetWithdrawalListRequest {
 		Page   int    `form:"page"`
 		Size   int    `form:"size"`
 		UserId *int64 `form:"user_id,omitempty"`
 		Status *uint8 `form:"status,omitempty"`
 		Method *uint8 `form:"method,omitempty"`
 	}
 	GetWithdrawalListResponse {
 		List  []WithdrawalLog `json:"list"`
 		Total int64           `json:"total"`
 	}
 	ApproveWithdrawalRequest {
 		WithdrawalId int64 `json:"withdrawal_id" validate:"required,gt=0"`
 	}
 	RejectWithdrawalRequest {
 		WithdrawalId int64  `json:"withdrawal_id" validate:"required,gt=0"`
 		Reason       string `json:"reason" validate:"required,max=500"`
 	}
 )
@server (
@@ -370,5 +388,16 @@ service ppanel {
 	@doc "Dissolve family"
 	@handler DissolveFamily
 	put /family/dissolve (DissolveFamilyRequest)
 }
 	@doc "Get withdrawal list"
 	@handler GetWithdrawalList
 	get /withdrawal/list (GetWithdrawalListRequest) returns (GetWithdrawalListResponse)
 	@doc "Approve withdrawal"
 	@handler ApproveWithdrawal
 	post /withdrawal/approve (ApproveWithdrawalRequest)
 	@doc "Reject withdrawal"
 	@handler RejectWithdrawal
 	post /withdrawal/reject (RejectWithdrawalRequest)
 }
@@ -154,6 +154,7 @@ type (
 		UserAgent   string `json:"user_agent" validate:"required"`
 		CfToken     string `json:"cf_token,optional"`
 		ShortCode   string `json:"short_code,optional"`
 		BasePayload string `json:"base_payload,optional"`
 	}
 	GenerateCaptchaResponse {
 		Id         string `json:"id"`
@@ -0,0 +1,76 @@
 syntax = "v1"
 info (
 	title:   "File API"
 	desc:    "API for ppanel file upload"
 	author:  "Codex"
 	email:   "codex@openai.com"
 	version: "0.0.1"
 )
 import "../types.api"
 type (
 	FileUploadRequest {
 		BizType string `form:"biz_type" validate:"required"`
 	}
 	FileUploadResponse {
 		FileId      string `json:"file_id"`
 		FileName    string `json:"file_name"`
 		ObjectKey   string `json:"object_key"`
 		Size        int64  `json:"size"`
 		ContentType string `json:"content_type"`
 		Etag        string `json:"etag"`
 		Status      string `json:"status"`
 	}
 	FileUploadInitRequest {
 		BizType     string `json:"biz_type" validate:"required"`
 		FileName    string `json:"file_name" validate:"required"`
 		ContentType string `json:"content_type" validate:"required"`
 		Size        int64  `json:"size" validate:"required"`
 		Sha256      string `json:"sha256"`
 	}
 	FileUploadInitResponse {
 		FileId     string            `json:"file_id"`
 		ObjectKey  string            `json:"object_key"`
 		UploadURL  string            `json:"upload_url"`
 		Method     string            `json:"method"`
 		Headers    map[string]string `json:"headers"`
 		ExpiredAt  int64             `json:"expired_at"`
 	}
 	FileUploadCompleteRequest {
 		FileId string `json:"file_id" validate:"required"`
 	}
 	FileUploadCompleteResponse {
 		FileId      string `json:"file_id"`
 		ObjectKey   string `json:"object_key"`
 		Size        int64  `json:"size"`
 		ContentType string `json:"content_type"`
 		Etag        string `json:"etag"`
 		Status      string `json:"status"`
 	}
 )
@server (
 	prefix:     v1/public/file
 	group:      public/file
 	middleware: AuthMiddleware,DeviceMiddleware
 )
 service ppanel {
 	@doc "Upload file to RustFS"
 	@handler FileUpload
 	post /upload (FileUploadRequest) returns (FileUploadResponse)
 	@doc "Init file upload"
 	@handler FileUploadInit
 	post /upload/init (FileUploadInitRequest) returns (FileUploadInitResponse)
 	@doc "Complete file upload"
 	@handler FileUploadComplete
 	post /upload/complete (FileUploadCompleteRequest) returns (FileUploadCompleteResponse)
 }
@@ -0,0 +1,41 @@
 syntax = "v1"
 info (
 	title:   "recovery API"
 	desc:    "API for order recovery"
 	author:  "Tension"
 	email:   "tension@ppanel.com"
 	version: "0.0.1"
 )
 import "../types.api"
 type (
 	RecoverySendCodeRequest {
 		Email string `json:"email" validate:"required,email"`
 	}
 	RecoverOrderRequest {
 		OrderNo string `json:"order_no" validate:"required"`
 		Email   string `json:"email" validate:"required,email"`
 		Code    string `json:"code" validate:"required"`
 	}
 	RecoverOrderResponse {
 		Success bool   `json:"success"`
 		Message string `json:"message"`
 	}
 )
@server (
 	prefix:     v1/public/recovery
 	group:      public/recovery
 	middleware: DeviceMiddleware
 )
 service ppanel {
 	@doc "Send recovery verification code"
 	@handler SendCode
 	post /send_code (RecoverySendCodeRequest) returns (SendCodeResponse)
 	@doc "Recover order subscription"
 	@handler RecoverOrder
 	post /order (RecoverOrderRequest) returns (RecoverOrderResponse)
 }
@@ -111,6 +111,9 @@ type (
 	CommissionWithdrawRequest {
 		Amount    int64  `json:"amount"`
 		Content   string `json:"content"`
 		Method    uint8  `json:"method" validate:"oneof=0 1 2 3"`
 		Account   string `json:"account,omitempty"`
 		QrCodeUrl string `json:"qr_code_url,omitempty"`
 	}
 	WithdrawalLog {
 		Id        int64  `json:"id"`
@@ -119,9 +122,15 @@ type (
 		Content   string `json:"content"`
 		Status    uint8  `json:"status"`
 		Reason    string `json:"reason,omitempty"`
 		Method    uint8  `json:"method"`
 		Account   string `json:"account"`
 		QrCodeUrl string `json:"qr_code_url"`
 		CreatedAt int64  `json:"created_at"`
 		UpdatedAt int64  `json:"updated_at"`
 	}
 	CancelWithdrawalRequest {
 		WithdrawalId int64 `json:"withdrawal_id" validate:"required,gt=0"`
 	}
 	QueryWithdrawalLogListRequest {
 		Page int `form:"page"`
 		Size int `form:"size"`
@@ -352,6 +361,10 @@ service ppanel {
 	@handler CommissionWithdraw
 	post /commission_withdraw (CommissionWithdrawRequest) returns (WithdrawalLog)
 	@doc "Cancel pending withdrawal"
 	@handler CancelWithdrawal
 	post /withdrawal_cancel (CancelWithdrawalRequest) returns (WithdrawalLog)
 	@doc "Query Withdrawal Log"
 	@handler QueryWithdrawalLog
 	get /withdrawal_log (QueryWithdrawalLogListRequest) returns (QueryWithdrawalLogListResponse)
@@ -27,6 +27,7 @@ type (
 		EnableLoginNotify     bool             `json:"enable_login_notify"`
 		EnableSubscribeNotify bool             `json:"enable_subscribe_notify"`
 		EnableTradeNotify     bool             `json:"enable_trade_notify"`
 		UseStatus             bool             `json:"use_status"` // Whether to show the "bind email to get free trial" prompt
 		AuthMethods           []UserAuthMethod `json:"auth_methods"`
 		UserDevices           []UserDevice     `json:"user_devices"`
 		Rules                 []string         `json:"rules"`
@@ -227,6 +228,7 @@ type (
 	SubscribeDiscount {
 		Quantity int64   `json:"quantity"`
 		Discount float64 `json:"discount"`
 		MapApple string  `json:"map_apple"`
 	}
 	TrafficLimit {
 		StatType     string `json:"stat_type"`
@@ -425,6 +427,7 @@ type (
 		FeeAmount      int64         `json:"fee_amount"`
 		TradeNo        string        `json:"trade_no"`
 		Status         uint8         `json:"status"`
 		StatusName     string        `json:"status_name,omitempty"`
 		SubscribeId    int64         `json:"subscribe_id"`
 		CreatedAt      int64         `json:"created_at"`
 		UpdatedAt      int64         `json:"updated_at"`
@@ -447,6 +450,7 @@ type (
 		FeeAmount      int64         `json:"fee_amount"`
 		TradeNo        string        `json:"trade_no"`
 		Status         uint8         `json:"status"`
 		StatusName     string        `json:"status_name,omitempty"`
 		SubscribeId    int64         `json:"subscribe_id"`
 		Subscribe      Subscribe     `json:"subscribe"`
 		CreatedAt      int64         `json:"created_at"`
@@ -1003,4 +1007,3 @@ type (
 		ConfigSnapshot map[string]interface{} `json:"config_snapshot,omitempty"`
 	}
 )
@@ -0,0 +1,796 @@
 package cmd
 import (
 	"bufio"
 	"context"
 	"fmt"
 	"io"
 	"math/rand"
 	"os"
 	"sort"
 	"strconv"
 	"strings"
 	"time"
 	"github.com/perfect-panel/server/internal/config"
 	logmodel "github.com/perfect-panel/server/internal/model/log"
 	ordermodel "github.com/perfect-panel/server/internal/model/order"
 	usermodel "github.com/perfect-panel/server/internal/model/user"
 	"github.com/perfect-panel/server/pkg/conf"
 	"github.com/perfect-panel/server/pkg/orm"
 	"github.com/redis/go-redis/v9"
 	"github.com/spf13/cobra"
 	"gorm.io/gorm"
 )
 func init() {
 	retroactiveReferralCmd.Flags().StringVar(&retroAgentIdStr, "agent-id", "", "目标代理用户 ID，或 * 表示所有 referral_percentage>0 的代理（必填）")
 	retroactiveReferralCmd.Flags().StringVar(&retroPoolStart, "pool-start", "2025-05-04", "自然流量订单起始时间，格式 YYYY-MM-DD 或 'YYYY-MM-DD HH:MM:SS'（默认 2025-05-04）")
 	retroactiveReferralCmd.Flags().StringVar(&retroPoolEnd, "pool-end", "", "自然流量订单截止时间，格式 YYYY-MM-DD 或 'YYYY-MM-DD HH:MM:SS'（默认今天）")
 	retroactiveReferralCmd.Flags().IntVar(&retroPercentage, "percentage", 120, "补偿百分比，例如 120 表示 120%（默认 120）")
 	retroactiveReferralCmd.Flags().IntVar(&retroForceCommissionPct, "force-commission-pct", 50, "强制指定发佣比例（0=使用数据库/配置，非0时覆盖代理设置，默认 50%）")
 	retroactiveReferralCmd.Flags().StringVar(&retroOutput, "output", "retro_result.txt", "结果输出到指定 txt 文件（默认 retro_result.txt）")
 	retroactiveReferralCmd.Flags().StringVar(&retroConfigPath, "config", "etc/ppanel.yaml", "配置文件路径")
 	retroactiveReferralCmd.Flags().StringVar(&retroAgentCreatedAfter, "agent-created-after", "", "仅处理在此日期之后注册的代理，格式 YYYY-MM-DD（留空=不限）")
 	retroactiveReferralCmd.Flags().StringVar(&retroLossStart, "loss-start", "2026-05-06", "数据丢失起始时间，丢失时长=现在-此时间（默认 2026-05-06）")
 	retroactiveReferralCmd.Flags().StringVar(&retroOrderStart, "order-start", "2026-05-01", "池内用户至少有一笔 updated_at >= 此时间的订单才入池（默认 2026-05-01）")
 	retroactiveReferralCmd.Flags().BoolVar(&retroDryRun, "dry-run", false, "仅预览，不执行写入")
 	rootCmd.AddCommand(retroactiveReferralCmd)
 }
 var (
 	retroAgentIdStr         string
 	retroAgentCreatedAfter  string
 	retroLossStart          string
 	retroOrderStart         string
 	retroPoolStart          string
 	retroPoolEnd            string
 	retroPercentage         int
 	retroForceCommissionPct int
 	retroConfigPath         string
 	retroDryRun             bool
 	retroOutput             string
 )
 var retroactiveReferralCmd = &cobra.Command{
 	Use:   "retro-referral",
 	Short: "补单：按代理历史日均佣金补偿指定比例的用户",
 	Long: `统计代理从首次邀请到 pool-end 的日均佣金，
 按指定百分比计算目标补偿金额，
 从 pool-start 到 pool-end 的自然流量用户中随机抽取匹配的用户数量挂载到该代理。
 --agent-id 支持单个 ID 或 *（处理所有 referral_percentage>0 的代理）。`,
 	RunE: func(cmd *cobra.Command, args []string) error {
 		if retroAgentIdStr == "" {
 			return fmt.Errorf("--agent-id 必填（单个 ID 或 *）")
 		}
 		return runRetroactiveReferral()
 	},
 }
 // commissionRule holds resolved commission settings for an agent.
 type commissionRule struct {
 	Percentage        uint8
 	OnlyFirstPurchase bool
 }
 // candidateUser holds a pool user plus their pre-calculated qualifying orders.
 type candidateUser struct {
 	Id              int64
 	CreatedAt       time.Time
 	Identifier      string
 	Orders          []ordermodel.Order
 	CommissionTotal int64
 }
 // agentPlan holds one agent's computed allocation plan (preview phase output).
 type agentPlan struct {
 	Agent         *usermodel.User
 	Rule          commissionRule
 	Selected      []candidateUser
 	TargetAmt     float64 // in cents
 	PreviewCommission int64
 }
 func runRetroactiveReferral() error {
 	// ── 0. 初始化输出（终端 + 可选文件）─────────────────────────
 	var w io.Writer = os.Stdout
 	if retroOutput != "" {
 		f, err := os.Create(retroOutput)
 		if err != nil {
 			return fmt.Errorf("创建输出文件失败: %w", err)
 		}
 		defer f.Close()
 		w = io.MultiWriter(os.Stdout, f)
 		fmt.Printf("结果将同步写入: %s\n\n", retroOutput)
 	}
 	// ── 1. 加载配置 ──────────────────────────────────────────────
 	var c config.Config
 	conf.MustLoad(retroConfigPath, &c)
 	// ── 2. 初始化 DB + Redis ──────────────────────────────────────
 	db, err := orm.ConnectMysql(orm.Mysql{Config: c.MySQL})
 	if err != nil {
 		return fmt.Errorf("连接数据库失败: %w", err)
 	}
 	rds := redis.NewClient(&redis.Options{
 		Addr:     c.Redis.Host,
 		Password: c.Redis.Pass,
 		DB:       c.Redis.DB,
 	})
 	ctx := context.Background()
 	if err = rds.Ping(ctx).Err(); err != nil {
 		return fmt.Errorf("连接 Redis 失败: %w", err)
 	}
 	um := usermodel.NewModel(db, rds)
 	// ── 3. 解析时间参数 ───────────────────────────────────────────
 	poolStart, err := parseFlexibleTime(retroPoolStart)
 	if err != nil {
 		return fmt.Errorf("--pool-start 格式错误: %w", err)
 	}
 	poolEnd := time.Now()
 	if retroPoolEnd != "" {
 		poolEnd, err = parseFlexibleTime(retroPoolEnd)
 		if err != nil {
 			return fmt.Errorf("--pool-end 格式错误: %w", err)
 		}
 	}
 	if !poolEnd.After(poolStart) {
 		return fmt.Errorf("--pool-end 必须晚于 --pool-start")
 	}
 	// ── 4. 确定代理列表 ───────────────────────────────────────────
 	var agents []*usermodel.User
 	if retroAgentIdStr == "*" {
 		agents, err = queryAllActiveAgents(ctx, db, retroAgentCreatedAfter)
 		if err != nil {
 			return fmt.Errorf("查询代理列表失败: %w", err)
 		}
 		if len(agents) == 0 {
 			return fmt.Errorf("没有找到任何 referral_percentage>0 的代理用户")
 		}
 		fmt.Fprintf(w, "模式：全量代理，共找到 %d 个代理（referral_percentage>0）\n\n", len(agents))
 	} else {
 		agentID, parseErr := strconv.ParseInt(retroAgentIdStr, 10, 64)
 		if parseErr != nil || agentID <= 0 {
 			return fmt.Errorf("--agent-id 必须是正整数或 *")
 		}
 		agent, findErr := um.FindOne(ctx, agentID)
 		if findErr != nil {
 			return fmt.Errorf("查询代理用户失败: %w", findErr)
 		}
 		if agent.DeletedAt.Valid {
 			return fmt.Errorf("代理用户 %d 已被删除", agentID)
 		}
 		agents = []*usermodel.User{agent}
 	}
 	if retroForceCommissionPct > 0 {
 		fmt.Fprintf(w, "⚠️  强制覆盖所有代理佣金比例为 %d%%\n\n", retroForceCommissionPct)
 	}
 	// ── 5. 查询自然流量用户池（所有代理共用同一个池）────────────
 	// 先用 50% 规则（或强制值）预加载池，以便预览；执行时每个代理用自身规则
 	var orderStart time.Time
 	if retroOrderStart != "" {
 		orderStart, err = parseFlexibleTime(retroOrderStart)
 		if err != nil {
 			return fmt.Errorf("--order-start 格式错误: %w", err)
 		}
 	}
 	previewRule := commissionRule{Percentage: uint8(retroForceCommissionPct), OnlyFirstPurchase: false}
 	pool, err := queryNaturalTrafficPool(ctx, db, poolStart, poolEnd, orderStart, previewRule)
 	if err != nil {
 		return fmt.Errorf("查询自然流量用户池失败: %w", err)
 	}
 	orderStartDesc := ""
 	if !orderStart.IsZero() {
 		orderStartDesc = fmt.Sprintf("，订单 updated_at >= %s", orderStart.Format("2006-01-02"))
 	}
 	fmt.Fprintf(w, "自然流量用户池（%s ~ %s，referer_id=0，有已支付订单%s）：共 %d 人\n\n",
 		poolStart.Format("2006-01-02 15:04"), poolEnd.Format("2006-01-02 15:04"), orderStartDesc, len(pool))
 	if len(pool) == 0 {
 		return fmt.Errorf("自然流量用户池为空，无法补充")
 	}
 	// ── 6. 逐代理生成分配计划（预览阶段）────────────────────────
 	// 池按顺序分配：每个代理从剩余池中取用户，避免重复分配
 	remainingPool := make([]candidateUser, len(pool))
 	copy(remainingPool, pool)
 	plans := make([]agentPlan, 0, len(agents))
 	for _, agent := range agents {
 		plan, planErr := buildAgentPlan(w, ctx, db, c, agent, remainingPool, poolEnd)
 		if planErr != nil {
 			fmt.Fprintf(w, "⚠️  代理 %d 跳过: %v\n\n", agent.Id, planErr)
 			continue
 		}
 		// 从剩余池中移除已分配给该代理的用户
 		assignedSet := make(map[int64]struct{}, len(plan.Selected))
 		for _, u := range plan.Selected {
 			assignedSet[u.Id] = struct{}{}
 		}
 		newRemaining := remainingPool[:0]
 		for _, u := range remainingPool {
 			if _, used := assignedSet[u.Id]; !used {
 				newRemaining = append(newRemaining, u)
 			}
 		}
 		remainingPool = newRemaining
 		plans = append(plans, plan)
 	}
 	if len(plans) == 0 {
 		return fmt.Errorf("所有代理均无法生成分配计划")
 	}
 	// ── 7. 汇总预览 ───────────────────────────────────────────────
 	var grandTotalUsers int
 	var grandTotalCommission int64
 	var grandTargetAmt float64
 	for _, p := range plans {
 		grandTotalUsers += len(p.Selected)
 		grandTotalCommission += p.PreviewCommission
 		grandTargetAmt += p.TargetAmt
 	}
 	fmt.Fprintln(w, strings.Repeat("═", 75))
 	fmt.Fprintf(w, "汇总：共 %d 个代理，补充 %d 个用户\n", len(plans), grandTotalUsers)
 	fmt.Fprintf(w, "     预计追溯佣金总额: $%.2f（目标补偿金额: $%.2f）\n",
 		float64(grandTotalCommission)/100, grandTargetAmt/100)
 	fmt.Fprintln(w, strings.Repeat("═", 75))
 	fmt.Fprintln(w)
 	if retroDryRun {
 		fmt.Fprintln(w, "[dry-run] 预览完成，未执行任何写入。")
 		return nil
 	}
 	// ── 8. 执行前汇总打印 ─────────────────────────────────────────
 	fmt.Fprintf(w, "\n┌─────────────────────────────────────────────┐\n")
 	fmt.Fprintf(w, "│  即将写入数据库                              │\n")
 	fmt.Fprintf(w, "│  代理数量  : %-4d 个                         │\n", len(plans))
 	fmt.Fprintf(w, "│  补充用户  : %-4d 人                         │\n", grandTotalUsers)
 	fmt.Fprintf(w, "│  赠送金额  : $%-10.2f                    │\n", float64(grandTotalCommission)/100)
 	fmt.Fprintf(w, "└─────────────────────────────────────────────┘\n\n")
 	fmt.Printf("确认执行？(yes/no): ")
 	reader := bufio.NewReader(os.Stdin)
 	answer, _ := reader.ReadString('\n')
 	answer = strings.TrimSpace(strings.ToLower(answer))
 	if answer != "yes" && answer != "y" {
 		fmt.Fprintln(w, "已取消。")
 		return nil
 	}
 	// ── 9. 逐代理执行 ─────────────────────────────────────────────
 	var totalSuccess, totalFailed int
 	var totalCreditedOrder, totalCreditedAmt int64
 	for _, plan := range plans {
 		fmt.Fprintf(w, "\n── 执行代理 %d ──────────────────────────────────────────────\n", plan.Agent.Id)
 		var sc, fc int
 		var co, ca int64
 		for _, eu := range plan.Selected {
 			if eu.Id == plan.Agent.Id {
 				fmt.Fprintf(w, "[SKIP] 用户 %d 与代理相同，跳过\n", eu.Id)
 				fc++
 				continue
 			}
 			credited, amount, execErr := processOneUser(ctx, db, um, plan.Agent.Id, eu.Id, plan.Rule, orderStart)
 			if execErr != nil {
 				fmt.Fprintf(w, "[FAIL] 用户 %d: %v\n", eu.Id, execErr)
 				fc++
 				continue
 			}
 			fmt.Fprintf(w, "[OK]   用户 %d → 代理 %d，发佣 %d 单，金额 $%.2f\n",
 				eu.Id, plan.Agent.Id, credited, float64(amount)/100)
 			sc++
 			co += credited
 			ca += amount
 		}
 		// 直接删除代理的缓存 key，下次请求时从 DB 重新加载（避免 FindOne 读到旧缓存再写回）
 		if sc > 0 {
 			cacheKey := fmt.Sprintf("cache:user:id:%d", plan.Agent.Id)
 			_ = rds.Del(ctx, cacheKey).Err()
 		}
 		fmt.Fprintf(w, "  代理 %d 小计：成功 %d 人，失败 %d 人，佣金 $%.2f\n", plan.Agent.Id, sc, fc, float64(ca)/100)
 		totalSuccess += sc
 		totalFailed += fc
 		totalCreditedOrder += co
 		totalCreditedAmt += ca
 	}
 	// ── 10. 全局汇总 ──────────────────────────────────────────────
 	fmt.Fprintf(w, "\n══════════════════════════════════════════════\n")
 	fmt.Fprintf(w, " 成功挂载  : %d 人\n", totalSuccess)
 	fmt.Fprintf(w, " 失败/跳过 : %d 人\n", totalFailed)
 	fmt.Fprintf(w, " 追溯佣金  : %d 单，总额 $%.2f\n", totalCreditedOrder, float64(totalCreditedAmt)/100)
 	fmt.Fprintf(w, "══════════════════════════════════════════════\n")
 	return nil
 }
 // buildAgentPlan 计算一个代理的补单预览，同时打印预览内容，返回分配计划。
 func buildAgentPlan(w io.Writer, ctx context.Context, db *gorm.DB, c config.Config,
 	agent *usermodel.User, pool []candidateUser, poolEnd time.Time) (agentPlan, error) {
 	rule := resolveCommissionRule(agent, c)
 	if retroForceCommissionPct > 0 {
 		rule.Percentage = uint8(retroForceCommissionPct)
 	}
 	// 补单场景：不区分新购/续费，所有已支付订单均参与佣金计算
 	rule.OnlyFirstPurchase = false
 	firstReferralTime, lastReferralTime, agentCreatedAt, totalReferred, totalOrders, totalCommission, err :=
 		queryAgentStats(ctx, db, agent.Id, poolEnd)
 	if err != nil {
 		return agentPlan{}, fmt.Errorf("查询历史数据失败: %w", err)
 	}
 	if totalReferred == 0 {
 		return agentPlan{}, fmt.Errorf("在 %s 之前没有任何邀请记录", poolEnd.Format("2006-01-02"))
 	}
 	lossStartTime, err := parseFlexibleTime(retroLossStart)
 	if err != nil {
 		return agentPlan{}, fmt.Errorf("--loss-start 格式错误: %w", err)
 	}
 	lossHours := time.Now().Sub(lossStartTime).Hours()
 	statsDays := lastReferralTime.Sub(firstReferralTime).Hours() / 24
 	if statsDays < 1 {
 		statsDays = 1
 	}
 	dailyAvgOrders := float64(totalOrders) / statsDays
 	avgOrdersPerUser := float64(totalOrders) / float64(totalReferred)
 	if avgOrdersPerUser < 1 {
 		avgOrdersPerUser = 1
 	}
 	dailyAvgCommission := float64(totalCommission) / statsDays
 	// 用用户池自身的平均佣金估算人数（避免历史费率与当前50%费率不匹配导致超发）
 	var poolAvgCommissionPerUser float64
 	if len(pool) > 0 {
 		var poolCommTotal int64
 		for _, u := range pool {
 			poolCommTotal += u.CommissionTotal
 		}
 		poolAvgCommissionPerUser = float64(poolCommTotal) / float64(len(pool))
 	}
 	if poolAvgCommissionPerUser < 1 {
 		poolAvgCommissionPerUser = 1
 	}
 	estimatedLostCommission := dailyAvgCommission * (lossHours / 24)
 	targetCommission := estimatedLostCommission * (float64(retroPercentage) / 100)
 	extraCount := int(targetCommission/poolAvgCommissionPerUser + 0.5)
 	if extraCount < 1 {
 		extraCount = 1
 	}
 	fmt.Fprintf(w, "\n═══════════════════════════════════════════════════════════\n")
 	fmt.Fprintf(w, " 代理 ID          : %d（注册于 %s）\n", agent.Id, agentCreatedAt.Format("2006-01-02 15:04:05"))
 	fmt.Fprintf(w, " 统计起点         : %s（首次邀请时间）\n", firstReferralTime.Format("2006-01-02 15:04:05"))
 	fmt.Fprintf(w, " 统计截止         : %s（最后邀请时间）\n", lastReferralTime.Format("2006-01-02 15:04:05"))
 	fmt.Fprintf(w, " 统计天数         : %.2f 天\n", statsDays)
 	fmt.Fprintf(w, " 历史邀请总人数   : %d 人\n", totalReferred)
 	fmt.Fprintf(w, " 下线总订单数     : %d 单（所有下线，不限时间）\n", totalOrders)
 	fmt.Fprintf(w, " 日均订单         : %.4f 单/天\n", dailyAvgOrders)
 	fmt.Fprintf(w, " 每用户平均订单   : %.4f 单\n", avgOrdersPerUser)
 	fmt.Fprintf(w, " 历史佣金总额     : $%.2f\n", float64(totalCommission)/100)
 	fmt.Fprintf(w, " 日均佣金         : $%.4f\n", dailyAvgCommission/100)
 	fmt.Fprintf(w, " 池内用户均佣金   : $%.4f\n", poolAvgCommissionPerUser/100)
 	fmt.Fprintf(w, " 佣金规则         : %d%%  仅首单=%v\n", rule.Percentage, rule.OnlyFirstPurchase)
 	fmt.Fprintf(w, "───────────────────────────────────────────────────────────\n")
 	fmt.Fprintf(w, " 丢失时长         : %.2f 小时（%s → 现在）\n",
 		lossHours, lossStartTime.Format("2006-01-02 15:04:05"))
 	fmt.Fprintf(w, " 预估丢失佣金     : $%.4f（$%.4f × %.2f/24）\n",
 		estimatedLostCommission/100, dailyAvgCommission/100, lossHours)
 	fmt.Fprintf(w, " 目标补偿金额     : $%.4f（× %d%%）\n",
 		targetCommission/100, retroPercentage)
 	fmt.Fprintf(w, " 需补充人数       : %d 人（$%.4f ÷ $%.4f）\n",
 		extraCount, targetCommission/100, poolAvgCommissionPerUser/100)
 	fmt.Fprintf(w, "═══════════════════════════════════════════════════════════\n\n")
 	// 重新按当前代理规则计算池内用户佣金（pool 由调用方传入，已是当前规则计算好的）
 	if len(pool) == 0 {
 		return agentPlan{}, fmt.Errorf("剩余用户池为空")
 	}
 	if len(pool) < extraCount {
 		fmt.Fprintf(w, "⚠️  剩余用户池只有 %d 人，少于需要的 %d 人，将全部分配\n\n", len(pool), extraCount)
 		extraCount = len(pool)
 	}
 	selected := randomSampleCandidates(pool, extraCount)
 	// 兜底追加：确保佣金合计 >= 目标
 	{
 		selectedSet := make(map[int64]struct{}, len(selected))
 		var selectedCommTotal int64
 		for _, u := range selected {
 			selectedSet[u.Id] = struct{}{}
 			selectedCommTotal += u.CommissionTotal
 		}
 		if selectedCommTotal < int64(targetCommission) {
 			remaining := make([]candidateUser, 0, len(pool)-len(selected))
 			for _, u := range pool {
 				if _, used := selectedSet[u.Id]; !used {
 					remaining = append(remaining, u)
 				}
 			}
 			// 按佣金从小到大排序，追加时精准补足，减少超发
 			sort.Slice(remaining, func(i, j int) bool {
 				return remaining[i].CommissionTotal < remaining[j].CommissionTotal
 			})
 			for _, u := range remaining {
 				if selectedCommTotal >= int64(targetCommission) {
 					break
 				}
 				selected = append(selected, u)
 				selectedCommTotal += u.CommissionTotal
 			}
 			if selectedCommTotal < int64(targetCommission) {
 				fmt.Fprintf(w, "⚠️  用户池佣金不足，已抽取全部可用用户（实际 $%.2f < 目标 $%.2f）\n\n",
 					float64(selectedCommTotal)/100, targetCommission/100)
 			}
 		}
 	}
 	// 打印选中用户明细
 	var previewTotalCommission int64
 	fmt.Fprintf(w, "随机抽取 %d 个用户（含待追溯佣金订单）：\n", len(selected))
 	fmt.Fprintln(w, strings.Repeat("═", 75))
 	for i, u := range selected {
 		fmt.Fprintf(w, "[%d] 用户 %-10d  注册: %s  %s\n",
 			i+1, u.Id, u.CreatedAt.Format("2006-01-02 15:04:05"), u.Identifier)
 		if len(u.Orders) == 0 {
 			fmt.Fprintln(w, "    （无符合条件的订单）")
 		} else {
 			fmt.Fprintf(w, "    %-38s  %10s  %8s  %10s  %s\n", "订单号", "金额", "手续费", "佣金", "类型")
 			fmt.Fprintf(w, "    %s\n", strings.Repeat("-", 72))
 			for _, od := range u.Orders {
 				commAmt := calcCommissionAmount(od.Amount, od.FeeAmount, rule.Percentage)
 				orderType := "首购"
 				if od.Type == 2 {
 					orderType = "续费"
 				}
 				fmt.Fprintf(w, "    %-38s  $%8.2f  $%6.2f  $%8.2f  %s\n",
 					od.OrderNo,
 					float64(od.Amount)/100,
 					float64(od.FeeAmount)/100,
 					float64(commAmt)/100,
 					orderType)
 			}
 			fmt.Fprintf(w, "    本用户追溯佣金合计: $%.2f\n", float64(u.CommissionTotal)/100)
 		}
 		previewTotalCommission += u.CommissionTotal
 		fmt.Fprintln(w)
 	}
 	fmt.Fprintln(w, strings.Repeat("═", 75))
 	fmt.Fprintf(w, "预计追溯佣金总额: $%.2f（目标补偿金额: $%.2f）\n\n",
 		float64(previewTotalCommission)/100, targetCommission/100)
 	return agentPlan{
 		Agent:             agent,
 		Rule:              rule,
 		Selected:          selected,
 		TargetAmt:         targetCommission,
 		PreviewCommission: previewTotalCommission,
 	}, nil
 }
 // queryAllActiveAgents returns all agents with referral_percentage > 0, optionally filtered by created_after.
 func queryAllActiveAgents(ctx context.Context, db *gorm.DB, createdAfter string) ([]*usermodel.User, error) {
 	q := db.WithContext(ctx).Model(&usermodel.User{}).
 		Where("referral_percentage > 0 AND deleted_at IS NULL")
 	if createdAfter != "" {
 		t, err := parseFlexibleTime(createdAfter)
 		if err != nil {
 			return nil, fmt.Errorf("--agent-created-after 格式错误: %w", err)
 		}
 		q = q.Where("created_at >= ?", t)
 	}
 	var agents []*usermodel.User
 	if err := q.Order("id ASC").Find(&agents).Error; err != nil {
 		return nil, err
 	}
 	return agents, nil
 }
 // parseFlexibleTime parses "YYYY-MM-DD HH:MM:SS" or "YYYY-MM-DD".
 func parseFlexibleTime(s string) (time.Time, error) {
 	s = strings.TrimSpace(s)
 	if t, err := time.ParseInLocation("2006-01-02 15:04:05", s, time.Local); err == nil {
 		return t, nil
 	}
 	return time.ParseInLocation("2006-01-02", s, time.Local)
 }
 // queryAgentStats returns (firstReferralTime, lastReferralTime, agentCreatedAt, totalReferred, totalOrders, totalCommission, error).
 func queryAgentStats(ctx context.Context, db *gorm.DB, agentID int64, endTime time.Time) (time.Time, time.Time, time.Time, int64, int64, int64, error) {
 	var agent usermodel.User
 	if err := db.WithContext(ctx).Model(&usermodel.User{}).
 		Where("id = ?", agentID).
 		First(&agent).Error; err != nil {
 		return time.Time{}, time.Time{}, time.Time{}, 0, 0, 0, err
 	}
 	agentCreatedAt := agent.CreatedAt
 	var firstUser usermodel.User
 	if err := db.WithContext(ctx).Model(&usermodel.User{}).
 		Where("referer_id = ? AND created_at >= ? AND created_at <= ? AND deleted_at IS NULL",
 			agentID, agentCreatedAt, endTime).
 		Order("created_at ASC").
 		First(&firstUser).Error; err != nil {
 		if err == gorm.ErrRecordNotFound {
 			return time.Time{}, time.Time{}, agentCreatedAt, 0, 0, 0, nil
 		}
 		return time.Time{}, time.Time{}, agentCreatedAt, 0, 0, 0, err
 	}
 	var lastUser usermodel.User
 	if err := db.WithContext(ctx).Model(&usermodel.User{}).
 		Where("referer_id = ? AND created_at >= ? AND created_at <= ? AND deleted_at IS NULL",
 			agentID, agentCreatedAt, endTime).
 		Order("created_at DESC").
 		First(&lastUser).Error; err != nil {
 		return time.Time{}, time.Time{}, agentCreatedAt, 0, 0, 0, err
 	}
 	var totalReferred int64
 	if err := db.WithContext(ctx).Model(&usermodel.User{}).
 		Where("referer_id = ? AND created_at >= ? AND created_at <= ? AND deleted_at IS NULL",
 			agentID, agentCreatedAt, endTime).
 		Count(&totalReferred).Error; err != nil {
 		return time.Time{}, time.Time{}, agentCreatedAt, 0, 0, 0, err
 	}
 	var totalOrders int64
 	if err := db.WithContext(ctx).Model(&ordermodel.Order{}).
 		Joins("JOIN user u ON u.id = `order`.user_id").
 		Where("u.referer_id = ?", agentID).
 		Where("`order`.status IN (2, 5)").
 		Count(&totalOrders).Error; err != nil {
 		return time.Time{}, time.Time{}, agentCreatedAt, 0, 0, 0, err
 	}
 	type commResult struct{ Total int64 }
 	var result commResult
 	err := db.WithContext(ctx).Raw(`
 		SELECT COALESCE(SUM(
 			CAST(JSON_UNQUOTE(JSON_EXTRACT(content, '$.amount')) AS SIGNED)
 		), 0) AS total
 		FROM system_logs
 		WHERE type = 33
 		  AND object_id = ?
 		  AND created_at <= ?
 		  AND JSON_UNQUOTE(JSON_EXTRACT(content, '$.type')) IN ('331', '332')
 	`, agentID, endTime).Scan(&result).Error
 	if err != nil {
 		return time.Time{}, time.Time{}, agentCreatedAt, 0, 0, 0, err
 	}
 	return firstUser.CreatedAt, lastUser.CreatedAt, agentCreatedAt, totalReferred, totalOrders, result.Total, nil
 }
 // queryNaturalTrafficPool returns pool candidates with pre-loaded qualifying orders.
 // orderStart (可为零值)：若非零，则只收录至少有一笔 updated_at >= orderStart 订单的用户，
 // 且只加载/统计 updated_at >= orderStart 的订单（确保分配后代理能在对应月份的销售报表中看到记录）。
 func queryNaturalTrafficPool(ctx context.Context, db *gorm.DB, start, end time.Time, orderStart time.Time, rule commissionRule) ([]candidateUser, error) {
 	type userRow struct {
 		Id             int64
 		CreatedAt      time.Time
 		AuthIdentifier string
 	}
 	var userRows []userRow
 	q := db.WithContext(ctx).
 		Table("user u").
 		Select("u.id, u.created_at, COALESCE(am.auth_identifier, '') AS auth_identifier").
 		Joins("JOIN `order` o ON o.user_id = u.id AND o.status IN (2, 5)").
 		Joins("LEFT JOIN user_auth_methods am ON am.user_id = u.id AND am.auth_type = 'email'").
 		Where("u.created_at >= ? AND u.created_at <= ?", start, end).
 		Where("u.referer_id = 0").
 		Where("u.deleted_at IS NULL")
 	if !orderStart.IsZero() {
 		// 只入池那些在 orderStart 之后有过订单的用户（保证代理销售报表里能看到）
 		q = q.Where("o.updated_at >= ?", orderStart)
 	}
 	if err := q.Group("u.id, u.created_at, am.auth_identifier").
 		Order("u.id ASC").
 		Scan(&userRows).Error; err != nil {
 		return nil, err
 	}
 	if len(userRows) == 0 {
 		return nil, nil
 	}
 	userIDs := make([]int64, len(userRows))
 	for i, r := range userRows {
 		userIDs[i] = r.Id
 	}
 	orderQuery := db.WithContext(ctx).Model(&ordermodel.Order{}).
 		Where("user_id IN ? AND status IN (2, 5)", userIDs)
 	if !orderStart.IsZero() {
 		// 只加载 orderStart 之后的订单：保证佣金统计和销售记录对齐
 		orderQuery = orderQuery.Where("updated_at >= ?", orderStart)
 	}
 	var allOrders []ordermodel.Order
 	if err := orderQuery.Order("user_id ASC, created_at ASC").Find(&allOrders).Error; err != nil {
 		return nil, err
 	}
 	ordersByUser := make(map[int64][]ordermodel.Order, len(userRows))
 	for _, od := range allOrders {
 		ordersByUser[od.UserId] = append(ordersByUser[od.UserId], od)
 	}
 	candidates := make([]candidateUser, 0, len(userRows))
 	for _, r := range userRows {
 		orders := ordersByUser[r.Id]
 		var commTotal int64
 		for i := range orders {
 			if canCreditOrder(rule, &orders[i]) {
 				commTotal += calcCommissionAmount(orders[i].Amount, orders[i].FeeAmount, rule.Percentage)
 			}
 		}
 		candidates = append(candidates, candidateUser{
 			Id:              r.Id,
 			CreatedAt:       r.CreatedAt,
 			Identifier:      r.AuthIdentifier,
 			Orders:          orders,
 			CommissionTotal: commTotal,
 		})
 	}
 	return candidates, nil
 }
 // randomSampleCandidates picks n random elements from pool without replacement.
 func randomSampleCandidates(pool []candidateUser, n int) []candidateUser {
 	if n >= len(pool) {
 		return append([]candidateUser{}, pool...)
 	}
 	rng := rand.New(rand.NewSource(time.Now().UnixNano()))
 	indices := rng.Perm(len(pool))[:n]
 	result := make([]candidateUser, n)
 	for i, idx := range indices {
 		result[i] = pool[idx]
 	}
 	return result
 }
 // processOneUser assigns agentId as referer and retroactively credits commission for qualifying orders.
 func processOneUser(
 	ctx context.Context,
 	db *gorm.DB,
 	um usermodel.Model,
 	agentID, userID int64,
 	rule commissionRule,
 	orderStart time.Time,
 ) (int64, int64, error) {
 	var creditedOrders, creditedAmount int64
 	err := db.WithContext(ctx).Transaction(func(tx *gorm.DB) error {
 		var target usermodel.User
 		if e := tx.Model(&usermodel.User{}).
 			Where("id = ? AND referer_id = 0 AND deleted_at IS NULL", userID).
 			First(&target).Error; e != nil {
 			if e == gorm.ErrRecordNotFound {
 				return fmt.Errorf("用户不存在或已有代理或已删除")
 			}
 			return e
 		}
 		var orders []ordermodel.Order
 		oq := tx.Model(&ordermodel.Order{}).
 			Where("user_id = ? AND status IN (2, 5)", userID)
 		if !orderStart.IsZero() {
 			oq = oq.Where("updated_at >= ?", orderStart)
 		}
 		if e := oq.Order("created_at ASC, id ASC").Find(&orders).Error; e != nil {
 			return e
 		}
 		if len(orders) == 0 {
 			return fmt.Errorf("无合格订单")
 		}
 		if e := tx.Model(&usermodel.User{}).
 			Where("id = ? AND referer_id = 0 AND deleted_at IS NULL", userID).
 			Updates(map[string]interface{}{
 				"referer_id": agentID,
 				"updated_at": time.Now(),
 			}).Error; e != nil {
 			return e
 		}
 		for i := range orders {
 			od := &orders[i]
 			if !canCreditOrder(rule, od) {
 				continue
 			}
 			amount := calcCommissionAmount(od.Amount, od.FeeAmount, rule.Percentage)
 			if amount <= 0 {
 				continue
 			}
 			var existCount int64
 			if e := tx.Model(&logmodel.SystemLog{}).
 				Where("type = ? AND object_id = ? AND content LIKE ?",
 					logmodel.TypeCommission.Uint8(), agentID,
 					fmt.Sprintf("%%\"%s\"%%", od.OrderNo),
 				).Count(&existCount).Error; e != nil {
 				return e
 			}
 			if existCount > 0 {
 				continue
 			}
 			if e := tx.Model(&usermodel.User{}).
 				Where("id = ? AND deleted_at IS NULL", agentID).
 				UpdateColumn("commission", gorm.Expr("commission + ?", amount)).Error; e != nil {
 				return e
 			}
 			commType := logmodel.CommissionTypePurchase
 			if od.Type == 2 {
 				commType = logmodel.CommissionTypeRenewal
 			}
 			payload := &logmodel.Commission{
 				Type:      commType,
 				Amount:    amount,
 				OrderNo:   od.OrderNo,
 				Timestamp: od.CreatedAt.UnixMilli(),
 			}
 			content, _ := payload.Marshal()
 			if e := tx.Create(&logmodel.SystemLog{
 				Type:      logmodel.TypeCommission.Uint8(),
 				Date:      od.CreatedAt.Format("2006-01-02"),
 				ObjectID:  agentID,
 				Content:   string(content),
 				CreatedAt: od.CreatedAt,
 			}).Error; e != nil {
 				return e
 			}
 			creditedOrders++
 			creditedAmount += amount
 		}
 		return nil
 	})
 	if err != nil {
 		return 0, 0, err
 	}
 	if updated, e := um.FindOne(ctx, userID); e == nil {
 		_ = um.UpdateUserCache(ctx, updated)
 	}
 	return creditedOrders, creditedAmount, nil
 }
 func resolveCommissionRule(agent *usermodel.User, c config.Config) commissionRule {
 	if agent.ReferralPercentage > 0 {
 		onlyFirst := true
 		if agent.OnlyFirstPurchase != nil {
 			onlyFirst = *agent.OnlyFirstPurchase
 		}
 		return commissionRule{Percentage: agent.ReferralPercentage, OnlyFirstPurchase: onlyFirst}
 	}
 	return commissionRule{
 		Percentage:        uint8(c.Invite.ReferralPercentage),
 		OnlyFirstPurchase: c.Invite.OnlyFirstPurchase,
 	}
 }
 func canCreditOrder(rule commissionRule, od *ordermodel.Order) bool {
 	if rule.Percentage == 0 {
 		return false
 	}
 	if rule.OnlyFirstPurchase && !od.IsNew {
 		return false
 	}
 	return od.Status == 2 || od.Status == 5
 }
 func calcCommissionAmount(amount, feeAmount int64, percentage uint8) int64 {
 	base := amount - feeAmount
 	if base <= 0 || percentage == 0 {
 		return 0
 	}
 	return int64(float64(base) * float64(percentage) / 100)
 }
@@ -0,0 +1,152 @@
 # MySQL 8.0 master/replica compose for two separate servers.
 #
 # Master server:
 #   COMPOSE_PROFILES=master docker compose -f config/docker-compose.mysql-replication.yml up -d
 #
 # Replica server:
 #   MASTER_HOST=<master_public_or_private_ip> COMPOSE_PROFILES=replica docker compose -f config/docker-compose.mysql-replication.yml up -d
 #
 # Required env on both servers:
 #   MYSQL_ROOT_PASSWORD=<strong-root-password>
 #   MYSQL_REPLICATION_PASSWORD=<strong-replication-password>
 #
 # Optional env:
 #   MYSQL_DATABASE=ppanel
 #   MYSQL_REPLICATION_USER=repl
 #   MYSQL_MASTER_PORT=3306
 #   MYSQL_REPLICA_PORT=3306
 #   MYSQL_SERVER_ID=1     # master default
 #   MYSQL_REPLICA_ID=2    # replica default
 #
 # If the master already has data, import a GTID-aware dump into the replica
 # before starting replication. Fresh empty deployments can start master first,
 # then replica, then point the application at the master.
 services:
  mysql-master:
    image: mysql:8.0
    container_name: ppanel-mysql-master
    profiles:
      - master
    restart: always
    ports:
      - "${MYSQL_MASTER_PORT:-3306}:3306"
    environment:
      MYSQL_ROOT_PASSWORD: "${MYSQL_ROOT_PASSWORD:?please set MYSQL_ROOT_PASSWORD}"
      MYSQL_DATABASE: "${MYSQL_DATABASE:-ppanel}"
      MYSQL_REPLICATION_USER: "${MYSQL_REPLICATION_USER:-repl}"
      MYSQL_REPLICATION_PASSWORD: "${MYSQL_REPLICATION_PASSWORD:?please set MYSQL_REPLICATION_PASSWORD}"
      TZ: Asia/Shanghai
    command:
      - --default-authentication-plugin=mysql_native_password
      - --server-id=${MYSQL_SERVER_ID:-1}
      - --log-bin=mysql-bin
      - --binlog-format=ROW
      - --gtid-mode=ON
      - --enforce-gtid-consistency=ON
      - --log-replica-updates=ON
      - --binlog-expire-logs-seconds=604800
      - --max_connections=1000
      - --character-set-server=utf8mb4
      - --collation-server=utf8mb4_unicode_ci
    volumes:
      - mysql_master_data:/var/lib/mysql
    configs:
      - source: mysql_master_init
        target: /docker-entrypoint-initdb.d/01-create-replication-user.sh
        mode: 0755
    healthcheck:
      test: ["CMD-SHELL", "mysqladmin ping -h 127.0.0.1 -uroot -p$${MYSQL_ROOT_PASSWORD}"]
      interval: 10s
      timeout: 5s
      retries: 10
    logging:
      driver: json-file
      options:
        max-size: 10m
        max-file: "3"
  mysql-replica:
    image: mysql:8.0
    container_name: ppanel-mysql-replica
    profiles:
      - replica
    restart: always
    ports:
      - "${MYSQL_REPLICA_PORT:-3306}:3306"
    environment:
      MYSQL_ROOT_PASSWORD: "${MYSQL_ROOT_PASSWORD:?please set MYSQL_ROOT_PASSWORD}"
      MYSQL_DATABASE: "${MYSQL_DATABASE:-ppanel}"
      TZ: Asia/Shanghai
    command:
      - --default-authentication-plugin=mysql_native_password
      - --server-id=${MYSQL_REPLICA_ID:-2}
      - --relay-log=mysql-relay-bin
      - --read-only=ON
      - --super-read-only=ON
      - --gtid-mode=ON
      - --enforce-gtid-consistency=ON
      - --log-replica-updates=ON
      - --binlog-format=ROW
      - --max_connections=1000
      - --character-set-server=utf8mb4
      - --collation-server=utf8mb4_unicode_ci
    volumes:
      - mysql_replica_data:/var/lib/mysql
    healthcheck:
      test: ["CMD-SHELL", "mysqladmin ping -h 127.0.0.1 -uroot -p$${MYSQL_ROOT_PASSWORD}"]
      interval: 10s
      timeout: 5s
      retries: 10
    logging:
      driver: json-file
      options:
        max-size: 10m
        max-file: "3"
  mysql-replica-init:
    image: mysql:8.0
    container_name: ppanel-mysql-replica-init
    profiles:
      - replica
    restart: "no"
    depends_on:
      mysql-replica:
        condition: service_healthy
    environment:
      MYSQL_ROOT_PASSWORD: "${MYSQL_ROOT_PASSWORD:?please set MYSQL_ROOT_PASSWORD}"
      MYSQL_REPLICATION_USER: "${MYSQL_REPLICATION_USER:-repl}"
      MYSQL_REPLICATION_PASSWORD: "${MYSQL_REPLICATION_PASSWORD:?please set MYSQL_REPLICATION_PASSWORD}"
      MASTER_HOST: "${MASTER_HOST:?please set MASTER_HOST to the master server ip or hostname}"
      MASTER_PORT: "${MASTER_PORT:-3306}"
    entrypoint:
      - /bin/sh
      - -ec
      - |
        mysql -hmysql-replica -uroot -p"$${MYSQL_ROOT_PASSWORD}" <<SQL
        STOP REPLICA;
        CHANGE REPLICATION SOURCE TO
          SOURCE_HOST='$${MASTER_HOST}',
          SOURCE_PORT=$${MASTER_PORT},
          SOURCE_USER='$${MYSQL_REPLICATION_USER}',
          SOURCE_PASSWORD='$${MYSQL_REPLICATION_PASSWORD}',
          SOURCE_AUTO_POSITION=1,
          GET_SOURCE_PUBLIC_KEY=1;
        START REPLICA;
        SQL
 configs:
  mysql_master_init:
    content: |
      #!/bin/sh
      set -eu
      mysql -uroot -p"$${MYSQL_ROOT_PASSWORD}" <<SQL
      CREATE USER IF NOT EXISTS '$${MYSQL_REPLICATION_USER}'@'%' IDENTIFIED WITH mysql_native_password BY '$${MYSQL_REPLICATION_PASSWORD}';
      GRANT REPLICATION SLAVE, REPLICATION CLIENT ON *.* TO '$${MYSQL_REPLICATION_USER}'@'%';
      FLUSH PRIVILEGES;
      SQL
 volumes:
  mysql_master_data:
  mysql_replica_data:
@@ -0,0 +1,20 @@
 EC2 SSH 连接资料
 服务器名称: hifast-hk-app-01
 公网 IP: 43.198.248.161
 登录用户: ubuntu
 私钥文件:
 - hifast-hk-app-01-reset
 公钥文件:
 - hifast-hk-app-01-reset.pub
 连接命令:
 ssh -i hifast-hk-app-01-reset ubuntu@43.198.248.161
 如果在 Mac / Linux 上使用，先执行:
 chmod 600 hifast-hk-app-01-reset
 如果要给别人使用，只需要把私钥文件 hifast-hk-app-01-reset 发给对方即可。
 出于安全考虑，建议通过安全渠道传输，并在后续需要时重新轮换密钥。
@@ -0,0 +1,8 @@
 -----BEGIN OPENSSH PRIVATE KEY-----
 b3BlbnNzaC1rZXktdjEAAAAABG5vbmUAAAAEbm9uZQAAAAAAAAABAAAAMwAAAAtzc2gtZW
 QyNTUxOQAAACDb6msDqmSHLv0mWgCP4vfjQ3A552qv95uQdsH94RnoCgAAAKjy5KDa8uSg
 2gAAAAtzc2gtZWQyNTUxOQAAACDb6msDqmSHLv0mWgCP4vfjQ3A552qv95uQdsH94RnoCg
 AAAEDwSb0b/0S6Tw8Od5hAtIKqt1JvomqQQS44Ty3xL+FjPtvqawOqZIcu/SZaAI/i9+ND
 cDnnaq/3m5B2wf3hGegKAAAAIWhpZmFzdC1oay1hcHAtMDEtcmVzZXQtMjAyNi0wNS0xMA
 ECAwQ=
 -----END OPENSSH PRIVATE KEY-----
@@ -0,0 +1 @@
 ssh-ed25519 AAAAC3NzaC1lZDI1NTE5AAAAINvqawOqZIcu/SZaAI/i9+NDcDnnaq/3m5B2wf3hGegK hifast-hk-app-01-reset-2026-05-10
@@ -0,0 +1,363 @@
 # PPanel 香港区新 AWS 账号部署说明
 本目录用于在 **新 AWS 账号** 中按 **香港区 `ap-east-1`** 重建一套全新空环境。
 目标架构：
 `DNS -> ALB -> WAF -> EC2(Nginx + ppanel-server + Redis + observability) -> RDS MySQL`
 ## 1. 资源清单
 按下面顺序创建资源：
 1. VPC
 2. 2 个公有子网 + 2 个私有子网
 3. Internet Gateway
 4. 公有 / 私有路由表
 5. 安全组
 6. RDS MySQL
 7. EC2 本机 Redis Docker
 8. EC2
 9. ACM 证书
 10. ALB + Target Group
 11. WAF Web ACL
 12. 平行环境域名
 建议命名：
 - VPC: `ppanel-hk-prod`
 - EC2: `ppanel-app-hk-01`
 - RDS: `ppanel-mysql-hk`
 - Redis container: `hifast-redis`
 - ALB: `ppanel-alb-hk`
 - WAF: `ppanel-waf-hk`
 ## 2. 默认规格
 ### EC2
 - Region: `ap-east-1`
 - OS: Ubuntu 24.04 LTS
 - Instance type: `t4g.large` 起步
 - Disk: `gp3 80GB`
 - Public subnet: 是
 - IAM Role: 允许读取 CloudWatch / SSM（如使用）
 - 如果要在 AWS EC2 本机执行 S3 备份：额外允许写入专用备份桶
 ### RDS MySQL
 - Engine: MySQL 8.0
 - Class: `db.r7g.xlarge`
 - Storage: `gp3 100GB`
 - DB name: `hifast`
 - Username: `admin`
 - Public access: `No`
 - Charset: `utf8mb4`
 - Backup: `7-14 days`
 ### Redis
 - 部署位置：业务 EC2 本机
 - 部署方式：Docker
 - 版本：`redis:8.2.1`
 - 监听：`0.0.0.0:6379`
 - 应用连接：`127.0.0.1:6379`
 - 安全组：仅对白名单备用节点或同机应用开放
 ## 3. 网络与安全组
 ### 子网布局
 - `public-a`, `public-b`: ALB / EC2
 - `private-a`, `private-b`: RDS
 ### 安全组建议
 #### `sg-alb`
 - Inbound
  - `80/tcp` from `0.0.0.0/0`
  - `443/tcp` from `0.0.0.0/0`
 - Outbound
  - `80/tcp` to `sg-ec2`
 #### `sg-ec2`
 - Inbound
  - `80/tcp` from `sg-alb`
  - `22/tcp` from `你的固定运维 IP`
 - Outbound
  - all
 说明：
 - 应用容器监听 `127.0.0.1:8080`
 - EC2 对外只让 Nginx 监听 `80`
 - Grafana / Prometheus / Tempo 仅监听 `127.0.0.1`
 #### `sg-rds`
 - Inbound
  - `3306/tcp` from `sg-ec2`
 #### `sg-ec2` 额外说明
 - 如果需要外部备用节点复制 Redis，再额外放行：
  - `6379/tcp` from `104.238.220.230/32`
 ## 4. ALB / Target Group / 健康检查
 ### Target Group
 - Type: `Instance`
 - Protocol: `HTTP`
 - Port: `80`
 - Health check path: `/v1/common/heartbeat`
 - Success code: `200`
 这个路径已由项目现有接口提供，无需额外改代码。
 ### ALB 监听器
 - `80` -> redirect to `443`
 - `443` -> forward 到 target group
 ### ACM
 - 在 `ap-east-1` 申请证书
 - 先给平行环境域名，例如：
  - `api-new.hifast.biz`
  - `logs-new.hifast.biz`
 ## 5. WAF 规则
 首版至少启用：
 1. `AWSManagedRulesCommonRuleSet`
 2. `AWSManagedRulesKnownBadInputsRuleSet`
 3. `AWSManagedRulesAmazonIpReputationList`
 4. 全站 rate-based rule
 5. 针对高风险路径的 rate-based rule
 建议的第一版限流：
 - 全站：每 IP `2000 / 5 分钟`
 - `/v1/public/user/subscribe`：每 IP `300 / 5 分钟`
 - 登录 / 注册 / 验证码接口：每 IP `100 / 5 分钟`
 节点上报接口建议后续补：
 - `/v1/server/status`
 - `/v1/server/online`
 - `/v1/server/traffic`
 优先用节点出口 IP 白名单；没有固定出口 IP 的节点暂时保留 `secret_key`，但不要把它当成唯一防线。
 ## 6. EC2 文件落地
 在 EC2 上建议使用：
 - 应用目录：`/opt/ppanel`
 - Nginx 配置：`/etc/nginx/sites-available/ppanel-api.conf`
 需要上传这些文件 / 目录：
 - `docker-compose.cloud.yml`
 - `deploy/aws/ap-east-1/configs/ppanel.yaml.example` -> 重命名为 `configs/ppanel.yaml`
 - `deploy/aws/ap-east-1/nginx/ppanel-api.conf`
 - `grafana/`
 - `loki/`
 - `prometheus/`
 - `tempo/`
 - `.env.example` -> 重命名为 `.env`
 目标目录示例：
 ```text
 /opt/ppanel/
  docker-compose.cloud.yml
  .env
  configs/ppanel.yaml
  grafana/
  loki/
  prometheus/
  tempo/
  logs/
  cache/
  tempo_data/
 ```
 ## 7. 应用配置
 基线模板见：
 - [`configs/ppanel.yaml.example`](./configs/ppanel.yaml.example)
 - [`nginx/ppanel-api.conf`](./nginx/ppanel-api.conf)
 关键值必须替换：
 - `MySQL.Addr`
 - `MySQL.Password`
 - `Redis.Host`
 - `Redis.Pass`
 - `JwtAuth.AccessSecret`
 - `Administrator.Email`
 - `Administrator.Password`
 - `AppSignature.AppSecrets.*`
 - `device.security_secret`
 - `Site.Host`
 - `Site.SiteName`
 Redis 约定保持不变：
 - 业务缓存：DB `0`
 - Asynq：DB `5`（代码内部已固定使用）
 ## 8. 部署步骤
 ### 8.1 初始化 EC2
 把脚本上传到 EC2 后执行：
 ## 9. 104 灾备节点常用运维脚本
 如果你要在 `104.238.220.230` 上执行数据迁移、主从重拉、主库提升，可以直接复用仓库里的这几份脚本：
 - 数据导出 / 导入交互工具：
  - [`deploy/scripts/hifast_data_sync_tool.sh`](/Users/Apple/code_vpn/vpn/ppanel-server/deploy/scripts/hifast_data_sync_tool.sh)
 - MySQL 主从运维工具：
  - [`deploy/scripts/mysql_replica_ops.sh`](/Users/Apple/code_vpn/vpn/ppanel-server/deploy/scripts/mysql_replica_ops.sh)
 - Redis 主从运维工具：
  - [`deploy/scripts/redis_replica_ops.sh`](/Users/Apple/code_vpn/vpn/ppanel-server/deploy/scripts/redis_replica_ops.sh)
 - 统一总入口：
  - [`deploy/scripts/hifast_data_sync_tool.sh`](/Users/Apple/code_vpn/vpn/ppanel-server/deploy/scripts/hifast_data_sync_tool.sh)
 - 主从运维环境模板：
  - [`deploy/aws/ap-east-1/configs/replica-ops.env.example`](/Users/Apple/code_vpn/vpn/ppanel-server/deploy/aws/ap-east-1/configs/replica-ops.env.example)
 ### 9.1 数据迁移工具
 支持：
 - 备份 MySQL 到 S3
 - 备份 Redis 到 S3
 - 从正式库导出 MySQL `sql.gz`
 - 把 `sql.gz` 导入 AWS RDS
 - 从正式 Redis 导出 `RDB`
 - 将 `RDB` 导入 Docker Redis 或宿主机 Redis
 - 查看 MySQL / Redis 当前主从状态
 - 强制重拉 MySQL / Redis 主从
 - 把 MySQL / Redis 从库提升为可写主库
 示例：
 ```bash
 bash deploy/scripts/hifast_data_sync_tool.sh /root/replica-ops.env
 ```
 ```bash
 chmod +x deploy/scripts/bootstrap_aws_ec2.sh
 sudo APP_DIR=/opt/ppanel deploy/scripts/bootstrap_aws_ec2.sh
 ```
 ### 8.2 安装 Nginx 配置
 ```bash
 sudo cp deploy/aws/ap-east-1/nginx/ppanel-api.conf /etc/nginx/sites-available/ppanel-api.conf
 sudo ln -sf /etc/nginx/sites-available/ppanel-api.conf /etc/nginx/sites-enabled/ppanel-api.conf
 sudo nginx -t
 sudo systemctl reload nginx
 ```
 ### 8.3 启动容器
 ```bash
 cd /opt/ppanel
 docker compose -f docker-compose.cloud.yml up -d
 ```
 ### 8.4 预检
 ```bash
 chmod +x deploy/scripts/preflight_aws_hk.sh
 APP_DIR=/opt/ppanel \
 RDS_HOST=<new-rds-endpoint> \
 REDIS_HOST=127.0.0.1 \
 deploy/scripts/preflight_aws_hk.sh
 ```
 ## 9. 平行环境验证
 先验证 `api-new.hifast.biz`，不要直接切正式域名。
 必测项：
 1. `ALB target` 为 healthy
 2. `GET /v1/common/heartbeat` 返回 200
 3. 管理员登录
 4. 用户注册 / 登录
 5. 订阅查询
 6. 节点上报 `/v1/server/status`
 7. 本机 Redis 可写缓存
 8. Asynq 可入队并消费
 ## 10. 正式切换
 切换前检查：
 1. ALB 5xx 为 0
 2. EC2 CPU / Memory 正常
 3. RDS CPU / Connections 正常
 4. 本机 Redis CPU / Connections / Memory 正常
 5. WAF 已挂到 ALB
 6. EC2 安全组没有对公网放 `8080/3333/9090/4317`
 切换方式：
 1. 保持新环境先跑平行域名
 2. 正式域名切到新 ALB
 3. 观察至少 1 小时
 4. 确认无误后再处理旧环境
 ## 11. 监控建议
 至少建这些 CloudWatch / Grafana 观测项：
 - ALB `RequestCount`, `HTTPCode_ELB_5XX_Count`, `TargetResponseTime`
 - EC2 `CPUUtilization`, `NetworkIn`, `NetworkOut`, `StatusCheckFailed`
 - RDS `CPUUtilization`, `DatabaseConnections`, `ReadLatency`, `WriteLatency`
 - Redis 容器 CPU / Memory / restart count
 ## 12. 这次方案的边界
 本目录交付的是：
 - 香港区新账号的部署模板
 - 新空环境启动与验证流程
 - ALB / WAF / EC2 / RDS / 本机 Redis 的落地约定
 不包含：
 - 旧数据迁移
 - Terraform / CloudFormation 自动建资源
 - Redis 托管版改造
 - 多活 / 自动扩缩容
 ## 13. S3 备份补强
 当前已落地的 S3 备份桶：
 - `hifast-prod-backups-200810848252-ap-east-1`
 建议与现网结合方式：
 1. `RDS automated backup` 继续保留，作为第一层恢复能力
 2. `104` 外部 MySQL 从库执行逻辑备份并上传到 S3，作为第二层可下载备份
 3. `104` 外部 Redis 从库按需导出 `RDB` 到 S3，补齐缓存类灾备材料
 仓库中已补充：
 - 环境变量模板：[`configs/backup-to-s3.env.example`](./configs/backup-to-s3.env.example)
 - MySQL 备份脚本：[`../../scripts/mysql_backup_to_s3.sh`](../../scripts/mysql_backup_to_s3.sh)
 - Redis 备份脚本：[`../../scripts/redis_rdb_backup_to_s3.sh`](../../scripts/redis_rdb_backup_to_s3.sh)
 建议把 MySQL 备份脚本优先部署到 `104`，因为它直接连接本地只读从库，对 AWS 主库扰动最小。
@@ -0,0 +1,18 @@
 AWS_REGION=ap-east-1
 S3_BUCKET=hifast-prod-backups-200810848252-ap-east-1
 S3_PREFIX=mysql
 BACKUP_DIR=/var/backups/hifast
 HOST_TAG=104-standby
 KEEP_LOCAL_DAYS=3
 CHECK_REPLICA=1
 MYSQL_HOST=127.0.0.1
 MYSQL_PORT=3306
 MYSQL_USER=backup_reader
 MYSQL_PASSWORD=CHANGE_ME
 MYSQL_SOCKET=
 MYSQL_DATABASE=hifast
 REDIS_HOST=127.0.0.1
 REDIS_PORT=6379
 REDIS_PASSWORD=CHANGE_ME
@@ -0,0 +1,20 @@
 PRIMARY_HOST=hifast-mysql-prod-v2.cd6aey40m6ag.ap-east-1.rds.amazonaws.com
 PRIMARY_PORT=3306
 PRIMARY_USER=admin
 PRIMARY_PASSWORD=CHANGE_ME
 PRIMARY_DB=hifast
 PRIMARY_REPL_USER=repl
 PRIMARY_REPL_PASSWORD=CHANGE_ME
 PRIMARY_REPL_HOST=104.238.220.230
 PRIMARY_BINLOG_RETENTION_HOURS=24
 REPLICA_HOST=127.0.0.1
 REPLICA_PORT=3306
 REPLICA_USER=root
 REPLICA_PASSWORD=
 REPLICA_SOCKET=/var/run/mysqld/mysqld.sock
 REPLICA_DB=hifast
 REPLICA_SOURCE_SSL=1
 DUMP_FILE=
@@ -0,0 +1,111 @@
 Host: 0.0.0.0
 Port: 8080
 Debug: false
 JwtAuth:
  AccessSecret: CHANGE_ME_TO_A_LONG_RANDOM_SECRET
  AccessExpire: 604800
 Logger:
  ServiceName: PPanel
  Mode: console
  Encoding: plain
  TimeFormat: "2006-01-02 15:04:05.000"
  Path: logs
  Level: info
  MaxContentLength: 0
  Compress: false
  Stat: true
  KeepDays: 7
  StackCooldownMillis: 100
  MaxBackups: 7
  MaxSize: 100
  Rotation: daily
  FileTimeFormat: "2006-01-02T15:04:05.000Z07:00"
 MySQL:
  Addr: YOUR_RDS_ENDPOINT:3306
  Dbname: hifast
  Username: admin
  Password: CHANGE_ME_TO_RDS_PASSWORD
  Config: charset=utf8mb4&parseTime=true&loc=Asia%2FShanghai
  MaxIdleConns: 10
  MaxOpenConns: 100
  SlowThreshold: 1000
 Redis:
  Host: 127.0.0.1:6379
  Pass: CHANGE_ME_TO_REDIS_PASSWORD
  DB: 0
  PoolSize: 100
  MinIdleConns: 10
  MaxRetries: 3
  PoolTimeout: 4
  IdleTimeout: 300
  MaxConnAge: 0
  DialTimeout: 5
  ReadTimeout: 3
  WriteTimeout: 3
 Trace:
  Name: ppanel-server
  Endpoint: 127.0.0.1:4317
  Sampler: 0.1
  Batcher: otlpgrpc
 Site:
  Host: api-new.hifast.biz
  SiteName: HiFastVPN
 Administrator:
  Email: admin@example.com
  Password: CHANGE_ME_TO_STRONG_ADMIN_PASSWORD
 Telegram:
  Enable: false
  BotID: 0
  BotName: ""
  BotToken: ""
  GroupChatID: ""
  EnableNotify: false
  WebHookDomain: ""
 Kutt:
  Enable: false
  ApiURL: ""
  ApiKey: ""
  TargetURL: ""
  Domain: ""
 OpenInstall:
  Enable: false
  AppKey: ""
  ApiKey: ""
 Loki:
  Enable: true
  URL: "http://localhost:3100"
 AppSignature:
  AppSecrets:
    android-client: CHANGE_ME_ANDROID_SIGNATURE_SECRET
    ios-client: CHANGE_ME_IOS_SIGNATURE_SECRET
    web-client: CHANGE_ME_WEB_SIGNATURE_SECRET
  ValidWindowSeconds: 300
  SkipPrefixes:
    - /v1/notify/
    - /v1/iap/notifications
    - /v1/telegram/webhook
    - /v1/subscribe/config
 Signature:
  EnableSignature: false
 device:
  enable: true
  security_secret: CHANGE_ME_DEVICE_SECURITY_SECRET
 Register:
  EnableTrial: true
  EnableTrialEmailWhitelist: true
  TrialEmailDomainWhitelist: "gmail.com,outlook.com,icloud.com,qq.com,163.com"
@@ -0,0 +1,23 @@
 MYSQL_HOST=127.0.0.1
 MYSQL_PORT=3306
 MYSQL_USER=root
 MYSQL_PASSWORD=CHANGE_ME
 MYSQL_SOCKET=
 REPL_SOURCE_HOST=hifast-mysql-prod-v2.cd6aey40m6ag.ap-east-1.rds.amazonaws.com
 REPL_SOURCE_PORT=3306
 REPL_SOURCE_USER=repl
 REPL_SOURCE_PASSWORD=CHANGE_ME
 REPL_SOURCE_SSL=1
 REPL_SOURCE_LOG_FILE=
 REPL_SOURCE_LOG_POS=
 REPL_SOURCE_AUTO_POSITION=1
 REDIS_HOST=127.0.0.1
 REDIS_PORT=6379
 REDIS_PASSWORD=CHANGE_ME
 REDIS_SOURCE_HOST=18.163.33.75
 REDIS_SOURCE_PORT=6379
 REDIS_SOURCE_USER=
 REDIS_SOURCE_PASSWORD=CHANGE_ME
@@ -0,0 +1,33 @@
 server {
    listen 80 default_server;
    listen [::]:80 default_server;
    server_name _;
    client_max_body_size 20m;
    access_log /var/log/nginx/ppanel-access.log;
    error_log /var/log/nginx/ppanel-error.log warn;
    location / {
        proxy_http_version 1.1;
        proxy_pass http://127.0.0.1:8080;
        proxy_set_header Host $host;
        proxy_set_header X-Real-IP $remote_addr;
        proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
        proxy_set_header X-Forwarded-Proto $scheme;
        proxy_set_header X-Forwarded-Host $host;
        proxy_set_header X-Forwarded-Port $server_port;
        proxy_connect_timeout 10s;
        proxy_send_timeout 60s;
        proxy_read_timeout 60s;
    }
    location = /nginx_status {
        stub_status;
        access_log off;
        allow 127.0.0.1;
        deny all;
    }
 }
@@ -0,0 +1,862 @@
 # 邀请赠送与购买订阅逻辑说明
 本文档说明当前代码中的购买订阅、订单激活、邀请佣金、邀请赠送时间、家庭组归属逻辑。重点覆盖每个主要分支，方便排查“重复订单/重复订阅/邀请未赠时/赠时落点错误”等问题。
 涉及核心文件：
 - `internal/logic/public/order/purchaseLogic.go`
 - `queue/logic/order/activateOrderLogic.go`
 - `internal/logic/common/familyEntitlement.go`
 - `internal/model/user/model.go`
 ## 1. 核心概念
 ### 1.1 订单状态
 | 状态 | 含义 |
 | --- | --- |
 | `1` | pending，已创建未支付 |
 | `2` | paid，已支付待激活 |
 | `3` | close，已关闭 |
 | `4` | failed/claimed，代码里同时用于失败和 worker 临时领取 |
 | `5` | finished，激活完成 |
 ### 1.2 订单类型
 | 类型 | 含义 |
 | --- | --- |
 | `1` | 新购套餐 |
 | `2` | 续费/换套餐 |
 | `3` | 重置流量 |
 | `4` | 余额充值 |
 | `5` | 兑换码激活 |
 ### 1.3 用户 ID 与订阅归属
 订单有两个重要用户字段：
 | 字段 | 含义 |
 | --- | --- |
 | `user_id` | 发起订单/付款的用户 |
 | `subscription_user_id` | 订阅权益归属用户；`0` 表示同 `user_id` |
 家庭组规则：
 - 普通用户下单：`subscription_user_id = user_id`。
 - 家庭组成员下单：`subscription_user_id = 家主用户 ID`。
 - 家庭组主账号下单：`subscription_user_id = 家主用户 ID`。
 当前代码使用 `ResolveEntitlementUser` 判断家庭归属：
 - 只有有效家庭组、有效成员关系、角色为 member 时，权益归家主。
 - 家主本人不会被改写到别人名下。
 ## 2. 购买订阅下单逻辑
 入口：`Purchase(req *types.PurchaseOrderRequest)`
 这里只是创建订单和安排关闭任务，不直接发放订阅。真正发放订阅在订单支付后由队列激活处理。
 ### 2.1 登录用户检查
 分支：
 - 上下文没有当前用户：返回 `InvalidAccess`。
 - 当前用户存在：继续。
 ### 2.2 解析订阅权益归属
 调用 `ResolveEntitlementUser`：
 | 场景 | `effective_user_id` | 结果 |
 | --- | --- | --- |
 | 普通用户 | 本人 ID | 订阅归本人 |
 | 家庭组主账号 | 本人 ID | 订阅归本人 |
 | 家庭组成员 | 家主 ID | 订阅归家主 |
 后续查询已有订阅、quota、创建订单里的 `subscription_user_id` 都会使用这个归属结果。
 ### 2.3 数量校验
 分支：
 - `quantity <= 0`：自动改成 `1`。
 - `quantity > MaxQuantity`：返回参数错误。
 - 合法：继续。
 ### 2.4 单订阅模式路由
 先默认：
 ```text
 order_type = 1
 target_subscribe_id = req.subscribe_id
 parent_order_id = 0
 subscribe_token = ""
 ```
 如果开启 `Subscribe.SingleModel`：
 | 查询结果 | 行为 |
 | --- | --- |
 | 找到已有 anchor 订阅 | 下单路由为续费：`order_type=2`，保留新请求套餐 ID，设置 parent/order token |
 | 没找到已有订阅 | 保持新购：`order_type=1` |
 | 查询异常 | 返回数据库错误 |
 说明：即使是换套餐，只要单订阅模式已有订阅，也走续费语义，后续激活会更新套餐 ID 和流量配置。
 ### 2.5 非 SingleModel 的全局单订阅兜底
 如果未开启 `SingleModel`，且当前还是新购 `order_type=1`：
 - 查询 `effective_user_id` 名下已有付费订阅：
 ```sql
 user_id = effective_user_id
 AND token != ''
 AND (order_id > 0 OR token LIKE 'iap:%')
 ```
 | 查询结果 | 行为 |
 | --- | --- |
 | 找到已有订阅 | 路由为续费：`order_type=2`，用已有订阅 token |
 | 没找到 | 仍然新购 |
 目的：避免同一个权益归属用户购买不同套餐后出现多条订阅权益。
 ### 2.6 pending 订单处理
 当前只有一个分支会主动关闭旧 pending 单：
 ```text
 SingleModel = true
 AND order_type = 1
 AND 存在同 user_id + subscribe_id + status=1 的订单
 ```
 行为：
 - 关闭旧 pending 订单。
 - 继续创建新订单。
 注意：
 - 如果订单已被路由为 `order_type=2`，这里不会关闭旧 pending。
 - 非 `SingleModel` 下也不会走这段 pending 关闭逻辑。
 ### 2.7 套餐校验
 分支：
 | 条件 | 行为 |
 | --- | --- |
 | 套餐不存在 | 返回数据库错误 |
 | `sell=false` | 返回套餐不可售 |
 | 新购且库存为 `0` | 返回库存不足 |
 | 续费/换套餐 | 不检查库存为 0 的拦截分支 |
 ### 2.8 新用户优惠与新用户限定
 调用 `resolveNewUserDiscountEligibility`：
 | 分支 | 行为 |
 | --- | --- |
 | 解析失败 | 返回错误 |
 | 有折扣且符合条件 | 按折扣计算金额 |
 | 有折扣但不符合条件 | 按原价 |
 | 新用户限定且不是新用户窗口 | 新购事务内再次校验，不通过则失败 |
 ### 2.9 优惠券逻辑
 如果 `req.coupon` 为空：跳过。
 如果不为空：
 | 校验 | 不通过行为 |
 | --- | --- |
 | 优惠券存在 | 返回 `CouponNotExist` |
 | 总使用次数未超限 | 返回使用次数不足 |
 | 用户使用次数未超限 | 返回用户次数不足 |
 | 套餐适用 | 返回不适用 |
 通过后计算 `coupon_discount`，从订单金额中扣除。
 ### 2.10 支付手续费与礼品余额抵扣
 流程：
 1. 找支付方式。
 2. 如果金额大于 0，计算手续费并加到订单金额。
 3. 如果用户 `gift_amount > 0`，继续抵扣订单金额。
 4. 抵扣金额记录到订单 `gift_amount`。
 事务内如果有礼品余额抵扣：
 - 扣减用户 `gift_amount`。
 - 写 `system_logs` 的 gift reduce 日志。
 ### 2.11 `is_new` 首单标记
 创建订单前调用：
 ```sql
 SELECT COUNT(*)
 FROM `order`
 WHERE user_id = 当前付款用户
  AND status IN (2, 5)
 ```
 | 结果 | `is_new` |
 | --- | --- |
 | count = 0 | `true` |
 | count > 0 | `false` |
 注意：
 - 判断口径是付款用户 `user_id`，不是 `subscription_user_id`。
 - pending/closed 订单不影响 `is_new`。
 - 续费订单也可能是 `is_new=true`，例如单订阅模式下首次购买被路由为续费。
 ### 2.12 事务内创建订单
 事务内执行：
 1. 新购且套餐有 quota 时，再查一次 `effective_user_id` 名下订阅数量防并发。
 2. 新购且新用户限定时，再查一次新用户资格。
 3. 如有礼品余额抵扣，扣减余额并写日志。
 4. 新购且库存不是 `-1` 时扣库存。
 5. 插入订单。
 订单核心字段：
 | 字段 | 值 |
 | --- | --- |
 | `user_id` | 当前付款用户 |
 | `subscription_user_id` | 权益归属用户 |
 | `type` | 新购 `1` 或续费 `2` |
 | `subscribe_id` | 本次购买的套餐 ID |
 | `subscribe_token` | 续费时已有订阅 token |
 | `is_new` | 首单标记 |
 | `status` | `1` pending |
 ### 2.13 延迟关单任务
 订单创建成功后，发送 `DeferCloseOrder` 任务：
 - 延迟时间：15 分钟。
 - 作用：未支付订单自动关闭。
 ## 3. 订单支付后的激活逻辑
 入口：`ActivateOrderLogic.ProcessTask`
 ### 3.1 任务解析和订单领取
 分支：
 | 分支 | 行为 |
 | --- | --- |
 | payload 解析失败 | 记录错误，不重试 |
 | 订单不存在 | 返回错误，允许重试 |
 | 订单已 finished | 幂等跳过 |
 | 订单不是 paid | 跳过 |
 | paid 订单 | 原子更新为 claimed 状态后处理 |
 ### 3.2 按订单类型分发
 | 订单类型 | 处理函数 |
 | --- | --- |
 | 新购 `1` | `NewPurchase` |
 | 续费 `2` | `Renewal` |
 | 重置流量 `3` | `ResetTraffic` |
 | 充值 `4` | `Recharge` |
 | 兑换码 `5` | `RedemptionActivate` |
 处理成功后：
 1. 执行订阅合并兜底 `reconcilePostOrderSubscriptions`。
 2. 更新优惠券使用次数。
 3. 更新订单为 `finished`。
 如果处理失败：
 - 把订单从 claimed 释放回 paid。
 - 返回错误给队列重试。
 ## 4. 新购订单激活与订阅发放
 入口：`NewPurchase`
 ### 4.1 获取用户
 分支：
 | 订单 user_id | 行为 |
 | --- | --- |
 | 不为 0 | 查询已有用户 |
 | 为 0 | 从 Redis 临时订单创建游客用户 |
 游客订单创建用户时：
 - 创建用户和 auth method。
 - 生成 refer code。
 - 把订单 `user_id` 更新为新用户 ID。
 - 如果临时订单有邀请码，绑定 `referer_id`。
 ### 4.2 新用户限定激活时复查
 如果套餐是新用户限定，激活时再次校验：
 - 不符合则激活失败，订单回到 paid 等待重试/处理。
 - 符合继续。
 ### 4.3 SingleModel 下复用 anchor 订阅
 如果 `Subscribe.SingleModel=true`：
 | 分支 | 行为 |
 | --- | --- |
 | 找到 anchor 订阅 | 更新订单 parent_id；用续费逻辑延长/换套餐 |
 | 找不到 | 继续后续分支 |
 | 查询异常 | 记录错误，继续后续分支 |
 家庭组场景下查 anchor 的用户 ID：
 - 优先 `subscription_user_id`。
 - 没有则用 `user_id`。
 ### 4.4 复用赠送订阅
 如果还没有可复用订阅：
 - 查找权益归属用户名下 `order_id=0` 的赠送订阅。
 - 找到后将它升级为付费订阅：
  - `order_id` 改为当前订单 ID。
  - 延长到期时间。
  - 状态改为 active。
  - 如果套餐变更，更新套餐 ID、流量额度，并清空已用流量。
 ### 4.5 兜底复用已有订阅
 如果仍未复用到订阅：
 - 候选用户 ID：
  - `order.user_id`
  - 如果 `subscription_user_id` 存在且不同，也加入候选。
 - 查找这些用户名下 `token != ''` 的订阅。
 找到后：
 - 如果订阅 owner 不是当前 `subscription_user_id`，先把 `user_id` 修正为权益归属用户。
 - 用续费逻辑延长/换套餐。
 目的：家庭组绑定前后 owner 变化时，也尽量复用旧记录，避免创建重复订阅。
 ### 4.6 创建新订阅
 如果以上都没有复用成功，才创建新 `user_subscribe`：
 | 字段 | 值 |
 | --- | --- |
 | `user_id` | `subscription_user_id`，没有则 `order.user_id` |
 | `order_id` | 当前订单 ID |
 | `subscribe_id` | 当前订单套餐 ID |
 | `start_time` | 当前时间 |
 | `expire_time` | 按套餐时间单位和数量计算 |
 | `traffic` | 套餐流量 |
 | `token` | 基于订单号生成 |
 | `uuid` | 新 UUID |
 | `status` | `1` active |
 创建前如果套餐有 quota，会再按订阅 owner 统计数量。
 ### 4.7 新购激活后的异步逻辑
 订阅发放后：
 1. 后台触发用户分组重算。
 2. 后台异步处理邀请佣金和赠送时间。
 3. 清套餐缓存。
 注意：邀请逻辑在 goroutine 中执行，不阻塞订单激活。
 ## 5. 续费/换套餐激活逻辑
 入口：`Renewal`
 ### 5.1 获取用户和订阅
 - 查询订单 `user_id` 对应用户。
 - 通过 `subscribe_token` 查订阅。
 - 查询订单 `subscribe_id` 对应套餐。
 ### 5.2 Apple IAP 与普通续费分支
 | 分支 | 行为 |
 | --- | --- |
 | `iap_expire_at > 0` | 使用 IAP 到期时间兜底，但仍按累计加时语义 |
 | 普通续费 | `updateSubscriptionForRenewal` |
 ### 5.3 普通续费/换套餐规则
 `updateSubscriptionForRenewal`：
 - 如果当前订阅已过期，先把基准时间改为现在。
 - 如果套餐 ID 变化：
  - 更新订阅套餐 ID。
  - 更新流量额度。
  - 清空已用流量。
 - 如果套餐没变：
  - 如果套餐设置 renewal reset，或今天是重置日，则清空已用流量。
 - 清理 `finished_at`。
 - `order_id` 改为当前订单 ID。
 - 按套餐时间单位和数量延长到期时间。
 - 状态改为 active。
 - 清空过期流量字段。
 ### 5.4 续费后的邀请逻辑
 续费成功后也会调用 `handleCommission`：
 - 是否发佣金由邀请配置和 `order.is_new` 决定。
 - 是否赠时同样由邀请配置和 `order.is_new` 决定。
 注意：如果 `OnlyFirstPurchase=true`，非首单续费通常不会发佣金，也不会赠首单时间。
 ## 6. 邀请关系绑定逻辑
 ### 6.1 注册/登录时的邀请码
 新用户注册、游客订单创建用户时，如果带邀请码：
 - 根据邀请码查邀请人。
 - 设置新用户 `referer_id = 邀请人 ID`。
 ### 6.2 用户后绑邀请码
 入口：`BindInviteCode`
 分支：
 | 分支 | 行为 |
 | --- | --- |
 | 当前用户不存在 | 返回无权限 |
 | 当前用户已有 `referer_id` | 返回已绑定 |
 | 邀请码不存在 | 返回邀请码错误 |
 | 邀请码属于自己 | 返回不允许绑定自己 |
 | 通过 | 更新当前用户 `referer_id` |
 注意：
 - `referer_id` 始终记录实际邀请码所有者。
 - 邀请人是家庭成员时，`referer_id` 仍然是该成员 ID，不自动改为家主 ID。
 ## 7. 邀请佣金与赠送时间逻辑
 入口：`handleCommission(userInfo, orderInfo)`
 这里的 `userInfo` 是订单付款用户，也就是被邀请人。
 ### 7.1 总入口分支
 先调用 `shouldProcessCommission(userInfo, orderInfo.IsNew)`。
 | 结果 | 行为 |
 | --- | --- |
 | `false` | 不发佣金；如果 `is_new=true`，走双方赠时 |
 | `true` | 发佣金；如果 `is_new=true`，被邀请人赠时 |
 ### 7.2 什么时候发佣金
 `shouldProcessCommission` 规则：
 | 条件 | 结果 |
 | --- | --- |
 | 被邀请人为空 | 不发 |
 | 被邀请人 `referer_id=0` | 不发 |
 | 查不到邀请人 | 不发 |
 | 邀请人自定义 `referral_percentage > 0`，且只首购但不是首单 | 不发 |
 | 邀请人自定义 `referral_percentage > 0`，且通过首购限制 | 发佣金 |
 | 邀请人无自定义比例，系统 `ReferralPercentage=0` | 不发 |
 | 系统 `OnlyFirstPurchase=true` 且不是首单 | 不发 |
 | 系统有比例且通过首购限制 | 发佣金 |
 ### 7.3 发佣金路径
 如果 `shouldProcessCommission=true`：
 1. 查询邀请人，也就是 `userInfo.referer_id` 对应用户。
 2. 佣金比例：
   - 邀请人自定义比例优先。
   - 否则用系统配置 `Invite.ReferralPercentage`。
 3. 佣金金额：
 ```text
 (order.amount - order.fee_amount) * referral_percentage / 100
 ```
 4. 事务内幂等检查：
   - 如果已有同订单佣金日志，则跳过。
   - 否则增加邀请人的 `commission`。
   - 写 `system_logs type=33` 佣金日志。
 5. 更新邀请人缓存。
 6. 如果 `order.is_new=true`：
   - 给被邀请人赠送订阅时间。
 当前保持不变的行为：
 - 邀请人是家庭成员时，佣金仍然给实际邀请人成员本人。
 - 佣金不归并到家主。
 - 有佣金路径下，邀请人不额外赠送订阅时间。
 ### 7.4 不发佣金路径
 如果 `shouldProcessCommission=false`：
 | `order.is_new` | 行为 |
 | --- | --- |
 | `true` | 被邀请人和邀请人双方赠送订阅时间 |
 | `false` | 不赠送时间 |
 双方赠时具体为：
 1. 被邀请人赠时：
   - 如果被邀请人是家庭成员，加到被邀请人家主套餐。
   - 否则加到被邀请人本人套餐。
 2. 邀请人赠时：
   - 如果邀请人是家庭成员，加到邀请人家主套餐。
   - 否则加到邀请人本人套餐。
 ## 8. 赠送时间目标解析
 入口：`resolveGiftTargetUser(source, forcedOwnerID)`
 ### 8.1 强制 owner 分支
 如果 `forcedOwnerID > 0`：
 - 赠送目标直接使用 `forcedOwnerID`。
 - 典型场景：订单里已有 `subscription_user_id`。
 - 这保证了家庭成员购买时，被邀请人的赠时落到家主。
 ### 8.2 自动家庭组解析分支
 如果没有强制 owner：
 - 调用 `ResolveEntitlementUser(source.Id)`。
 - 如果 source 是有效家庭成员，目标改为家主。
 - 否则目标为本人。
 典型场景：
 - 无佣金路径下，邀请人也赠时。
 - 邀请人如果是家庭成员，赠时会加到邀请人家主套餐。
 ### 8.3 目标用户查询失败
 如果解析出来的目标用户查不到：
 - 记录错误日志。
 - 回退为 source 本人。
 ## 9. 赠送时间落库逻辑
 入口：`grantGiftDays(u, days, orderNo, remark)`
 ### 9.1 空值和配置分支
 | 条件 | 行为 |
 | --- | --- |
 | 目标用户为空 | 直接返回，不写日志 |
 | `days <= 0` | 直接返回，不写日志 |
 ### 9.2 幂等检查
 按下面条件查 gift 日志：
 ```sql
 type = 34
 AND object_id = 目标用户 ID
 AND content LIKE '%订单号%'
 ```
 | 结果 | 行为 |
 | --- | --- |
 | 已存在 | 跳过，不重复赠时 |
 | 不存在 | 继续 |
 ### 9.3 查目标用户活跃订阅
 调用 `FindActiveSubscribe`。
 当前活跃口径：
 ```sql
 user_id = 目标用户 ID
 AND status IN (0, 1)
 AND (
  expire_time > NOW()
  OR expire_time = FROM_UNIXTIME(0)
 )
 ```
 说明：
 - `expire_time > NOW()` 是普通未过期订阅。
 - `expire_time = FROM_UNIXTIME(0)` 是永久/不限时订阅。
 ### 9.4 没有活跃订阅
 如果查不到活跃订阅：
 - 不创建新订阅。
 - 写一条 `system_logs type=34` 日志。
 - 日志 remark 为：
 ```text
 邀请赠送 skipped: no active subscription
 ```
 这表示邀请赠时触发过，但目标用户当时没有可加时的套餐。
 ### 9.5 找到普通活跃订阅
 如果目标订阅不是永久订阅：
 - `expire_time += days * 24h`
 - 更新订阅。
 - 写 `system_logs type=34` gift increase 日志。
 ### 9.6 找到永久订阅
 如果目标订阅 `expire_time = FROM_UNIXTIME(0)`：
 - 不改变 `expire_time`，因为永久订阅没有可延长的到期时间。
 - 仍写 `system_logs type=34` gift increase 日志，表示赠送逻辑已识别并处理。
 ### 9.7 赠时失败日志
 发佣金路径和无佣金路径都会检查 `grantGiftDays` 返回错误。
 如果出错，会写应用日志：
 ```text
 Grant invite gift days failed
 ```
 附带字段：
 - `stage`
 - `target_user_id`
 - `order_no`
 - `error`
 ## 10. 家庭组下的完整分支示例
 ### 10.1 被邀请人是普通用户，邀请人普通用户，有佣金
 条件：
 - 被邀请人 `referer_id != 0`
 - 系统或邀请人佣金比例大于 0
 - `order.is_new=true`
 结果：
 - 佣金给邀请人本人。
 - 被邀请人本人套餐加赠送时间。
 - 邀请人不加赠送时间。
 ### 10.2 被邀请人是家庭成员，邀请人普通用户，有佣金
 结果：
 - 佣金给邀请人本人。
 - 被邀请人的赠送时间加到被邀请人家主套餐。
 - 被邀请人成员本人不单独加订阅时间。
 ### 10.3 被邀请人普通用户，邀请人是家庭成员，有佣金
 结果：
 - 佣金给邀请人成员本人。
 - 被邀请人本人套餐加赠送时间。
 - 邀请人不加赠送时间。
 - 邀请人家主不拿佣金，也不因该佣金路径加赠时。
 ### 10.4 被邀请人是家庭成员，邀请人也是家庭成员，有佣金
 结果：
 - 佣金给邀请人成员本人。
 - 被邀请人的赠送时间加到被邀请人家主套餐。
 - 邀请人不加赠送时间。
 - 邀请人家主不拿佣金。
 ### 10.5 无佣金路径，被邀请人普通用户，邀请人普通用户
 触发条件示例：
 - `ReferralPercentage=0`
 - 或因首购限制导致不发佣金
 - 且 `order.is_new=true`
 结果：
 - 被邀请人本人套餐加赠送时间。
 - 邀请人本人套餐加赠送时间。
 ### 10.6 无佣金路径，被邀请人是家庭成员
 结果：
 - 被邀请人的赠送时间加到被邀请人家主套餐。
 - 邀请人的赠时按邀请人自己的家庭归属解析。
 ### 10.7 无佣金路径，邀请人是家庭成员
 结果：
 - 被邀请人的赠时按被邀请人的家庭归属解析。
 - 邀请人的赠送时间加到邀请人家主套餐。
 - 邀请人成员本人不单独加订阅时间。
 ### 10.8 被邀请人没有活跃订阅
 结果：
 - 不创建新订阅。
 - 写 skipped gift 日志。
 - 后续即使用户后来有订阅，也不会自动补赠，除非另行补偿。
 ### 10.9 被邀请人或目标家主是永久订阅
 结果：
 - 识别为活跃订阅。
 - 不改变到期时间。
 - 写 gift increase 日志。
 ## 11. 排查 SQL
 ### 11.1 查邀请配置
 ```sql
 SELECT `key`, `value`, `updated_at`
 FROM system
 WHERE category = 'invite'
  AND `key` IN ('GiftDays', 'OnlyFirstPurchase', 'ReferralPercentage');
 ```
 ### 11.2 查某邀请人的被邀请用户
 ```sql
 SELECT id, referer_id, created_at
 FROM `user`
 WHERE referer_id = 23944
 ORDER BY id DESC
 LIMIT 100;
 ```
 ### 11.3 查被邀请人的订单和首单标记
 ```sql
 SELECT u.id AS invited_user_id,
       o.id AS order_id,
       o.order_no,
       o.type,
       o.status,
       o.amount,
       o.is_new,
       o.subscribe_id,
       o.subscription_user_id,
       o.created_at
 FROM `user` u
 LEFT JOIN `order` o
  ON o.user_id = u.id
 AND o.type IN (1, 2)
 WHERE u.referer_id = 23944
 ORDER BY u.id DESC, o.id ASC
 LIMIT 200;
 ```
 ### 11.4 查某订单佣金和赠时日志
 ```sql
 SELECT id, type, object_id, content, created_at
 FROM system_logs
 WHERE content LIKE '%202604281812556044982351822%'
 ORDER BY id DESC;
 ```
 ### 11.5 查某用户订阅
 ```sql
 SELECT id, user_id, order_id, subscribe_id, status,
       expire_time, finished_at, token, created_at, updated_at
 FROM user_subscribe
 WHERE user_id = 24425
 ORDER BY id DESC;
 ```
 ### 11.6 查首单但没有赠时日志的被邀请人
 ```sql
 SELECT first_orders.user_id AS invited_user_id,
       first_orders.order_no,
       first_orders.is_new,
       first_orders.status,
       first_orders.subscription_user_id,
       first_orders.created_at,
       (
         SELECT COUNT(*)
         FROM system_logs sl
         WHERE sl.type = 34
           AND sl.content LIKE CONCAT('%', first_orders.order_no, '%')
       ) AS gift_log_count,
       (
         SELECT COUNT(*)
         FROM system_logs sl
         WHERE sl.type = 33
           AND sl.content LIKE CONCAT('%', first_orders.order_no, '%')
       ) AS commission_log_count
 FROM (
  SELECT o.*
  FROM `order` o
  JOIN (
    SELECT user_id, MIN(id) AS first_order_id
    FROM `order`
    WHERE type IN (1, 2)
      AND status IN (2, 5)
    GROUP BY user_id
  ) fo ON fo.first_order_id = o.id
 ) first_orders
 JOIN `user` u ON u.id = first_orders.user_id
 WHERE u.referer_id = 23944
 ORDER BY first_orders.created_at DESC
 LIMIT 100;
 ```
 ## 12. 部署注意事项
 邀请配置存在两层状态：
 1. Redis 缓存：`system:invite_config`
 2. 服务进程内存：`svc.Config.Invite`
 如果直接修改数据库或 Redis，已经运行的 `ppanel-server` 进程不会自动刷新内存配置。订单激活和赠时发生在服务进程/队列 worker 内，所以修改邀请配置或部署赠时逻辑后，需要重启服务。
 推荐步骤：
 ```bash
 docker exec ppanel-redis redis-cli DEL system:invite_config system:global_config
 docker restart ppanel-server
 ```
 确认启动时间：
 ```bash
 docker inspect --format '{{.Name}} {{.State.StartedAt}} {{.Config.Image}}' ppanel-server
 docker ps --filter name=ppanel-server
 ```
@@ -0,0 +1,184 @@
 # TAPI 文件上传接入说明
 本文档说明 `https://tapi.hifast.biz/v1/public/file/upload` 相关上传接口的推荐接入方式、签名规则与常见排查方式。
 ## 总览
 上传能力包含两类接入方式：
 - 推荐方式：`init -> S3 PUT -> complete`
 - 兼容方式：`/upload` multipart 直传
 推荐优先使用预签名三段式，因为：
 - 现有签名串包含 `BODY_SHA256`
 - `/upload` 是 `multipart/form-data`
 - multipart 原始 body 的签名和调试成本更高
 - `init` 和 `complete` 是 JSON，更适合客户端和 Apifox 调试
 ## 签名生效逻辑
 项目保持现有旧逻辑，不做强制签名改造：
 - `Signature.EnableSignature = false` 时：不校验签名
 - `Signature.EnableSignature = true` 且未携带 `X-App-Id` 时：不校验签名，兼容老客户端
 - `Signature.EnableSignature = true` 且携带 `X-App-Id` 时：必须同时携带并校验
  - `X-Timestamp`
  - `X-Nonce`
  - `X-Signature`
 这意味着：
 - 新客户端建议始终带完整签名头
 - 老客户端如果没有 `X-App-Id`，仍可按旧逻辑访问
 ## 签名头定义
 - `X-App-Id`: 客户端标识，例如 `ios-client`
 - `X-Timestamp`: Unix 秒级时间戳
 - `X-Nonce`: 每次请求唯一随机串
 - `X-Signature`: `HMAC-SHA256` 结果的十六进制小写字符串
 ## StringToSign 规则
 StringToSign 由下面 7 段按换行符 `\n` 拼接：
 ```text
 METHOD
 PATH
 CANONICAL_QUERY
 BODY_SHA256
 X-App-Id
 X-Timestamp
 X-Nonce
 ```
 说明：
 - `METHOD`：HTTP 方法大写，例如 `POST`
 - `PATH`：请求路径，例如 `/v1/public/file/upload/init`
 - `CANONICAL_QUERY`：按 key 排序后的 query string，没有 query 则为空字符串
 - `BODY_SHA256`：请求体原始字节的 SHA-256 十六进制小写
 - 其余三项直接使用请求头值
 签名计算方式：
 ```text
 signature = hex_lower(HMAC_SHA256(app_secret, string_to_sign))
 ```
 时间窗与防重放：
 - `X-Timestamp` 默认有效时间窗是 300 秒
 - `X-Nonce` 在有效时间窗内不能重复使用
 ## 推荐接入：预签名三段式
 ### 1. 初始化上传
 请求：
 ```bash
 curl -X POST 'https://tapi.hifast.biz/v1/public/file/upload/init' \
  -H 'Accept: application/json, text/plain, */*' \
  -H 'Content-Type: application/json' \
  -H 'authorization: your-token' \
  -H 'X-App-Id: ios-client' \
  -H 'X-Timestamp: 1778776400' \
  -H 'X-Nonce: nonce-001' \
  -H 'X-Signature: your-signature' \
  -d '{
    "biz_type": "app-package",
    "file_name": "demo.zip",
    "content_type": "application/zip",
    "size": 123456,
    "sha256": ""
  }'
 ```
 典型返回：
 ```json
 {
  "code": 200,
  "msg": "success",
  "data": {
    "file_id": "c29274ee26ab5aa211e0396e",
    "object_key": "app-upload/app-package/519/2026/05/c29274ee26ab5aa211e0396e_demo.zip",
    "upload_url": "https://bucket.s3.ap-east-1.amazonaws.com/...",
    "method": "PUT",
    "headers": {
      "Content-Type": "application/zip"
    },
    "expired_at": 1778776715
  }
 }
 ```
 ### 2. 直传 S3
 这一步是直接上传二进制文件到 S3，不走业务签名中间件。
 ```bash
 curl -X PUT 'https://bucket.s3.ap-east-1.amazonaws.com/...' \
  -H 'Content-Type: application/zip' \
  --upload-file '/tmp/demo.zip'
 ```
 说明：
 - `Content-Type` 需和 `init` 返回的 `headers.Content-Type` 一致
 - `upload_url` 有过期时间，通常 300 秒
 - 成功时 S3 常见返回 `200` 或 `204`
 ### 3. 完成上传
 ```bash
 curl -X POST 'https://tapi.hifast.biz/v1/public/file/upload/complete' \
  -H 'Accept: application/json, text/plain, */*' \
  -H 'Content-Type: application/json' \
  -H 'authorization: your-token' \
  -H 'X-App-Id: ios-client' \
  -H 'X-Timestamp: 1778776405' \
  -H 'X-Nonce: nonce-002' \
  -H 'X-Signature: your-signature' \
  -d '{
    "file_id": "c29274ee26ab5aa211e0396e"
  }'
 ```
 ## 兼容接入：单接口 multipart 直传
 接口：
 - `POST /v1/public/file/upload`
 表单字段：
 - `biz_type`
 - `file`
 说明：
 - 该接口继续保留，兼容旧客户端
 - 如果请求带了 `X-App-Id`，就按现有逻辑验签
 - 如果没有 `X-App-Id`，仍按旧逻辑放行
 - 如果要给该接口加签，签名时必须对原始 multipart body 计算 `BODY_SHA256`
 ## 常见错误码
 - `200`: 成功
 - `400`: 参数错误
 - `40008`: 缺少签名头
 - `40009`: 签名已过期
 - `40010`: 签名无效
 - `40011`: nonce 重放
 - `10001`: 上传元数据不存在或对象不存在
 ## 排查建议
 - `40008`：确认带了 `X-App-Id` 后，也同时带上 `X-Timestamp / X-Nonce / X-Signature`
 - `40009`：检查客户端时间是否偏差过大
 - `40010`：确认 `PATH`、query 排序、body 原始字节、secret 是否完全一致
 - `40011`：确保每次请求都生成新的 `X-Nonce`
 - `complete` 失败：确认 S3 `PUT` 已成功，且上传大小与 `init.size` 一致
@@ -6,9 +6,9 @@
 # 4. 运行: docker-compose -f docker-compose.cloud.yml up -d
 #
 # 网络说明：
-#   ppanel-server 使用 host 网络（可出外网，访问 MySQL/Redis/Tempo 用 127.0.0.1）
+#   ppanel-server 使用 host 网络（可出外网，直接访问 AWS RDS / 本机 Redis）
-#   监控服务（MySQL/Redis/Loki/Tempo/Grafana/Prometheus）在 ppanel_net bridge 网络中
+#   监控服务（Loki/Tempo/Grafana/Prometheus）在 ppanel_net bridge 网络中
-#   MySQL(3306)/Redis(6379)/Tempo(4317) 将端口映射到 127.0.0.1，ppanel-server 通过 host 网络访问
+#   Tempo(4317) 将端口映射到 127.0.0.1，ppanel-server 通过 host 网络访问
 #   监控端口绑定 127.0.0.1，需通过 SSH 隧道或 Nginx 反代访问
 #
 # 未来多开 ppanel-server 时：
@@ -18,10 +18,11 @@
 services:
  # ----------------------------------------------------
  # 1. 业务后端 (PPanel Server)
-  #    host 网络：可出外网，通过 127.0.0.1 访问 MySQL/Redis/Tempo
+  #    host 网络：可出外网，直接访问 AWS RDS/Redis；通过 127.0.0.1 访问 Tempo
  #    PPANEL_SERVER_TAG 由 CI/CD 传入不可变镜像标签（如 git SHA）
  # ----------------------------------------------------
  ppanel-server:
-    image: registry.kxsw.us/vpn-server:${PPANEL_SERVER_TAG:-latest}
+    image: registry.kxsw.us/vpn-server:${PPANEL_SERVER_TAG:?please set PPANEL_SERVER_TAG to an immutable image tag}
    container_name: ppanel-server
    restart: always
    volumes:
@@ -37,10 +38,6 @@ services:
        soft: 65535
        hard: 65535
    depends_on:
      mysql:
        condition: service_healthy
      redis:
        condition: service_healthy
      tempo:
        condition: service_started
    logging:
@@ -50,81 +47,7 @@ services:
        max-file: "3"
  # ----------------------------------------------------
-  # 2. MySQL Database
+  # 2. Tempo (链路追踪存储)
  # ----------------------------------------------------
  mysql:
    image: mysql:8.0
    container_name: ppanel-mysql
    restart: always
    ports:
      - "3306:3306"  # 仅宿主机可访问，ppanel-server(host网络)通过127.0.0.1连接
    environment:
      MYSQL_ROOT_PASSWORD: "${MYSQL_ROOT_PASSWORD:?请在 .env 文件中设置 MYSQL_ROOT_PASSWORD}"
      MYSQL_DATABASE: "ppanel"
      TZ: Asia/Shanghai
    command:
      - --default-authentication-plugin=mysql_native_password
      - --innodb_buffer_pool_size=16G
      - --innodb_buffer_pool_instances=16
      - --innodb_log_file_size=2G
      - --innodb_flush_log_at_trx_commit=2
      - --innodb_io_capacity=5000
      - --max_connections=5000
    volumes:
      - mysql_data:/var/lib/mysql
    ulimits:
      nproc: 65535
      nofile:
        soft: 65535
        hard: 65535
    networks:
      - ppanel_net
    healthcheck:
      test: ["CMD", "mysqladmin", "ping", "-h", "localhost", "-uroot", "-p${MYSQL_ROOT_PASSWORD}"]
      interval: 10s
      timeout: 5s
      retries: 5
    logging:
      driver: "json-file"
      options:
        max-size: "10m"
        max-file: "3"
  # ----------------------------------------------------
  # 3. Redis
  # ----------------------------------------------------
  redis:
    image: redis:8.2.1
    container_name: ppanel-redis
    restart: always
    ports:
      - "127.0.0.1:6379:6379"  # 仅宿主机可访问，ppanel-server(host网络)通过127.0.0.1连接
    command:
      - redis-server
      - --tcp-backlog 65535
      - --maxmemory-policy allkeys-lru
    volumes:
      - redis_data:/data
    ulimits:
      nproc: 65535
      nofile:
        soft: 65535
        hard: 65535
    networks:
      - ppanel_net
    healthcheck:
      test: ["CMD", "redis-cli", "ping"]
      interval: 10s
      timeout: 5s
      retries: 5
    logging:
      driver: "json-file"
      options:
        max-size: "10m"
        max-file: "3"
  # ----------------------------------------------------
  # 4. Tempo (链路追踪存储)
  # ----------------------------------------------------
  tempo:
    image: grafana/tempo:2.4.1
@@ -148,7 +71,7 @@ services:
        max-file: "3"
  # ----------------------------------------------------
-  # 5. Loki (日志存储)
+  # 3. Loki (日志存储)
  # ----------------------------------------------------
  loki:
    image: grafana/loki:3.0.0
@@ -168,7 +91,7 @@ services:
        max-file: "3"
  # ----------------------------------------------------
-  # 6. Promtail (日志采集)
+  # 4. Promtail (日志采集)
  # ----------------------------------------------------
  promtail:
    image: grafana/promtail:3.0.0
@@ -192,20 +115,27 @@ services:
        max-file: "3"
  # ----------------------------------------------------
-  # 7. Grafana (可观测面板)
+  # 5. Grafana (可观测面板)
  # 访问: ssh -L 3333:localhost:3333 your-server 后浏览器打开 http://localhost:3333
  # 或配置 Nginx 反代（建议加认证）
  # ----------------------------------------------------
  grafana:
-    image: grafana/grafana:latest
+    image: grafana/grafana:13.0.1
    container_name: ppanel-grafana
    restart: always
    ports:
-      - "127.0.0.1:3333:3000"  # 仅本机可访问，需 SSH 隧道或 Nginx 反代
+      - "3333:3000" # 仅本机可访问，需 SSH 隧道或 Nginx 反代
    environment:
      - GF_SECURITY_ADMIN_PASSWORD=${GRAFANA_PASSWORD:?请在 .env 文件中设置 GRAFANA_PASSWORD}
      - GF_USERS_ALLOW_SIGN_UP=false
      - GF_SERVER_DOMAIN=${GRAFANA_DOMAIN:-logsx.hifast.biz}
      - GF_SERVER_ROOT_URL=${GRAFANA_ROOT_URL:-https://logsx.hifast.biz}
      - GF_FEATURE_TOGGLES_ENABLE=appObservability
      - AWS_REGION=${AWS_REGION:-ap-east-1}
      - AWS_DEFAULT_REGION=${AWS_REGION:-ap-east-1}
      - AWS_ACCESS_KEY_ID=${AWS_ACCESS_KEY_ID:-}
      - AWS_SECRET_ACCESS_KEY=${AWS_SECRET_ACCESS_KEY:-}
      - AWS_SESSION_TOKEN=${AWS_SESSION_TOKEN:-}
    volumes:
      - grafana_data:/var/lib/grafana
      - ./grafana/provisioning:/etc/grafana/provisioning
@@ -222,10 +152,10 @@ services:
        max-file: "3"
  # ----------------------------------------------------
-  # 8. Prometheus (指标采集)
+  # 6. Prometheus (指标采集)
  # ----------------------------------------------------
  prometheus:
-    image: prom/prometheus:latest
+    image: prom/prometheus:v3.11.3
    container_name: ppanel-prometheus
    restart: always
    ports:
@@ -247,29 +177,10 @@ services:
        max-file: "3"
  # ----------------------------------------------------
-  # 9. Redis Exporter
+  # 7. Nginx Exporter (监控宿主机 Nginx)
  # ----------------------------------------------------
  redis-exporter:
    image: oliver006/redis_exporter:latest
    container_name: ppanel-redis-exporter
    restart: always
    environment:
      - REDIS_ADDR=redis://redis:6379
    networks:
      - ppanel_net
    depends_on:
      - redis
    logging:
      driver: "json-file"
      options:
        max-size: "10m"
        max-file: "3"
  # ----------------------------------------------------
  # 10. Nginx Exporter (监控宿主机 Nginx)
  # ----------------------------------------------------
  nginx-exporter:
-    image: nginx/nginx-prometheus-exporter:latest
+    image: nginx/nginx-prometheus-exporter:1.5.0
    container_name: ppanel-nginx-exporter
    restart: always
    command:
@@ -285,31 +196,10 @@ services:
        max-file: "3"
  # ----------------------------------------------------
-  # 11. MySQL Exporter
+  # 8. Node Exporter (宿主机监控)
  # ----------------------------------------------------
  mysql-exporter:
    image: prom/mysqld-exporter:latest
    container_name: ppanel-mysql-exporter
    restart: always
    command:
      - --config.my-cnf=/etc/.my.cnf
    volumes:
      - ./mysql/.my.cnf:/etc/.my.cnf:ro
    networks:
      - ppanel_net
    depends_on:
      - mysql
    logging:
      driver: "json-file"
      options:
        max-size: "10m"
        max-file: "3"
  # ----------------------------------------------------
  # 12. Node Exporter (宿主机监控)
  # ----------------------------------------------------
  node-exporter:
-    image: prom/node-exporter:latest
+    image: prom/node-exporter:v1.11.1
    container_name: ppanel-node-exporter
    restart: always
    volumes:
@@ -329,10 +219,10 @@ services:
        max-file: "3"
  # ----------------------------------------------------
-  # 13. cAdvisor (容器监控)
+  # 9. cAdvisor (容器监控)
  # ----------------------------------------------------
  cadvisor:
-    image: gcr.io/cadvisor/cadvisor:latest
+    image: gcr.io/cadvisor/cadvisor:v0.55.1
    container_name: ppanel-cadvisor
    restart: always
    volumes:
@@ -350,8 +240,6 @@ services:
        max-file: "3"
 volumes:
  mysql_data:
  redis_data:
  loki_data:
  grafana_data:
  prometheus_data:
@@ -0,0 +1,38 @@
 services:
  mysql:
    image: mysql:8.0
    container_name: ppanel-mysql
    restart: unless-stopped
    environment:
      MYSQL_ROOT_PASSWORD: ppanel_dev
      MYSQL_DATABASE: ppanel
      MYSQL_USER: ppanel
      MYSQL_PASSWORD: ppanel_dev
    ports:
      - "3306:3306"
    volumes:
      - ppanel_mysql_data:/var/lib/mysql
    command: --character-set-server=utf8mb4 --collation-server=utf8mb4_unicode_ci
    healthcheck:
      test: ["CMD", "mysqladmin", "ping", "-h", "localhost", "-u", "root", "-pppanel_dev"]
      interval: 10s
      timeout: 5s
      retries: 5
  redis:
    image: redis:7-alpine
    container_name: ppanel-redis
    restart: unless-stopped
    ports:
      - "6379:6379"
    volumes:
      - ppanel_redis_data:/data
    healthcheck:
      test: ["CMD", "redis-cli", "ping"]
      interval: 10s
      timeout: 3s
      retries: 5
 volumes:
  ppanel_mysql_data:
  ppanel_redis_data:
@@ -15,7 +15,7 @@ Logger: # 日志配置
  Level: debug # 日志级别: debug, info, warn, error, panic, fatal
 MySQL:
-  Addr: 103.150.215.44:3306 # host 网络模式; bridge 模式改为 mysql:3306
+  Addr: 45.43.29.127:3306 # host 网络模式; bridge 模式改为 mysql:3306
  Username: root # MySQL用户名
  Password: jpcV41ppanel # MySQL密码，与 .env MYSQL_ROOT_PASSWORD 一致
  Dbname: hifast # MySQL数据库名
@@ -61,6 +61,21 @@ Trace: # 链路追踪配置 (OpenTelemetry)
  Batcher: otlpgrpc    # 本地开发留空""; 生产填 otlpgrpc
  Endpoint: "127.0.0.1:4317" # host 网络模式; bridge 模式改为 tempo:4317
 S3:
  Enable: false
  Region: ""
  Bucket: ""
  Endpoint: ""
  AccessKey: ""
  SecretKey: ""
  SessionToken: ""
  Prefix: "app-upload"
  PublicBaseURL: ""
  UsePathStyle: false
  PresignExpireSeconds: 300
  MaxUploadSize: 104857600
  AllowedContentTypes: "application/zip,application/x-zip-compressed,application/gzip,application/x-gzip,application/octet-stream,text/plain,application/json"
 device:
  enable: true # 开启设备加密通信
  security_secret: "" # AES加密密钥，需要和App端一致，key=SHA256(security_secret)[:32]
@@ -1,13 +1,12 @@
 module github.com/perfect-panel/server
-go 1.23.3
+go 1.24
 require (
 	github.com/GUAIK-ORG/go-snowflake v0.0.0-20200116064823-220c4260e85f
 	github.com/alibabacloud-go/darabonba-openapi v0.1.18
 	github.com/alibabacloud-go/dysmsapi-20170525/v2 v2.0.18
 	github.com/alibabacloud-go/tea v1.2.2
 	github.com/alicebob/miniredis/v2 v2.34.0
 	github.com/anaskhan96/go-password-encoder v0.0.0-20201010210601-c765b799fd72
 	github.com/andybalholm/brotli v1.1.1
 	github.com/forgoer/openssl v1.6.0
@@ -32,7 +31,6 @@ require (
 	github.com/smartwalle/alipay/v3 v3.2.23
 	github.com/spf13/cast v1.7.0 // indirect
 	github.com/spf13/cobra v1.8.1
 	github.com/stretchr/testify v1.10.0
 	github.com/stripe/stripe-go/v81 v81.1.0
 	github.com/twilio/twilio-go v1.23.11
 	go.opentelemetry.io/otel v1.29.0
@@ -51,15 +49,19 @@ require (
 	gopkg.in/yaml.v3 v3.0.1
 	gorm.io/driver/mysql v1.5.7
 	gorm.io/gorm v1.30.0
 	gorm.io/plugin/soft_delete v1.2.1
 	k8s.io/apimachinery v0.31.1
 )
 require (
 	github.com/Masterminds/sprig/v3 v3.3.0
 	github.com/aws/aws-sdk-go-v2 v1.41.7
 	github.com/aws/aws-sdk-go-v2/config v1.32.17
 	github.com/aws/aws-sdk-go-v2/credentials v1.19.16
 	github.com/aws/aws-sdk-go-v2/service/s3 v1.101.0
 	github.com/fatih/color v1.18.0
 	github.com/goccy/go-json v0.10.4
 	github.com/golang-migrate/migrate/v4 v4.18.2
 	github.com/mojocn/base64Captcha v1.3.8
 	github.com/oschwald/geoip2-golang v1.13.0
 	github.com/spaolacci/murmur3 v1.1.0
 	google.golang.org/grpc v1.64.1
@@ -79,8 +81,21 @@ require (
 	github.com/alibabacloud-go/tea-utils v1.4.5 // indirect
 	github.com/alibabacloud-go/tea-utils/v2 v2.0.7 // indirect
 	github.com/alibabacloud-go/tea-xml v1.1.3 // indirect
 	github.com/alicebob/gopher-json v0.0.0-20230218143504-906a9b012302 // indirect
 	github.com/aliyun/credentials-go v1.3.10 // indirect
 	github.com/aws/aws-sdk-go-v2/aws/protocol/eventstream v1.7.10 // indirect
 	github.com/aws/aws-sdk-go-v2/feature/ec2/imds v1.18.23 // indirect
 	github.com/aws/aws-sdk-go-v2/internal/configsources v1.4.23 // indirect
 	github.com/aws/aws-sdk-go-v2/internal/endpoints/v2 v2.7.23 // indirect
 	github.com/aws/aws-sdk-go-v2/internal/v4a v1.4.24 // indirect
 	github.com/aws/aws-sdk-go-v2/service/internal/accept-encoding v1.13.9 // indirect
 	github.com/aws/aws-sdk-go-v2/service/internal/checksum v1.9.15 // indirect
 	github.com/aws/aws-sdk-go-v2/service/internal/presigned-url v1.13.23 // indirect
 	github.com/aws/aws-sdk-go-v2/service/internal/s3shared v1.19.23 // indirect
 	github.com/aws/aws-sdk-go-v2/service/signin v1.0.11 // indirect
 	github.com/aws/aws-sdk-go-v2/service/sso v1.30.17 // indirect
 	github.com/aws/aws-sdk-go-v2/service/ssooidc v1.35.21 // indirect
 	github.com/aws/aws-sdk-go-v2/service/sts v1.42.1 // indirect
 	github.com/aws/smithy-go v1.25.1 // indirect
 	github.com/boj/redistore v0.0.0-20180917114910-cd5dcc76aeff // indirect
 	github.com/bytedance/sonic v1.12.7 // indirect
 	github.com/bytedance/sonic/loader v0.2.3 // indirect
@@ -88,7 +103,6 @@ require (
 	github.com/cespare/xxhash/v2 v2.3.0 // indirect
 	github.com/clbanning/mxj/v2 v2.5.6 // indirect
 	github.com/cloudwego/base64x v0.1.4 // indirect
 	github.com/davecgh/go-spew v1.1.2-0.20180830191138-d8f796af33cc // indirect
 	github.com/dgryski/go-rendezvous v0.0.0-20200823014737-9f7001d12a5f // indirect
 	github.com/gabriel-vasile/mimetype v1.4.8 // indirect
 	github.com/gin-contrib/sse v1.0.0 // indirect
@@ -114,27 +128,22 @@ require (
 	github.com/leodido/go-urn v1.4.0 // indirect
 	github.com/mattn/go-colorable v0.1.13 // indirect
 	github.com/mattn/go-isatty v0.0.20 // indirect
 	github.com/mattn/go-sqlite3 v1.14.22 // indirect
 	github.com/mitchellh/copystructure v1.2.0 // indirect
 	github.com/mitchellh/reflectwalk v1.0.2 // indirect
 	github.com/modern-go/concurrent v0.0.0-20180306012644-bacd9c7ef1dd // indirect
 	github.com/modern-go/reflect2 v1.0.2 // indirect
 	github.com/mojocn/base64Captcha v1.3.8 // indirect
 	github.com/openzipkin/zipkin-go v0.4.2 // indirect
 	github.com/oschwald/maxminddb-golang v1.13.0 // indirect
 	github.com/pelletier/go-toml/v2 v2.2.3 // indirect
 	github.com/pmezard/go-difflib v1.0.1-0.20181226105442-5d4384ee4fb2 // indirect
 	github.com/robfig/cron/v3 v3.0.1 // indirect
 	github.com/shopspring/decimal v1.4.0 // indirect
 	github.com/smartwalle/ncrypto v1.0.4 // indirect
 	github.com/smartwalle/ngx v1.0.9 // indirect
 	github.com/smartwalle/nsign v1.0.9 // indirect
 	github.com/spf13/pflag v1.0.5 // indirect
 	github.com/stretchr/objx v0.5.2 // indirect
 	github.com/tjfoc/gmsm v1.4.1 // indirect
 	github.com/twitchyliquid64/golang-asm v0.15.1 // indirect
 	github.com/ugorji/go/codec v1.2.12 // indirect
 	github.com/yuin/gopher-lua v1.1.1 // indirect
 	go.opentelemetry.io/otel/exporters/otlp/otlptrace v1.29.0 // indirect
 	go.opentelemetry.io/otel/metric v1.29.0 // indirect
 	go.opentelemetry.io/proto/otlp v1.3.1 // indirect
@@ -150,5 +159,4 @@ require (
 	google.golang.org/genproto/googleapis/rpc v0.0.0-20240513163218-0867130af1f8 // indirect
 	gopkg.in/alexcesaro/quotedprintable.v3 v3.0.0-20150716171945-2caba252f4dc // indirect
 	gopkg.in/ini.v1 v1.67.0 // indirect
 	gorm.io/driver/sqlite v1.6.0 // indirect
 )
@@ -52,10 +52,6 @@ github.com/alibabacloud-go/tea-utils/v2 v2.0.7/go.mod h1:qxn986l+q33J5VkialKMqT/
 github.com/alibabacloud-go/tea-xml v1.1.2/go.mod h1:Rq08vgCcCAjHyRi/M7xlHKUykZCEtyBy9+DPF6GgEu8=
 github.com/alibabacloud-go/tea-xml v1.1.3 h1:7LYnm+JbOq2B+T/B0fHC4Ies4/FofC4zHzYtqw7dgt0=
 github.com/alibabacloud-go/tea-xml v1.1.3/go.mod h1:Rq08vgCcCAjHyRi/M7xlHKUykZCEtyBy9+DPF6GgEu8=
 github.com/alicebob/gopher-json v0.0.0-20230218143504-906a9b012302 h1:uvdUDbHQHO85qeSydJtItA4T55Pw6BtAejd0APRJOCE=
 github.com/alicebob/gopher-json v0.0.0-20230218143504-906a9b012302/go.mod h1:SGnFV6hVsYE877CKEZ6tDNTjaSXYUk6QqoIK6PrAtcc=
 github.com/alicebob/miniredis/v2 v2.34.0 h1:mBFWMaJSNL9RwdGRyEDoAAv8OQc5UlEhLDQggTglU/0=
 github.com/alicebob/miniredis/v2 v2.34.0/go.mod h1:kWShP4b58T1CW0Y5dViCd5ztzrDqRWqM3nksiyXk5s8=
 github.com/aliyun/credentials-go v1.1.2/go.mod h1:ozcZaMR5kLM7pwtCMEpVmQ242suV6qTJya2bDq4X1Tw=
 github.com/aliyun/credentials-go v1.3.6/go.mod h1:1LxUuX7L5YrZUWzBrRyk0SwSdH4OmPrib8NVePL3fxM=
 github.com/aliyun/credentials-go v1.3.10 h1:45Xxrae/evfzQL9V10zL3xX31eqgLWEaIdCoPipOEQA=
@@ -64,6 +60,42 @@ github.com/anaskhan96/go-password-encoder v0.0.0-20201010210601-c765b799fd72 h1:
 github.com/anaskhan96/go-password-encoder v0.0.0-20201010210601-c765b799fd72/go.mod h1:PsJICrlruG9QcJDYuZ0dO/2KtMDALzRbony8NkxZ2nE=
 github.com/andybalholm/brotli v1.1.1 h1:PR2pgnyFznKEugtsUo0xLdDop5SKXd5Qf5ysW+7XdTA=
 github.com/andybalholm/brotli v1.1.1/go.mod h1:05ib4cKhjx3OQYUY22hTVd34Bc8upXjOLL2rKwwZBoA=
 github.com/aws/aws-sdk-go-v2 v1.41.7 h1:DWpAJt66FmnnaRIOT/8ASTucrvuDPZASqhhLey6tLY8=
 github.com/aws/aws-sdk-go-v2 v1.41.7/go.mod h1:4LAfZOPHNVNQEckOACQx60Y8pSRjIkNZQz1w92xpMJc=
 github.com/aws/aws-sdk-go-v2/aws/protocol/eventstream v1.7.10 h1:gx1AwW1Iyk9Z9dD9F4akX5gnN3QZwUB20GGKH/I+Rho=
 github.com/aws/aws-sdk-go-v2/aws/protocol/eventstream v1.7.10/go.mod h1:qqY157uZoqm5OXq/amuaBJyC9hgBCBQnsaWnPe905GY=
 github.com/aws/aws-sdk-go-v2/config v1.32.17 h1:FpL4/758/diKwqbytU0prpuiu60fgXKUWCpDJtApclU=
 github.com/aws/aws-sdk-go-v2/config v1.32.17/go.mod h1:OXqUMzgXytfoF9JaKkhrOYsyh72t9G+MJH8mMRaexOE=
 github.com/aws/aws-sdk-go-v2/credentials v1.19.16 h1:r3RJBuU7X9ibt8RHbMjWE6y60QbKBiII6wSrXnapxSU=
 github.com/aws/aws-sdk-go-v2/credentials v1.19.16/go.mod h1:6cx7zqDENJDbBIIWX6P8s0h6hqHC8Avbjh9Dseo27ug=
 github.com/aws/aws-sdk-go-v2/feature/ec2/imds v1.18.23 h1:UuSfcORqNSz/ey3VPRS8TcVH2Ikf0/sC+Hdj400QI6U=
 github.com/aws/aws-sdk-go-v2/feature/ec2/imds v1.18.23/go.mod h1:+G/OSGiOFnSOkYloKj/9M35s74LgVAdJBSD5lsFfqKg=
 github.com/aws/aws-sdk-go-v2/internal/configsources v1.4.23 h1:GpT/TrnBYuE5gan2cZbTtvP+JlHsutdmlV2YfEyNde0=
 github.com/aws/aws-sdk-go-v2/internal/configsources v1.4.23/go.mod h1:xYWD6BS9ywC5bS3sz9Xh04whO/hzK2plt2Zkyrp4JuA=
 github.com/aws/aws-sdk-go-v2/internal/endpoints/v2 v2.7.23 h1:bpd8vxhlQi2r1hiueOw02f/duEPTMK59Q4QMAoTTtTo=
 github.com/aws/aws-sdk-go-v2/internal/endpoints/v2 v2.7.23/go.mod h1:15DfR2nw+CRHIk0tqNyifu3G1YdAOy68RftkhMDDwYk=
 github.com/aws/aws-sdk-go-v2/internal/v4a v1.4.24 h1:OQqn11BtaYv1WLUowvcA30MpzIu8Ti4pcLPIIyoKZrA=
 github.com/aws/aws-sdk-go-v2/internal/v4a v1.4.24/go.mod h1:X5ZJyfwVrWA96GzPmUCWFQaEARPR7gCrpq2E92PJwAE=
 github.com/aws/aws-sdk-go-v2/service/internal/accept-encoding v1.13.9 h1:FLudkZLt5ci0ozzgkVo8BJGwvqNaZbTWb3UcucAateA=
 github.com/aws/aws-sdk-go-v2/service/internal/accept-encoding v1.13.9/go.mod h1:w7wZ/s9qK7c8g4al+UyoF1Sp/Z45UwMGcqIzLWVQHWk=
 github.com/aws/aws-sdk-go-v2/service/internal/checksum v1.9.15 h1:ieLCO1JxUWuxTZ1cRd0GAaeX7O6cIxnwk7tc1LsQhC4=
 github.com/aws/aws-sdk-go-v2/service/internal/checksum v1.9.15/go.mod h1:e3IzZvQ3kAWNykvE0Tr0RDZCMFInMvhku3qNpcIQXhM=
 github.com/aws/aws-sdk-go-v2/service/internal/presigned-url v1.13.23 h1:pbrxO/kuIwgEsOPLkaHu0O+m4fNgLU8B3vxQ+72jTPw=
 github.com/aws/aws-sdk-go-v2/service/internal/presigned-url v1.13.23/go.mod h1:/CMNUqoj46HpS3MNRDEDIwcgEnrtZlKRaHNaHxIFpNA=
 github.com/aws/aws-sdk-go-v2/service/internal/s3shared v1.19.23 h1:03xatSQO4+AM1lTAbnRg5OK528EUg744nW7F73U8DKw=
 github.com/aws/aws-sdk-go-v2/service/internal/s3shared v1.19.23/go.mod h1:M8l3mwgx5ToK7wot2sBBce/ojzgnPzZXUV445gTSyE8=
 github.com/aws/aws-sdk-go-v2/service/s3 v1.101.0 h1:etqBTKY581iwLL/H/S2sVgk3C9lAsTJFeXWFDsDcWOU=
 github.com/aws/aws-sdk-go-v2/service/s3 v1.101.0/go.mod h1:L2dcoOgS2VSgbPLvpak2NyUPsO1TBN7M45Z4H7DlRc4=
 github.com/aws/aws-sdk-go-v2/service/signin v1.0.11 h1:TdJ+HdzOBhU8+iVAOGUTU63VXopcumCOF1paFulHWZc=
 github.com/aws/aws-sdk-go-v2/service/signin v1.0.11/go.mod h1:R82ZRExE/nheo0N+T8zHPcLRTcH8MGsnR3BiVGX0TwI=
 github.com/aws/aws-sdk-go-v2/service/sso v1.30.17 h1:7byT8HUWrgoRp6sXjxtZwgOKfhss5fW6SkLBtqzgRoE=
 github.com/aws/aws-sdk-go-v2/service/sso v1.30.17/go.mod h1:xNWknVi4Ezm1vg1QsB/5EWpAJURq22uqd38U8qKvOJc=
 github.com/aws/aws-sdk-go-v2/service/ssooidc v1.35.21 h1:+1Kl1zx6bWi4X7cKi3VYh29h8BvsCoHQEQ6ST9X8w7w=
 github.com/aws/aws-sdk-go-v2/service/ssooidc v1.35.21/go.mod h1:4vIRDq+CJB2xFAXZ+YgGUTiEft7oAQlhIs71xcSeuVg=
 github.com/aws/aws-sdk-go-v2/service/sts v1.42.1 h1:F/M5Y9I3nwr2IEpshZgh1GeHpOItExNM9L1euNuh/fk=
 github.com/aws/aws-sdk-go-v2/service/sts v1.42.1/go.mod h1:mTNxImtovCOEEuD65mKW7DCsL+2gjEH+RPEAexAzAio=
 github.com/aws/smithy-go v1.25.1 h1:J8ERsGSU7d+aCmdQur5Txg6bVoYelvQJgtZehD12GkI=
 github.com/aws/smithy-go v1.25.1/go.mod h1:YE2RhdIuDbA5E5bTdciG9KrW3+TiEONeUWCqxX9i1Fc=
 github.com/beevik/etree v1.1.0/go.mod h1:r8Aw8JqVegEf0w2fDnATrX9VpkMcyFeM0FhwO62wh+A=
 github.com/boj/redistore v0.0.0-20180917114910-cd5dcc76aeff h1:RmdPFa+slIr4SCBg4st/l/vZWVe9QJKMXGO60Bxbe04=
 github.com/boj/redistore v0.0.0-20180917114910-cd5dcc76aeff/go.mod h1:+RTT1BOk5P97fT2CiHkbFQwkK3mjsFAP6zCYV2aXtjw=
@@ -226,8 +258,6 @@ github.com/jinzhu/copier v0.4.0 h1:w3ciUoD19shMCRargcpm0cm91ytaBhDvuRpz1ODO/U8=
 github.com/jinzhu/copier v0.4.0/go.mod h1:DfbEm0FYsaqBcKcFuvmOZb218JkPGtvSHsKg8S8hyyg=
 github.com/jinzhu/inflection v1.0.0 h1:K317FqzuhWc8YvSVlFMCCUb36O/S9MCKRDI7QkRKD/E=
 github.com/jinzhu/inflection v1.0.0/go.mod h1:h+uFLlag+Qp1Va5pdKtLDYj+kHp5pxUVkryuEj+Srlc=
 github.com/jinzhu/now v1.1.1/go.mod h1:d3SSVoowX0Lcu0IBviAWJpolVfI5UJVZZ7cO71lE/z8=
 github.com/jinzhu/now v1.1.4/go.mod h1:d3SSVoowX0Lcu0IBviAWJpolVfI5UJVZZ7cO71lE/z8=
 github.com/jinzhu/now v1.1.5 h1:/o9tlHleP7gOFmsnYNz3RGnqzefHA47wQpKrrdTIwXQ=
 github.com/jinzhu/now v1.1.5/go.mod h1:d3SSVoowX0Lcu0IBviAWJpolVfI5UJVZZ7cO71lE/z8=
 github.com/json-iterator/go v1.1.10/go.mod h1:KdQUCv79m/52Kvf8AW2vK1V8akMuk1QjK/uOdHXbAo4=
@@ -258,9 +288,6 @@ github.com/mattn/go-colorable v0.1.13/go.mod h1:7S9/ev0klgBDR4GtXTXX8a3vIGJpMovk
 github.com/mattn/go-isatty v0.0.16/go.mod h1:kYGgaQfpe5nmfYZH+SKPsOc2e4SrIfOl2e/yFXSvRLM=
 github.com/mattn/go-isatty v0.0.20 h1:xfD0iDuEKnDkl03q4limB+vH+GxLEtL/jb4xVJSWWEY=
 github.com/mattn/go-isatty v0.0.20/go.mod h1:W+V8PltTTMOvKvAeJH7IuucS94S2C6jfK/D7dTCTo3Y=
 github.com/mattn/go-sqlite3 v1.14.3/go.mod h1:WVKg1VTActs4Qso6iwGbiFih2UIHo0ENGwNd0Lj+XmI=
 github.com/mattn/go-sqlite3 v1.14.22 h1:2gZY6PC6kBnID23Tichd1K+Z0oS6nE/XwU+Vz/5o4kU=
 github.com/mattn/go-sqlite3 v1.14.22/go.mod h1:Uh1q+B4BYcTPb+yiD3kU8Ct7aC0hY9fxUwlHK0RXw+Y=
 github.com/mitchellh/copystructure v1.2.0 h1:vpKXTN4ewci03Vljg/q9QvCGUDttBOGBIa15WveJJGw=
 github.com/mitchellh/copystructure v1.2.0/go.mod h1:qLl+cE2AmVv+CoeAwDPye/v+N2HKCj9FbZEVFJRxO9s=
 github.com/mitchellh/reflectwalk v1.0.2 h1:G2LzWKi524PWgd3mLHV8Y5k7s6XUvT0Gef6zxSIeXaQ=
@@ -365,8 +392,6 @@ github.com/yuin/goldmark v1.1.27/go.mod h1:3hX8gzYuyVAZsxl0MRgGTJEmQBFcNTphYh9de
 github.com/yuin/goldmark v1.1.30/go.mod h1:3hX8gzYuyVAZsxl0MRgGTJEmQBFcNTphYh9decYSb74=
 github.com/yuin/goldmark v1.3.5/go.mod h1:mwnBkeHKe2W/ZEtQ+71ViKU8L12m81fl3OWwC1Zlc8k=
 github.com/yuin/goldmark v1.4.13/go.mod h1:6yULJ656Px+3vBD8DxQVa3kxgyrAnzto9xy5taEt/CY=
 github.com/yuin/gopher-lua v1.1.1 h1:kYKnWBjvbNP4XLT3+bPEwAXJx262OhaHDWDVOPjL46M=
 github.com/yuin/gopher-lua v1.1.1/go.mod h1:GBR0iDaNXjAgGg9zfCvksxSRnQx76gclCIb7kdAd1Pw=
 go.opentelemetry.io/contrib/instrumentation/net/http/otelhttp v0.54.0 h1:TT4fX+nBOA/+LUkobKGW1ydGcn+G3vRw9+g5HwCphpk=
 go.opentelemetry.io/contrib/instrumentation/net/http/otelhttp v0.54.0/go.mod h1:L7UH0GbB0p47T4Rri3uHjbpCFYrVrwc1I25QhNPiGK8=
 go.opentelemetry.io/otel v1.29.0 h1:PdomN/Al4q/lN6iBJEN3AwPvUiHPMlt93c8bqTG5Llw=
@@ -578,20 +603,9 @@ gopkg.in/yaml.v3 v3.0.1 h1:fxVm/GzAzEWqLHuvctI91KS9hhNmmWOoWu0XTYJS7CA=
 gopkg.in/yaml.v3 v3.0.1/go.mod h1:K4uyk7z7BCEPqu6E+C64Yfv1cQ7kz7rIZviUmN+EgEM=
 gorm.io/driver/mysql v1.5.7 h1:MndhOPYOfEp2rHKgkZIhJ16eVUIRf2HmzgoPmh7FCWo=
 gorm.io/driver/mysql v1.5.7/go.mod h1:sEtPWMiqiN1N1cMXoXmBbd8C6/l+TESwriotuRRpkDM=
 gorm.io/driver/sqlite v1.1.3/go.mod h1:AKDgRWk8lcSQSw+9kxCJnX/yySj8G3rdwYlU57cB45c=
 gorm.io/driver/sqlite v1.4.4 h1:gIufGoR0dQzjkyqDyYSCvsYR6fba1Gw5YKDqKeChxFc=
 gorm.io/driver/sqlite v1.4.4/go.mod h1:0Aq3iPO+v9ZKbcdiz8gLWRw5VOPcBOPUQJFLq5e2ecI=
 gorm.io/driver/sqlite v1.6.0 h1:WHRRrIiulaPiPFmDcod6prc4l2VGVWHz80KspNsxSfQ=
 gorm.io/driver/sqlite v1.6.0/go.mod h1:AO9V1qIQddBESngQUKWL9yoH93HIeA1X6V633rBwyT8=
 gorm.io/gorm v1.20.1/go.mod h1:0HFTzE/SqkGTzK6TlDPPQbAYCluiVvhzoA1+aVyzenw=
 gorm.io/gorm v1.23.0/go.mod h1:l2lP/RyAtc1ynaTjFksBde/O8v9oOGIApu2/xRitmZk=
 gorm.io/gorm v1.25.7/go.mod h1:hbnx/Oo0ChWMn1BIhpy1oYozzpM15i4YPuHDmfYtwg8=
 gorm.io/gorm v1.25.12 h1:I0u8i2hWQItBq1WfE0o2+WuL9+8L21K9e2HHSTE/0f8=
 gorm.io/gorm v1.25.12/go.mod h1:xh7N7RHfYlNc5EmcI/El95gXusucDrQnHXe0+CgWcLQ=
 gorm.io/gorm v1.30.0 h1:qbT5aPv1UH8gI99OsRlvDToLxW5zR7FzS9acZDOZcgs=
 gorm.io/gorm v1.30.0/go.mod h1:8Z33v652h4//uMA76KjeDH8mJXPm1QNCYrMeatR0DOE=
 gorm.io/plugin/soft_delete v1.2.1 h1:qx9D/c4Xu6w5KT8LviX8DgLcB9hkKl6JC9f44Tj7cGU=
 gorm.io/plugin/soft_delete v1.2.1/go.mod h1:Zv7vQctOJTGOsJ/bWgrN1n3od0GBAZgnLjEx+cApLGk=
 honnef.co/go/tools v0.0.0-20190102054323-c2f93a96b099/go.mod h1:rf3lG4BRIbNafJWhAfAdb/ePZxsR/4RtNHQocxwk9r4=
 honnef.co/go/tools v0.0.0-20190523083050-ea95bdfd59fc/go.mod h1:rf3lG4BRIbNafJWhAfAdb/ePZxsR/4RtNHQocxwk9r4=
 k8s.io/apimachinery v0.31.1 h1:mhcUBbj7KUjaVhyXILglcVjuS4nYXiwC+KKFBgIVy7U=
@@ -0,0 +1,272 @@
 apiVersion: 1
 groups:
  - orgId: 1
    name: ppanel-core
    folder: PPanel
    interval: 1m
    rules:
      - uid: ppanel-target-down
        title: PPanel monitoring target down
        condition: C
        for: 2m
        noDataState: Alerting
        execErrState: Error
        annotations:
          summary: "Monitoring target is down"
          description: "{{ $labels.job }} on {{ $labels.instance }} has been down for more than 2 minutes."
        labels:
          severity: critical
          service: ppanel
        data:
          - refId: A
            relativeTimeRange:
              from: 300
              to: 0
            datasourceUid: prometheus
            model:
              datasource:
                type: prometheus
                uid: prometheus
              editorMode: code
              expr: 'up{job=~"grafana|prometheus|node-exporter|cadvisor|nginx-exporter|loki|tempo"}'
              instant: true
              intervalMs: 1000
              maxDataPoints: 43200
              refId: A
          - refId: C
            datasourceUid: __expr__
            model:
              conditions:
                - evaluator:
                    params:
                      - 1
                    type: lt
                  operator:
                    type: and
                  query:
                    params:
                      - A
                  reducer:
                    type: last
                  type: query
              datasource:
                type: __expr__
                uid: __expr__
              expression: A
              intervalMs: 1000
              maxDataPoints: 43200
              refId: C
              type: threshold
      - uid: ppanel-host-disk-high
        title: PPanel host disk usage high
        condition: C
        for: 10m
        noDataState: NoData
        execErrState: Error
        annotations:
          summary: "Host disk usage is high"
          description: "{{ $labels.instance }} {{ $labels.mountpoint }} disk usage is above 85% for 10 minutes."
        labels:
          severity: warning
          service: ppanel
        data:
          - refId: A
            relativeTimeRange:
              from: 900
              to: 0
            datasourceUid: prometheus
            model:
              datasource:
                type: prometheus
                uid: prometheus
              editorMode: code
              expr: '100 - (node_filesystem_avail_bytes{fstype!~"tmpfs|overlay|squashfs|aufs",mountpoint!~"/run.*|/var/lib/docker.*"} / node_filesystem_size_bytes{fstype!~"tmpfs|overlay|squashfs|aufs",mountpoint!~"/run.*|/var/lib/docker.*"} * 100)'
              instant: true
              intervalMs: 1000
              maxDataPoints: 43200
              refId: A
          - refId: C
            datasourceUid: __expr__
            model:
              conditions:
                - evaluator:
                    params:
                      - 85
                    type: gt
                  operator:
                    type: and
                  query:
                    params:
                      - A
                  reducer:
                    type: last
                  type: query
              datasource:
                type: __expr__
                uid: __expr__
              expression: A
              intervalMs: 1000
              maxDataPoints: 43200
              refId: C
              type: threshold
      - uid: ppanel-host-memory-high
        title: PPanel host memory usage high
        condition: C
        for: 10m
        noDataState: NoData
        execErrState: Error
        annotations:
          summary: "Host memory usage is high"
          description: "{{ $labels.instance }} memory usage is above 90% for 10 minutes."
        labels:
          severity: warning
          service: ppanel
        data:
          - refId: A
            relativeTimeRange:
              from: 900
              to: 0
            datasourceUid: prometheus
            model:
              datasource:
                type: prometheus
                uid: prometheus
              editorMode: code
              expr: '(1 - (node_memory_MemAvailable_bytes / node_memory_MemTotal_bytes)) * 100'
              instant: true
              intervalMs: 1000
              maxDataPoints: 43200
              refId: A
          - refId: C
            datasourceUid: __expr__
            model:
              conditions:
                - evaluator:
                    params:
                      - 90
                    type: gt
                  operator:
                    type: and
                  query:
                    params:
                      - A
                  reducer:
                    type: last
                  type: query
              datasource:
                type: __expr__
                uid: __expr__
              expression: A
              intervalMs: 1000
              maxDataPoints: 43200
              refId: C
              type: threshold
      - uid: ppanel-host-cpu-high
        title: PPanel host CPU usage high
        condition: C
        for: 10m
        noDataState: NoData
        execErrState: Error
        annotations:
          summary: "Host CPU usage is high"
          description: "{{ $labels.instance }} CPU usage is above 90% for 10 minutes."
        labels:
          severity: warning
          service: ppanel
        data:
          - refId: A
            relativeTimeRange:
              from: 900
              to: 0
            datasourceUid: prometheus
            model:
              datasource:
                type: prometheus
                uid: prometheus
              editorMode: code
              expr: '100 - (avg by (instance) (rate(node_cpu_seconds_total{mode="idle"}[5m])) * 100)'
              instant: true
              intervalMs: 1000
              maxDataPoints: 43200
              refId: A
          - refId: C
            datasourceUid: __expr__
            model:
              conditions:
                - evaluator:
                    params:
                      - 90
                    type: gt
                  operator:
                    type: and
                  query:
                    params:
                      - A
                  reducer:
                    type: last
                  type: query
              datasource:
                type: __expr__
                uid: __expr__
              expression: A
              intervalMs: 1000
              maxDataPoints: 43200
              refId: C
              type: threshold
      - uid: ppanel-container-restarts
        title: PPanel container restarted
        condition: C
        for: 1m
        noDataState: NoData
        execErrState: Error
        annotations:
          summary: "Container restarted"
          description: "{{ $labels.name }} restarted or changed start time in the last hour."
        labels:
          severity: warning
          service: ppanel
        data:
          - refId: A
            relativeTimeRange:
              from: 3600
              to: 0
            datasourceUid: prometheus
            model:
              datasource:
                type: prometheus
                uid: prometheus
              editorMode: code
              expr: 'sum by (name) (changes(container_start_time_seconds{name!=""}[1h]))'
              instant: true
              intervalMs: 1000
              maxDataPoints: 43200
              refId: A
          - refId: C
            datasourceUid: __expr__
            model:
              conditions:
                - evaluator:
                    params:
                      - 0
                    type: gt
                  operator:
                    type: and
                  query:
                    params:
                      - A
                  reducer:
                    type: last
                  type: query
              datasource:
                type: __expr__
                uid: __expr__
              expression: A
              intervalMs: 1000
              maxDataPoints: 43200
              refId: C
              type: threshold
@@ -0,0 +1,14 @@
 apiVersion: 1
 providers:
  - name: PPanel
    orgId: 1
    folder: PPanel
    folderUid: ppanel
    type: file
    disableDeletion: false
    allowUiUpdates: true
    updateIntervalSeconds: 30
    options:
      path: /etc/grafana/provisioning/dashboards/json
      foldersFromFilesStructure: false
@@ -0,0 +1,520 @@
 {
  "annotations": {
    "list": [
      {
        "builtIn": 1,
        "datasource": {
          "type": "grafana",
          "uid": "-- Grafana --"
        },
        "enable": true,
        "hide": true,
        "iconColor": "rgba(0, 211, 255, 1)",
        "name": "Annotations & Alerts",
        "type": "dashboard"
      }
    ]
  },
  "editable": true,
  "fiscalYearStartMonth": 0,
  "graphTooltip": 0,
  "id": null,
  "links": [],
  "panels": [
    {
      "datasource": {
        "type": "cloudwatch",
        "uid": "cloudwatch"
      },
      "gridPos": {
        "h": 3,
        "w": 24,
        "x": 0,
        "y": 0
      },
      "id": 1,
      "options": {
        "content": "<b>AWS CloudWatch overview</b><br/>Region: ap-east-1 (Hong Kong)<br/>RDS DBInstanceIdentifier: hifast-mysql-prod-v2<br/>Redis: current production uses a local Docker Redis container (<code>hifast-redis</code>) on EC2 rather than AWS ElastiCache.<br/><br/>This dashboard keeps the RDS CloudWatch panels. Redis should be observed from the local ops dashboard via Prometheus/cAdvisor instead of ElastiCache metrics.",
        "mode": "html"
      },
      "pluginVersion": "11.0.0",
      "title": "Read Me",
      "type": "text"
    },
    {
      "datasource": {
        "type": "cloudwatch",
        "uid": "cloudwatch"
      },
      "fieldConfig": {
        "defaults": {
          "unit": "percent"
        },
        "overrides": []
      },
      "gridPos": {
        "h": 8,
        "w": 8,
        "x": 0,
        "y": 3
      },
      "id": 2,
      "options": {
        "legend": {
          "displayMode": "table",
          "placement": "bottom"
        },
        "tooltip": {
          "mode": "single"
        }
      },
      "targets": [
        {
          "datasource": {
            "type": "cloudwatch",
            "uid": "cloudwatch"
          },
          "dimensions": {
            "DBInstanceIdentifier": "hifast-mysql-prod-v2"
          },
          "metricName": "CPUUtilization",
          "namespace": "AWS/RDS",
          "period": "",
          "refId": "A",
          "region": "ap-east-1",
          "statistic": "Average"
        }
      ],
      "title": "RDS CPU",
      "type": "timeseries"
    },
    {
      "datasource": {
        "type": "cloudwatch",
        "uid": "cloudwatch"
      },
      "fieldConfig": {
        "defaults": {
          "unit": "short"
        },
        "overrides": []
      },
      "gridPos": {
        "h": 8,
        "w": 8,
        "x": 8,
        "y": 3
      },
      "id": 3,
      "options": {
        "legend": {
          "displayMode": "table",
          "placement": "bottom"
        },
        "tooltip": {
          "mode": "single"
        }
      },
      "targets": [
        {
          "datasource": {
            "type": "cloudwatch",
            "uid": "cloudwatch"
          },
          "dimensions": {
            "DBInstanceIdentifier": "hifast-mysql-prod-v2"
          },
          "metricName": "DatabaseConnections",
          "namespace": "AWS/RDS",
          "period": "",
          "refId": "A",
          "region": "ap-east-1",
          "statistic": "Average"
        }
      ],
      "title": "RDS Connections",
      "type": "timeseries"
    },
    {
      "datasource": {
        "type": "cloudwatch",
        "uid": "cloudwatch"
      },
      "fieldConfig": {
        "defaults": {
          "unit": "bytes"
        },
        "overrides": []
      },
      "gridPos": {
        "h": 8,
        "w": 8,
        "x": 16,
        "y": 3
      },
      "id": 4,
      "options": {
        "legend": {
          "displayMode": "table",
          "placement": "bottom"
        },
        "tooltip": {
          "mode": "single"
        }
      },
      "targets": [
        {
          "datasource": {
            "type": "cloudwatch",
            "uid": "cloudwatch"
          },
          "dimensions": {
            "DBInstanceIdentifier": "hifast-mysql-prod-v2"
          },
          "metricName": "FreeStorageSpace",
          "namespace": "AWS/RDS",
          "period": "",
          "refId": "A",
          "region": "ap-east-1",
          "statistic": "Minimum"
        }
      ],
      "title": "RDS Free Storage",
      "type": "timeseries"
    },
    {
      "datasource": {
        "type": "cloudwatch",
        "uid": "cloudwatch"
      },
      "fieldConfig": {
        "defaults": {
          "unit": "s"
        },
        "overrides": []
      },
      "gridPos": {
        "h": 8,
        "w": 12,
        "x": 0,
        "y": 11
      },
      "id": 5,
      "options": {
        "legend": {
          "displayMode": "table",
          "placement": "bottom"
        },
        "tooltip": {
          "mode": "multi"
        }
      },
      "targets": [
        {
          "datasource": {
            "type": "cloudwatch",
            "uid": "cloudwatch"
          },
          "dimensions": {
            "DBInstanceIdentifier": "hifast-mysql-prod-v2"
          },
          "metricName": "ReadLatency",
          "namespace": "AWS/RDS",
          "period": "",
          "refId": "A",
          "region": "ap-east-1",
          "statistic": "Average"
        },
        {
          "datasource": {
            "type": "cloudwatch",
            "uid": "cloudwatch"
          },
          "dimensions": {
            "DBInstanceIdentifier": "hifast-mysql-prod-v2"
          },
          "metricName": "WriteLatency",
          "namespace": "AWS/RDS",
          "period": "",
          "refId": "B",
          "region": "ap-east-1",
          "statistic": "Average"
        }
      ],
      "title": "RDS Read / Write Latency",
      "type": "timeseries"
    },
    {
      "datasource": {
        "type": "cloudwatch",
        "uid": "cloudwatch"
      },
      "fieldConfig": {
        "defaults": {
          "unit": "iops"
        },
        "overrides": []
      },
      "gridPos": {
        "h": 8,
        "w": 12,
        "x": 12,
        "y": 11
      },
      "id": 6,
      "options": {
        "legend": {
          "displayMode": "table",
          "placement": "bottom"
        },
        "tooltip": {
          "mode": "multi"
        }
      },
      "targets": [
        {
          "datasource": {
            "type": "cloudwatch",
            "uid": "cloudwatch"
          },
          "dimensions": {
            "DBInstanceIdentifier": "hifast-mysql-prod-v2"
          },
          "metricName": "ReadIOPS",
          "namespace": "AWS/RDS",
          "period": "",
          "refId": "A",
          "region": "ap-east-1",
          "statistic": "Average"
        },
        {
          "datasource": {
            "type": "cloudwatch",
            "uid": "cloudwatch"
          },
          "dimensions": {
            "DBInstanceIdentifier": "hifast-mysql-prod-v2"
          },
          "metricName": "WriteIOPS",
          "namespace": "AWS/RDS",
          "period": "",
          "refId": "B",
          "region": "ap-east-1",
          "statistic": "Average"
        }
      ],
      "title": "RDS Read / Write IOPS",
      "type": "timeseries"
    },
    {
      "datasource": {
        "type": "cloudwatch",
        "uid": "cloudwatch"
      },
      "fieldConfig": {
        "defaults": {
          "unit": "percent"
        },
        "overrides": []
      },
      "gridPos": {
        "h": 8,
        "w": 6,
        "x": 0,
        "y": 19
      },
      "id": 7,
      "options": {
        "legend": {
          "displayMode": "table",
          "placement": "bottom"
        },
        "tooltip": {
          "mode": "single"
        }
      },
      "targets": [
        {
          "datasource": {
            "type": "cloudwatch",
            "uid": "cloudwatch"
          },
          "dimensions": {
            "ReplicationGroupId": "hifastapp-redis"
          },
          "metricName": "CPUUtilization",
          "namespace": "AWS/ElastiCache",
          "period": "",
          "refId": "A",
          "region": "ap-east-1",
          "statistic": "Average"
        }
      ],
      "title": "Redis Host CPU (Legacy ElastiCache)",
      "type": "timeseries"
    },
    {
      "datasource": {
        "type": "cloudwatch",
        "uid": "cloudwatch"
      },
      "fieldConfig": {
        "defaults": {
          "unit": "percent"
        },
        "overrides": []
      },
      "gridPos": {
        "h": 8,
        "w": 6,
        "x": 6,
        "y": 19
      },
      "id": 8,
      "options": {
        "legend": {
          "displayMode": "table",
          "placement": "bottom"
        },
        "tooltip": {
          "mode": "single"
        }
      },
      "targets": [
        {
          "datasource": {
            "type": "cloudwatch",
            "uid": "cloudwatch"
          },
          "dimensions": {
            "ReplicationGroupId": "hifastapp-redis"
          },
          "metricName": "EngineCPUUtilization",
          "namespace": "AWS/ElastiCache",
          "period": "",
          "refId": "A",
          "region": "ap-east-1",
          "statistic": "Average"
        }
      ],
      "title": "Redis Engine CPU (Legacy ElastiCache)",
      "type": "timeseries"
    },
    {
      "datasource": {
        "type": "cloudwatch",
        "uid": "cloudwatch"
      },
      "fieldConfig": {
        "defaults": {
          "unit": "short"
        },
        "overrides": []
      },
      "gridPos": {
        "h": 8,
        "w": 6,
        "x": 12,
        "y": 19
      },
      "id": 9,
      "options": {
        "legend": {
          "displayMode": "table",
          "placement": "bottom"
        },
        "tooltip": {
          "mode": "single"
        }
      },
      "targets": [
        {
          "datasource": {
            "type": "cloudwatch",
            "uid": "cloudwatch"
          },
          "dimensions": {
            "ReplicationGroupId": "hifastapp-redis"
          },
          "metricName": "CurrConnections",
          "namespace": "AWS/ElastiCache",
          "period": "",
          "refId": "A",
          "region": "ap-east-1",
          "statistic": "Average"
        }
      ],
      "title": "Redis Connections (Legacy ElastiCache)",
      "type": "timeseries"
    },
    {
      "datasource": {
        "type": "cloudwatch",
        "uid": "cloudwatch"
      },
      "fieldConfig": {
        "defaults": {
          "unit": "percent"
        },
        "overrides": []
      },
      "gridPos": {
        "h": 8,
        "w": 6,
        "x": 18,
        "y": 19
      },
      "id": 10,
      "options": {
        "legend": {
          "displayMode": "table",
          "placement": "bottom"
        },
        "tooltip": {
          "mode": "single"
        }
      },
      "targets": [
        {
          "datasource": {
            "type": "cloudwatch",
            "uid": "cloudwatch"
          },
          "dimensions": {
            "ReplicationGroupId": "hifastapp-redis"
          },
          "metricName": "DatabaseMemoryUsagePercentage",
          "namespace": "AWS/ElastiCache",
          "period": "",
          "refId": "A",
          "region": "ap-east-1",
          "statistic": "Average"
        }
      ],
      "title": "Redis Memory Usage % (Legacy ElastiCache)",
      "type": "timeseries"
    }
  ],
  "refresh": "30s",
  "schemaVersion": 39,
  "style": "dark",
  "tags": [
    "aws",
    "cloudwatch",
    "rds",
    "redis"
  ],
  "templating": {
    "list": []
  },
  "time": {
    "from": "now-6h",
    "to": "now"
  },
  "timepicker": {},
  "timezone": "browser",
  "title": "AWS RDS & Redis Overview",
  "uid": "aws-rds-redis-overview",
  "version": 1,
  "weekStart": ""
 }
@@ -0,0 +1,565 @@
 {
  "annotations": {
    "list": [
      {
        "builtIn": 1,
        "datasource": {
          "type": "grafana",
          "uid": "-- Grafana --"
        },
        "enable": true,
        "hide": true,
        "iconColor": "rgba(0, 211, 255, 1)",
        "name": "Annotations & Alerts",
        "type": "dashboard"
      }
    ]
  },
  "editable": true,
  "fiscalYearStartMonth": 0,
  "graphTooltip": 0,
  "id": null,
  "links": [],
  "panels": [
    {
      "datasource": {
        "type": "prometheus",
        "uid": "prometheus"
      },
      "fieldConfig": {
        "defaults": {
          "color": {
            "mode": "thresholds"
          },
          "mappings": [
            {
              "options": {
                "0": {
                  "text": "DOWN"
                },
                "1": {
                  "text": "UP"
                }
              },
              "type": "value"
            }
          ],
          "thresholds": {
            "mode": "absolute",
            "steps": [
              {
                "color": "red",
                "value": null
              },
              {
                "color": "green",
                "value": 1
              }
            ]
          }
        },
        "overrides": []
      },
      "gridPos": {
        "h": 4,
        "w": 24,
        "x": 0,
        "y": 0
      },
      "id": 1,
      "options": {
        "colorMode": "background",
        "graphMode": "none",
        "justifyMode": "center",
        "orientation": "horizontal",
        "reduceOptions": {
          "calcs": [
            "lastNotNull"
          ],
          "fields": "",
          "values": false
        },
        "showPercentChange": false,
        "textMode": "auto",
        "wideLayout": true
      },
      "pluginVersion": "13.0.1",
      "targets": [
        {
          "datasource": {
            "type": "prometheus",
            "uid": "prometheus"
          },
          "editorMode": "code",
          "expr": "up{job=~\"prometheus|grafana|node-exporter|cadvisor|nginx-exporter|loki|tempo\"}",
          "instant": true,
          "legendFormat": "{{job}}",
          "range": false,
          "refId": "A"
        }
      ],
      "title": "Service Availability",
      "type": "stat"
    },
    {
      "datasource": {
        "type": "prometheus",
        "uid": "prometheus"
      },
      "fieldConfig": {
        "defaults": {
          "max": 100,
          "min": 0,
          "thresholds": {
            "mode": "absolute",
            "steps": [
              {
                "color": "green",
                "value": null
              },
              {
                "color": "orange",
                "value": 75
              },
              {
                "color": "red",
                "value": 90
              }
            ]
          },
          "unit": "percent"
        },
        "overrides": []
      },
      "gridPos": {
        "h": 8,
        "w": 8,
        "x": 0,
        "y": 4
      },
      "id": 2,
      "options": {
        "legend": {
          "displayMode": "list",
          "placement": "bottom"
        },
        "tooltip": {
          "mode": "single"
        }
      },
      "targets": [
        {
          "datasource": {
            "type": "prometheus",
            "uid": "prometheus"
          },
          "editorMode": "code",
          "expr": "100 - (avg by (instance) (rate(node_cpu_seconds_total{mode=\"idle\"}[5m])) * 100)",
          "legendFormat": "{{instance}} CPU",
          "range": true,
          "refId": "A"
        }
      ],
      "title": "Host CPU Usage",
      "type": "timeseries"
    },
    {
      "datasource": {
        "type": "prometheus",
        "uid": "prometheus"
      },
      "fieldConfig": {
        "defaults": {
          "max": 100,
          "min": 0,
          "thresholds": {
            "mode": "absolute",
            "steps": [
              {
                "color": "green",
                "value": null
              },
              {
                "color": "orange",
                "value": 80
              },
              {
                "color": "red",
                "value": 90
              }
            ]
          },
          "unit": "percent"
        },
        "overrides": []
      },
      "gridPos": {
        "h": 8,
        "w": 8,
        "x": 8,
        "y": 4
      },
      "id": 3,
      "options": {
        "legend": {
          "displayMode": "list",
          "placement": "bottom"
        },
        "tooltip": {
          "mode": "single"
        }
      },
      "targets": [
        {
          "datasource": {
            "type": "prometheus",
            "uid": "prometheus"
          },
          "editorMode": "code",
          "expr": "(1 - (node_memory_MemAvailable_bytes / node_memory_MemTotal_bytes)) * 100",
          "legendFormat": "{{instance}} memory",
          "range": true,
          "refId": "A"
        }
      ],
      "title": "Host Memory Usage",
      "type": "timeseries"
    },
    {
      "datasource": {
        "type": "prometheus",
        "uid": "prometheus"
      },
      "fieldConfig": {
        "defaults": {
          "max": 100,
          "min": 0,
          "thresholds": {
            "mode": "absolute",
            "steps": [
              {
                "color": "green",
                "value": null
              },
              {
                "color": "orange",
                "value": 80
              },
              {
                "color": "red",
                "value": 90
              }
            ]
          },
          "unit": "percent"
        },
        "overrides": []
      },
      "gridPos": {
        "h": 8,
        "w": 8,
        "x": 16,
        "y": 4
      },
      "id": 4,
      "options": {
        "legend": {
          "displayMode": "list",
          "placement": "bottom"
        },
        "tooltip": {
          "mode": "single"
        }
      },
      "targets": [
        {
          "datasource": {
            "type": "prometheus",
            "uid": "prometheus"
          },
          "editorMode": "code",
          "expr": "100 - (node_filesystem_avail_bytes{fstype!~\"tmpfs|overlay|squashfs|aufs\",mountpoint!~\"/run.*|/var/lib/docker.*\"} / node_filesystem_size_bytes{fstype!~\"tmpfs|overlay|squashfs|aufs\",mountpoint!~\"/run.*|/var/lib/docker.*\"} * 100)",
          "legendFormat": "{{mountpoint}}",
          "range": true,
          "refId": "A"
        }
      ],
      "title": "Host Disk Usage",
      "type": "timeseries"
    },
    {
      "datasource": {
        "type": "prometheus",
        "uid": "prometheus"
      },
      "fieldConfig": {
        "defaults": {
          "unit": "percentunit"
        },
        "overrides": []
      },
      "gridPos": {
        "h": 8,
        "w": 8,
        "x": 0,
        "y": 12
      },
      "id": 5,
      "options": {
        "legend": {
          "displayMode": "table",
          "placement": "bottom"
        },
        "tooltip": {
          "mode": "multi"
        }
      },
      "targets": [
        {
          "datasource": {
            "type": "prometheus",
            "uid": "prometheus"
          },
          "editorMode": "code",
          "expr": "sum by (name) (rate(container_cpu_usage_seconds_total{name!=\"\"}[5m]))",
          "legendFormat": "{{name}}",
          "range": true,
          "refId": "A"
        }
      ],
      "title": "Container CPU",
      "type": "timeseries"
    },
    {
      "datasource": {
        "type": "prometheus",
        "uid": "prometheus"
      },
      "fieldConfig": {
        "defaults": {
          "unit": "bytes"
        },
        "overrides": []
      },
      "gridPos": {
        "h": 8,
        "w": 8,
        "x": 8,
        "y": 12
      },
      "id": 6,
      "options": {
        "legend": {
          "displayMode": "table",
          "placement": "bottom"
        },
        "tooltip": {
          "mode": "multi"
        }
      },
      "targets": [
        {
          "datasource": {
            "type": "prometheus",
            "uid": "prometheus"
          },
          "editorMode": "code",
          "expr": "sum by (name) (container_memory_working_set_bytes{name!=\"\"})",
          "legendFormat": "{{name}}",
          "range": true,
          "refId": "A"
        }
      ],
      "title": "Container Memory",
      "type": "timeseries"
    },
    {
      "datasource": {
        "type": "prometheus",
        "uid": "prometheus"
      },
      "fieldConfig": {
        "defaults": {
          "unit": "short"
        },
        "overrides": []
      },
      "gridPos": {
        "h": 8,
        "w": 8,
        "x": 16,
        "y": 12
      },
      "id": 7,
      "options": {
        "legend": {
          "displayMode": "table",
          "placement": "bottom"
        },
        "tooltip": {
          "mode": "multi"
        }
      },
      "targets": [
        {
          "datasource": {
            "type": "prometheus",
            "uid": "prometheus"
          },
          "editorMode": "code",
          "expr": "sum by (name) (changes(container_start_time_seconds{name!=\"\"}[1h]))",
          "legendFormat": "{{name}}",
          "range": true,
          "refId": "A"
        }
      ],
      "title": "Container Restarts / Changes",
      "type": "timeseries"
    },
    {
      "datasource": {
        "type": "prometheus",
        "uid": "prometheus"
      },
      "fieldConfig": {
        "defaults": {
          "unit": "reqps"
        },
        "overrides": []
      },
      "gridPos": {
        "h": 8,
        "w": 8,
        "x": 0,
        "y": 20
      },
      "id": 8,
      "options": {
        "legend": {
          "displayMode": "list",
          "placement": "bottom"
        },
        "tooltip": {
          "mode": "single"
        }
      },
      "targets": [
        {
          "datasource": {
            "type": "prometheus",
            "uid": "prometheus"
          },
          "editorMode": "code",
          "expr": "rate(nginx_http_requests_total[5m])",
          "legendFormat": "requests",
          "range": true,
          "refId": "A"
        }
      ],
      "title": "Nginx Requests",
      "type": "timeseries"
    },
    {
      "datasource": {
        "type": "loki",
        "uid": "loki"
      },
      "gridPos": {
        "h": 8,
        "w": 8,
        "x": 8,
        "y": 20
      },
      "id": 9,
      "options": {
        "dedupStrategy": "none",
        "enableLogDetails": true,
        "prettifyLogMessage": false,
        "showCommonLabels": false,
        "showLabels": true,
        "showTime": true,
        "sortOrder": "Descending",
        "wrapLogMessage": true
      },
      "targets": [
        {
          "datasource": {
            "type": "loki",
            "uid": "loki"
          },
          "editorMode": "code",
          "expr": "{job=~\"ppanel-server|nginx|docker\"} |~ \"(?i)(error|panic|fatal|timeout|exception|failed)\"",
          "queryType": "range",
          "refId": "A"
        }
      ],
      "title": "Recent Errors",
      "type": "logs"
    },
    {
      "datasource": {
        "type": "prometheus",
        "uid": "prometheus"
      },
      "fieldConfig": {
        "defaults": {
          "unit": "reqps"
        },
        "overrides": []
      },
      "gridPos": {
        "h": 8,
        "w": 8,
        "x": 16,
        "y": 20
      },
      "id": 10,
      "options": {
        "legend": {
          "displayMode": "table",
          "placement": "bottom"
        },
        "tooltip": {
          "mode": "multi"
        }
      },
      "targets": [
        {
          "datasource": {
            "type": "prometheus",
            "uid": "prometheus"
          },
          "editorMode": "code",
          "expr": "sum by (service_name) (rate(traces_spanmetrics_calls_total[5m]))",
          "legendFormat": "{{service_name}}",
          "range": true,
          "refId": "A"
        }
      ],
      "title": "Trace Span Calls",
      "type": "timeseries"
    }
  ],
  "refresh": "30s",
  "schemaVersion": 42,
  "tags": [
    "ppanel",
    "ops",
    "prometheus",
    "loki",
    "tempo"
  ],
  "templating": {
    "list": []
  },
  "time": {
    "from": "now-6h",
    "to": "now"
  },
  "timepicker": {},
  "timezone": "browser",
  "title": "PPanel Ops Overview",
  "uid": "ppanel-ops-overview",
  "version": 1,
  "weekStart": ""
 }
@@ -0,0 +1,330 @@
 {
  "annotations": {
    "list": [
      {
        "builtIn": 1,
        "datasource": {
          "type": "grafana",
          "uid": "-- Grafana --"
        },
        "enable": true,
        "hide": true,
        "iconColor": "rgba(0, 211, 255, 1)",
        "name": "Annotations & Alerts",
        "type": "dashboard"
      }
    ]
  },
  "editable": true,
  "fiscalYearStartMonth": 0,
  "graphTooltip": 0,
  "id": null,
  "links": [],
  "panels": [
    {
      "datasource": {
        "type": "loki",
        "uid": "P8E80F9AEF21F6940"
      },
      "fieldConfig": {
        "defaults": {
          "color": {
            "mode": "palette-classic"
          },
          "unit": "short"
        },
        "overrides": []
      },
      "gridPos": {
        "h": 7,
        "w": 12,
        "x": 0,
        "y": 0
      },
      "id": 1,
      "options": {
        "legend": {
          "displayMode": "table",
          "placement": "bottom"
        },
        "tooltip": {
          "mode": "multi"
        }
      },
      "targets": [
        {
          "datasource": {
            "type": "loki",
            "uid": "P8E80F9AEF21F6940"
          },
          "editorMode": "code",
          "expr": "sum(count_over_time({compose_service=\"ppanel-server\"}[5m]))",
          "queryType": "range",
          "refId": "A"
        }
      ],
      "title": "Matched Log Volume",
      "type": "timeseries"
    },
    {
      "datasource": {
        "type": "loki",
        "uid": "P8E80F9AEF21F6940"
      },
      "fieldConfig": {
        "defaults": {
          "color": {
            "mode": "palette-classic"
          },
          "unit": "short"
        },
        "overrides": []
      },
      "gridPos": {
        "h": 7,
        "w": 12,
        "x": 12,
        "y": 0
      },
      "id": 2,
      "options": {
        "legend": {
          "displayMode": "table",
          "placement": "bottom"
        },
        "tooltip": {
          "mode": "multi"
        }
      },
      "targets": [
        {
          "datasource": {
            "type": "loki",
            "uid": "P8E80F9AEF21F6940"
          },
          "editorMode": "code",
          "expr": "sum(count_over_time({compose_service=\"ppanel-server\"} |~ \"(?i)(error|panic|fatal)\" [5m]))",
          "queryType": "range",
          "refId": "A"
        }
      ],
      "title": "Matched Error Volume",
      "type": "timeseries"
    },
    {
      "datasource": {
        "type": "loki",
        "uid": "P8E80F9AEF21F6940"
      },
      "gridPos": {
        "h": 12,
        "w": 24,
        "x": 0,
        "y": 7
      },
      "id": 3,
      "options": {
        "dedupStrategy": "none",
        "enableLogDetails": true,
        "prettifyLogMessage": false,
        "showCommonLabels": false,
        "showLabels": true,
        "showTime": true,
        "sortOrder": "Descending",
        "wrapLogMessage": true
      },
      "targets": [
        {
          "datasource": {
            "type": "loki",
            "uid": "P8E80F9AEF21F6940"
          },
          "editorMode": "code",
          "expr": "{compose_service=\"ppanel-server\"}",
          "queryType": "range",
          "refId": "A"
        }
      ],
      "title": "Filtered Server Logs",
      "type": "logs"
    },
    {
      "datasource": {
        "type": "loki",
        "uid": "P8E80F9AEF21F6940"
      },
      "gridPos": {
        "h": 10,
        "w": 24,
        "x": 0,
        "y": 19
      },
      "id": 4,
      "options": {
        "dedupStrategy": "none",
        "enableLogDetails": true,
        "prettifyLogMessage": false,
        "showCommonLabels": false,
        "showLabels": true,
        "showTime": true,
        "sortOrder": "Descending",
        "wrapLogMessage": true
      },
      "targets": [
        {
          "datasource": {
            "type": "loki",
            "uid": "P8E80F9AEF21F6940"
          },
          "editorMode": "code",
          "expr": "{compose_service=\"ppanel-server\"} |~ \"(?i)(error|panic|fatal)\"",
          "queryType": "range",
          "refId": "A"
        }
      ],
      "title": "Filtered Server Errors",
      "type": "logs"
    }
  ],
  "refresh": "30s",
  "schemaVersion": 42,
  "tags": [
    "ppanel",
    "logs",
    "server",
    "loki"
  ],
  "templating": {
    "list": [
      {
        "current": {
          "selected": false,
          "text": ".",
          "value": "."
        },
        "description": "输入用户 ID；默认 . 表示不过滤",
        "hide": 0,
        "label": "用户ID",
        "name": "user_id",
        "options": [],
        "query": ".",
        "skipUrlSync": false,
        "type": "textbox"
      },
      {
        "current": {
          "selected": false,
          "text": ".",
          "value": "."
        },
        "description": "输入邮箱或邮箱片段；默认 . 表示不过滤",
        "hide": 0,
        "label": "邮箱",
        "name": "email",
        "options": [],
        "query": ".",
        "skipUrlSync": false,
        "type": "textbox"
      },
      {
        "current": {
          "selected": false,
          "text": ".",
          "value": "."
        },
        "description": "输入订单号/支付单号/交易号片段；默认 . 表示不过滤",
        "hide": 0,
        "label": "订单",
        "name": "order",
        "options": [],
        "query": ".",
        "skipUrlSync": false,
        "type": "textbox"
      },
      {
        "current": {
          "selected": true,
          "text": "All",
          "value": "."
        },
        "description": "日志等级",
        "hide": 0,
        "includeAll": false,
        "label": "等级",
        "multi": false,
        "name": "level",
        "options": [
          {
            "selected": true,
            "text": "All",
            "value": "."
          },
          {
            "selected": false,
            "text": "debug",
            "value": "debug"
          },
          {
            "selected": false,
            "text": "info",
            "value": "info"
          },
          {
            "selected": false,
            "text": "warn",
            "value": "warn"
          },
          {
            "selected": false,
            "text": "error",
            "value": "error"
          },
          {
            "selected": false,
            "text": "slow",
            "value": "slow"
          },
          {
            "selected": false,
            "text": "panic",
            "value": "panic"
          },
          {
            "selected": false,
            "text": "fatal",
            "value": "fatal"
          }
        ],
        "query": "All : .,debug,info,warn,error,slow,panic,fatal",
        "queryValue": "",
        "skipUrlSync": false,
        "type": "custom"
      },
      {
        "current": {
          "selected": false,
          "text": ".",
          "value": "."
        },
        "description": "任意关键字；默认 . 表示不过滤",
        "hide": 0,
        "label": "关键字",
        "name": "keyword",
        "options": [],
        "query": ".",
        "skipUrlSync": false,
        "type": "textbox"
      }
    ]
  },
  "time": {
    "from": "now-1h",
    "to": "now"
  },
  "timepicker": {},
  "timezone": "browser",
  "title": "PPanel Server Logs",
  "uid": "ppanel-server-logs",
  "version": 5,
  "weekStart": ""
 }
@@ -0,0 +1,57 @@
 apiVersion: 1
 datasources:
  - name: Prometheus
    uid: prometheus
    type: prometheus
    access: proxy
    url: http://prometheus:9090
    isDefault: true
    editable: true
    jsonData:
      httpMethod: POST
      manageAlerts: true
      prometheusType: Prometheus
      prometheusVersion: 2.50.0
      timeInterval: 15s
  - name: Loki
    uid: loki
    type: loki
    access: proxy
    url: http://loki:3100
    editable: true
    jsonData:
      derivedFields:
        - datasourceUid: tempo
          matcherRegex: '"(?:trace|traceID|trace_id)"\s*:\s*"([a-f0-9]{32})"'
          name: TraceID
          url: '$${__value.raw}'
  - name: Tempo
    uid: tempo
    type: tempo
    access: proxy
    url: http://tempo:3200
    editable: true
    jsonData:
      tracesToLogsV2:
        datasourceUid: loki
        filterByTraceID: true
        filterBySpanID: false
        tags:
          - key: service.name
            value: service_name
      tracesToMetrics:
        datasourceUid: prometheus
      serviceMap:
        datasourceUid: prometheus
  - name: CloudWatch
    uid: cloudwatch
    type: cloudwatch
    access: proxy
    editable: true
    jsonData:
      authType: default
      defaultRegion: ap-east-1
@@ -449,7 +449,7 @@ CREATE TABLE IF NOT EXISTS `user_device`
    `subscribe_id` bigint                                                        DEFAULT NULL COMMENT 'Subscribe ID',
    `ip`           varchar(191) CHARACTER SET utf8mb4 COLLATE utf8mb4_general_ci DEFAULT NULL COMMENT 'Device Ip.',
    `Identifier`   varchar(191) CHARACTER SET utf8mb4 COLLATE utf8mb4_general_ci DEFAULT NULL COMMENT 'Device Identifier.',
-    `user_agent`   varchar(64) CHARACTER SET utf8mb4 COLLATE utf8mb4_general_ci  DEFAULT NULL COMMENT 'Device User Agent.',
+    `user_agent`   varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_general_ci DEFAULT NULL COMMENT 'Device User Agent.',
    `online`       tinyint(1) NOT NULL                                           DEFAULT '0' COMMENT 'Online',
    `enabled`      tinyint(1) NOT NULL                                           DEFAULT '1' COMMENT 'EnableDeviceNumber',
    `created_at`   datetime(3)                                                   DEFAULT NULL COMMENT 'Creation Time',
@@ -0,0 +1,13 @@
 -- Remove app_account_token column from order table if it exists
 SET @col_exists = (SELECT COUNT(*) FROM INFORMATION_SCHEMA.COLUMNS WHERE TABLE_SCHEMA = DATABASE() AND TABLE_NAME = 'order' AND COLUMN_NAME = 'app_account_token');
 SET @sql = IF(@col_exists = 1, 'ALTER TABLE `order` DROP COLUMN `app_account_token`', 'SELECT 1');
 PREPARE stmt FROM @sql;
 EXECUTE stmt;
 DEALLOCATE PREPARE stmt;
 -- Remove subscription_user_id column from order table if it exists
 SET @col_exists2 = (SELECT COUNT(*) FROM INFORMATION_SCHEMA.COLUMNS WHERE TABLE_SCHEMA = DATABASE() AND TABLE_NAME = 'order' AND COLUMN_NAME = 'subscription_user_id');
 SET @sql2 = IF(@col_exists2 = 1, 'ALTER TABLE `order` DROP COLUMN `subscription_user_id`', 'SELECT 1');
 PREPARE stmt2 FROM @sql2;
 EXECUTE stmt2;
 DEALLOCATE PREPARE stmt2;
@@ -0,0 +1 @@
 ALTER TABLE `user_device` DROP COLUMN `base_payload`;
@@ -0,0 +1 @@
 ALTER TABLE `user_device` ADD COLUMN `base_payload` TEXT DEFAULT NULL COMMENT 'Base Payload' AFTER `short_code`;
@@ -0,0 +1 @@
 DROP TABLE IF EXISTS `order_recovery_claims`;
@@ -0,0 +1,15 @@
 CREATE TABLE IF NOT EXISTS `order_recovery_claims` (
  `id` BIGINT NOT NULL AUTO_INCREMENT,
  `order_no` VARCHAR(255) NOT NULL COMMENT 'Recovered Order No',
  `email` VARCHAR(255) NOT NULL COMMENT 'Claim Email',
  `user_id` BIGINT NOT NULL DEFAULT 0 COMMENT 'User ID',
  `subscribe_id` BIGINT NOT NULL DEFAULT 0 COMMENT 'Subscribe ID',
  `order_id` BIGINT NOT NULL DEFAULT 0 COMMENT 'Recovered Order ID',
  `user_subscribe_id` BIGINT NOT NULL DEFAULT 0 COMMENT 'User Subscribe ID',
  `claimed_at` DATETIME(3) NOT NULL COMMENT 'Claimed Time',
  `created_at` DATETIME(3) DEFAULT NULL COMMENT 'Creation Time',
  `updated_at` DATETIME(3) DEFAULT NULL COMMENT 'Update Time',
  PRIMARY KEY (`id`),
  UNIQUE KEY `idx_order_recovery_claim_order_no` (`order_no`),
  KEY `idx_order_recovery_claim_email` (`email`)
 ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_unicode_ci;
@@ -0,0 +1,7 @@
 ALTER TABLE `log_message`
  MODIFY COLUMN `app_version` VARCHAR(32) NULL,
  MODIFY COLUMN `os_name` VARCHAR(32) NULL,
  MODIFY COLUMN `os_version` VARCHAR(32) NULL,
  MODIFY COLUMN `device_id` VARCHAR(64) NULL,
  MODIFY COLUMN `session_id` VARCHAR(64) NULL,
  MODIFY COLUMN `error_code` VARCHAR(64) NULL;
@@ -0,0 +1,7 @@
 ALTER TABLE `log_message`
  MODIFY COLUMN `app_version` VARCHAR(64) NULL,
  MODIFY COLUMN `os_name` VARCHAR(64) NULL,
  MODIFY COLUMN `os_version` VARCHAR(64) NULL,
  MODIFY COLUMN `device_id` VARCHAR(255) NULL,
  MODIFY COLUMN `session_id` VARCHAR(255) NULL,
  MODIFY COLUMN `error_code` VARCHAR(128) NULL;
@@ -0,0 +1,2 @@
 ALTER TABLE `user_device`
  MODIFY COLUMN `user_agent` VARCHAR(64) NULL COMMENT 'Device User Agent.';
@@ -0,0 +1,2 @@
 ALTER TABLE `user_device`
  MODIFY COLUMN `user_agent` VARCHAR(255) NULL COMMENT 'Device User Agent.';
@@ -0,0 +1,19 @@
 -- Rollback: re-deduct commission for users with pending (status=0) withdrawals.
 -- This re-applies the OLD behaviour where commission is deducted on application.
 -- Only run this if you are rolling back to the old code; do NOT run against
 -- the new code or commission will be double-deducted on approval.
 UPDATE `user` u
 JOIN (
    SELECT user_id, COALESCE(SUM(amount), 0) AS pending_total
    FROM withdrawals
    WHERE status = 0
    GROUP BY user_id
 ) p ON u.id = p.user_id
 SET u.commission = u.commission - p.pending_total
 WHERE p.pending_total > 0;
 -- Remove the migration log entries written by the up migration.
 DELETE FROM system_logs
 WHERE type = 33
  AND content LIKE '%migration: refund pending withdrawal commission (HIF-22)%';
@@ -0,0 +1,65 @@
 -- Migration: refund commission for existing pending (status=0) withdrawals
 --
 -- Under the old logic, commission was deducted when a withdrawal was submitted.
 -- Under the new logic, commission is only deducted on approval.
 -- This migration refunds the deducted amounts back to each user so that
 -- the system is in a consistent state before the new code is deployed.
 --
 -- Idempotency: the UPDATE only touches rows whose commission would need
 -- to increase, and each execution produces the same result because
 -- COALESCE(SUM(amount),0) is deterministic given the same pending set.
 -- Running this script multiple times is safe only if no new pending
 -- withdrawals are created between runs; deploy new code immediately after.
 -- Compatibility: some historical databases missed migration 02122, so the
 -- withdrawals table may not exist yet. Create it idempotently before the
 -- refund logic so this migration can self-heal older installations.
 CREATE TABLE IF NOT EXISTS `withdrawals` (
    `id` BIGINT NOT NULL AUTO_INCREMENT COMMENT 'Primary Key',
    `user_id` BIGINT NOT NULL COMMENT 'User ID',
    `amount` BIGINT NOT NULL COMMENT 'Withdrawal Amount',
    `content` TEXT COMMENT 'Withdrawal Content',
    `status` TINYINT(1) NOT NULL DEFAULT 0 COMMENT 'Withdrawal Status',
    `reason` VARCHAR(500) NOT NULL DEFAULT '' COMMENT 'Rejection Reason',
    `created_at` DATETIME NOT NULL COMMENT 'Creation Time',
    `updated_at` DATETIME NOT NULL COMMENT 'Update Time',
    PRIMARY KEY (`id`),
    KEY `idx_user_id` (`user_id`)
    ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4;
 INSERT IGNORE INTO `system` (`category`, `key`, `value`, `type`, `desc`, `created_at`, `updated_at`)
 VALUES
    ('invite', 'WithdrawalMethod', '', 'string', 'withdrawal method', '2025-04-22 14:25:16.637', '2025-04-22 14:25:16.637');
 -- Step 1: refund commission for all users with pending withdrawals.
 UPDATE `user` u
 JOIN (
    SELECT user_id, COALESCE(SUM(amount), 0) AS pending_total
    FROM withdrawals
    WHERE status = 0
    GROUP BY user_id
 ) p ON u.id = p.user_id
 SET u.commission = u.commission + p.pending_total
 WHERE p.pending_total > 0;
 -- Step 2: write a migration log entry for each refunded user.
 INSERT INTO system_logs (type, date, object_id, content, created_at)
 SELECT
    33 AS type,
    DATE(NOW()) AS date,
    p.user_id AS object_id,
    JSON_OBJECT(
        'type', 99,
        'amount', p.pending_total,
        'order_no', '',
        'timestamp', UNIX_TIMESTAMP(NOW()) * 1000,
        'note', 'migration: refund pending withdrawal commission (HIF-22)'
    ) AS content,
    NOW() AS created_at
 FROM (
    SELECT user_id, COALESCE(SUM(amount), 0) AS pending_total
    FROM withdrawals
    WHERE status = 0
    GROUP BY user_id
    HAVING pending_total > 0
 ) p;
@@ -0,0 +1,2 @@
 -- Remove activation_context column from order table
 ALTER TABLE `order` DROP COLUMN IF EXISTS `activation_context`;
@@ -0,0 +1,6 @@
 -- Add activation_context column to order table for Redis fallback persistence (idempotent)
 SET @col_exists = (SELECT COUNT(*) FROM INFORMATION_SCHEMA.COLUMNS WHERE TABLE_SCHEMA = DATABASE() AND TABLE_NAME = 'order' AND COLUMN_NAME = 'activation_context');
 SET @sql = IF(@col_exists = 0, 'ALTER TABLE `order` ADD COLUMN `activation_context` TEXT DEFAULT NULL COMMENT ''Activation context JSON (guest/redemption info for DB fallback)'' AFTER `app_account_token`', 'SELECT 1');
 PREPARE stmt FROM @sql;
 EXECUTE stmt;
 DEALLOCATE PREPARE stmt;
@@ -0,0 +1,4 @@
 ALTER TABLE `withdrawals`
  DROP COLUMN `qr_code_url`,
  DROP COLUMN `account`,
  DROP COLUMN `method`;
@@ -0,0 +1,4 @@
 ALTER TABLE `withdrawals`
  ADD COLUMN `method`      TINYINT(1)   NOT NULL DEFAULT 0 COMMENT '收款方式 0:其他 1:支付宝 2:微信 3:银行卡' AFTER `content`,
  ADD COLUMN `account`     VARCHAR(255) NOT NULL DEFAULT '' COMMENT '收款账号' AFTER `method`,
  ADD COLUMN `qr_code_url` VARCHAR(500) NOT NULL DEFAULT '' COMMENT '收款码图片URL' AFTER `account`;
@@ -20,6 +20,14 @@ type schemaColumnPatch struct {
 	ddl    string
 }
 type schemaColumnDefinitionPatch struct {
 	table           string
 	column          string
 	dataType        string
 	characterMaxLen *int64
 	ddl             string
 }
 func EnsureSchemaCompatibility(ctx *svc.ServiceContext) error {
 	tablePatches := []schemaTablePatch{
 		{
@@ -142,6 +150,17 @@ func EnsureSchemaCompatibility(ctx *svc.ServiceContext) error {
 		},
 	}
 	varchar255 := int64(255)
 	columnDefinitionPatches := []schemaColumnDefinitionPatch{
 		{
 			table:           "user_device",
 			column:          "user_agent",
 			dataType:        "varchar",
 			characterMaxLen: &varchar255,
 			ddl:             "ALTER TABLE `user_device` MODIFY COLUMN `user_agent` VARCHAR(255) NULL COMMENT 'Device User Agent.';",
 		},
 	}
 	for _, patch := range tablePatches {
 		exists, err := tableExists(ctx.DB, patch.table)
 		if err != nil {
@@ -199,6 +218,27 @@ func EnsureSchemaCompatibility(ctx *svc.ServiceContext) error {
 		logger.Infof("[SchemaCompat] created missing index: %s.%s", patch.table, patch.index)
 	}
 	for _, patch := range columnDefinitionPatches {
 		tblExists, err := tableExists(ctx.DB, patch.table)
 		if err != nil {
 			return errors.Wrapf(err, "check table %s failed", patch.table)
 		}
 		if !tblExists {
 			continue
 		}
 		matches, err := columnDefinitionMatches(ctx.DB, patch.table, patch.column, patch.dataType, patch.characterMaxLen)
 		if err != nil {
 			return errors.Wrapf(err, "check column definition %s.%s failed", patch.table, patch.column)
 		}
 		if matches {
 			continue
 		}
 		if err = ctx.DB.Exec(patch.ddl).Error; err != nil {
 			return errors.Wrapf(err, "modify column %s.%s failed", patch.table, patch.column)
 		}
 		logger.Infof("[SchemaCompat] repaired column definition: %s.%s", patch.table, patch.column)
 	}
 	return nil
 }
@@ -237,6 +277,38 @@ func indexExists(db *gorm.DB, table, index string) (bool, error) {
 	return count > 0, nil
 }
 func columnDefinitionMatches(db *gorm.DB, table, column, dataType string, characterMaxLen *int64) (bool, error) {
 	type columnMeta struct {
 		DataType            string
 		CharacterMaximumLen *int64
 	}
 	var meta columnMeta
 	err := db.Raw(
 		`SELECT DATA_TYPE AS data_type, CHARACTER_MAXIMUM_LENGTH AS character_maximum_len
 FROM information_schema.COLUMNS
 WHERE TABLE_SCHEMA = DATABASE() AND TABLE_NAME = ? AND COLUMN_NAME = ?`,
 		table,
 		column,
 	).Scan(&meta).Error
 	if err != nil {
 		return false, err
 	}
 	if meta.DataType == "" {
 		return false, nil
 	}
 	if meta.DataType != dataType {
 		return false, nil
 	}
 	if characterMaxLen == nil {
 		return true, nil
 	}
 	if meta.CharacterMaximumLen == nil {
 		return false, nil
 	}
 	return *meta.CharacterMaximumLen == *characterMaxLen, nil
 }
 func _schemaCompatDebug(table, column string) string {
 	if column == "" {
 		return table
@@ -38,12 +38,29 @@ type Config struct {
 	Log           Log                     `yaml:"Log"`
 	Currency      Currency                `yaml:"Currency"`
 	Trace         trace.Config            `yaml:"Trace"`
 	S3            S3Config                `yaml:"S3"`
 	Administrator struct {
 		Email    string `yaml:"Email" default:"admin@ppanel.dev"`
 		Password string `yaml:"Password" default:"password"`
 	} `yaml:"Administrator"`
 }
 type S3Config struct {
 	Enable               bool   `yaml:"Enable" default:"false"`
 	Region               string `yaml:"Region" default:""`
 	Bucket               string `yaml:"Bucket" default:""`
 	Endpoint             string `yaml:"Endpoint" default:""`
 	AccessKey            string `yaml:"AccessKey" default:""`
 	SecretKey            string `yaml:"SecretKey" default:""`
 	SessionToken         string `yaml:"SessionToken" default:""`
 	Prefix               string `yaml:"Prefix" default:"app-upload"`
 	PublicBaseURL        string `yaml:"PublicBaseURL" default:""`
 	UsePathStyle         bool   `yaml:"UsePathStyle" default:"false"`
 	PresignExpireSeconds int64  `yaml:"PresignExpireSeconds" default:"300"`
 	MaxUploadSize        int64  `yaml:"MaxUploadSize" default:"104857600"`
 	AllowedContentTypes  string `yaml:"AllowedContentTypes" default:"application/zip,application/x-zip-compressed,application/gzip,application/x-gzip,application/octet-stream,text/plain,application/json"`
 }
 type RedisConfig struct {
 	Host         string `yaml:"Host" default:"localhost:6379"`
 	Pass         string `yaml:"Pass" default:""`
@@ -0,0 +1,25 @@
 package log
 import (
 	"github.com/gin-gonic/gin"
 	"github.com/perfect-panel/server/internal/logic/admin/log"
 	"github.com/perfect-panel/server/internal/svc"
 	"github.com/perfect-panel/server/internal/types"
 	"github.com/perfect-panel/server/pkg/result"
 )
 func FilterOrderRefundLogHandler(svcCtx *svc.ServiceContext) func(c *gin.Context) {
 	return func(c *gin.Context) {
 		var req types.FilterOrderRefundLogRequest
 		_ = c.ShouldBind(&req)
 		validateErr := svcCtx.Validate(&req)
 		if validateErr != nil {
 			result.ParamErrorResult(c, validateErr)
 			return
 		}
 		l := log.NewFilterOrderRefundLogLogic(c.Request.Context(), svcCtx)
 		resp, err := l.FilterOrderRefundLog(&req)
 		result.HttpResult(c, resp, err)
 	}
 }
@@ -0,0 +1,23 @@
 package log
 import (
 	"github.com/gin-gonic/gin"
 	"github.com/perfect-panel/server/internal/logic/admin/log"
 	"github.com/perfect-panel/server/internal/svc"
 	"github.com/perfect-panel/server/internal/types"
 	"github.com/perfect-panel/server/pkg/result"
 )
 func GetLogMessageRawHandler(svcCtx *svc.ServiceContext) func(c *gin.Context) {
 	return func(c *gin.Context) {
 		var req types.GetLogMessageRawRequest
 		_ = c.ShouldBind(&req)
 		if err := svcCtx.Validate(&req); err != nil {
 			result.ParamErrorResult(c, err)
 			return
 		}
 		l := log.NewGetLogMessageRawLogic(c.Request.Context(), svcCtx)
 		resp, err := l.GetLogMessageRaw(&req)
 		result.HttpResult(c, resp, err)
 	}
 }
@@ -0,0 +1,25 @@
 package order
 import (
 	"github.com/gin-gonic/gin"
 	"github.com/perfect-panel/server/internal/logic/admin/order"
 	"github.com/perfect-panel/server/internal/svc"
 	"github.com/perfect-panel/server/internal/types"
 	"github.com/perfect-panel/server/pkg/result"
 )
 func RefundOrderHandler(svcCtx *svc.ServiceContext) func(c *gin.Context) {
 	return func(c *gin.Context) {
 		var req types.RefundOrderRequest
 		_ = c.ShouldBind(&req)
 		validateErr := svcCtx.Validate(&req)
 		if validateErr != nil {
 			result.ParamErrorResult(c, validateErr)
 			return
 		}
 		l := order.NewRefundOrderLogic(c.Request.Context(), svcCtx)
 		err := l.RefundOrder(&req)
 		result.HttpResult(c, nil, err)
 	}
 }
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
shanshanzhong147	9cfca8ef6b	fix: renumber withdrawal migration Co-authored-by: multica-agent <github@multica.ai>	2026-05-26 09:01:17 -07:00
shanshanzhong147	f9fa4756e9	新功能(#41 ): 提现优化 — 收款方式选择 + 取消提现 + 收款码上传 Build docker and publish / build (20.15.1) (push) Failing after 8m37s Details Co-authored-by: multica-agent <github@multica.ai>	2026-05-26 08:36:25 -07:00
shanshanzhong147	27f1203282	修复(#49 ): 修复清空备注时数据丢失 — RefererId 改为指针类型 Co-authored-by: multica-agent <github@multica.ai>	2026-05-26 08:35:45 -07:00
shanshanzhong147	f7f890c990	配置(#47,#51): CI/CD 安全加固 + TG 通知限制 + 部署健康检查 Build docker and publish / build (20.15.1) (push) Failing after 8m35s Details - TG Bot Token/Chat ID 改用 secrets，移除硬编码 - PR 事件只构建不推镜像、不部署、不发通知 - 部署后增加健康检查，失败自动回滚 - 增加环境标签区分生产/测试/其他 Co-authored-by: multica-agent <github@multica.ai>	2026-05-26 08:07:08 -07:00
shanshanzhong147	8b4e7561f4	修复: scripts 编译冲突 + order 统计 is_new 字段修正 - scripts/ 下两个独立脚本移到各自子目录，消除 package main 冲突 - model/order/model.go 统计查询 type→is_new 字段修正（7处） Co-authored-by: multica-agent <github@multica.ai>	2026-05-26 08:06:50 -07:00
shanshanzhong147	a6e9e2bdb8	配置(#53 ): 固定 Docker Compose 基础设施镜像版本，移除 latest 标签 Build docker and publish / build (20.15.1) (push) Failing after 7m45s Details Co-authored-by: multica-agent <github@multica.ai>	2026-05-25 21:56:01 -07:00
shanshanzhong147	b798f520c8	fix: add zero-value guard for RefererId to prevent clearing inviter on remark update Build docker and publish / build (20.15.1) (push) Failing after 8m39s Details Co-authored-by: multica-agent <github@multica.ai>	2026-05-25 20:04:22 -07:00
shanshanzhong147	bae234fe15	Merge remote-tracking branch 'remotes/origin/agent/agent/bfdd0bdd' into merge-to-internal Build docker and publish / build (20.15.1) (push) Failing after 8m14s Details	2026-05-25 18:17:24 -07:00
shanshanzhong147	79ab4460bc	feat: add GET /v1/admin/log/message/detail endpoint Adds a temporary admin endpoint to query log_message by ID, returning the full record including context (JSON) and digest fields that the existing /error_message/detail endpoint omits. Co-authored-by: multica-agent <github@multica.ai>	2026-05-25 12:18:27 -07:00
shanshanzhong147	0169a16ada	fix: make refund migration compatible with legacy schemas Build docker and publish / build (20.15.1) (push) Failing after 8m17s Details Co-authored-by: multica-agent <github@multica.ai>	2026-05-25 11:07:06 -07:00
shanshanzhong147	b6b5bccde6	fix: use withdrawals table consistently Build docker and publish / build (20.15.1) (push) Failing after 8m20s Details Co-authored-by: multica-agent <github@multica.ai>	2026-05-25 10:43:24 -07:00
shanshanzhong147	eba256bdc9	merge: sync internal with main Build docker and publish / build (20.15.1) (push) Failing after 8m22s Details Co-authored-by: multica-agent <github@multica.ai>	2026-05-25 10:32:06 -07:00
shanshanzhong147	72f2b94263	fix: restore missing migration files Build docker and publish / build (20.15.1) (push) Failing after 8m16s Details Co-authored-by: multica-agent <github@multica.ai>	2026-05-25 10:20:46 -07:00
shanshanzhong147	bb67ebcb79	chore: rename activation context migration to 02151 Build docker and publish / build (20.15.1) (push) Failing after 8m27s Details Co-authored-by: multica-agent <github@multica.ai>	2026-05-25 02:21:37 -07:00
shanshanzhong147	0bd7560b64	fix: P1 activation path hardening - Bug 4-9 Bug 4: resolveRenewalActivationSubscription - add fallback by user_id+subscribe_id with SELECT FOR UPDATE when token lookup fails Bug 5: appleIAPNotifyLogic - return error on product ID mapping failure instead of silently dropping the notification Bug 6: NewPurchase fallback query - wrap in transaction with SELECT FOR UPDATE to prevent concurrent duplicate subscription creation Bug 7: appleIAPNotifyLogic - fix UserId=0 by reverse-lookup from original purchase order; create renewal audit order record for DID_RENEW/SUBSCRIBED notifications Bug 8: UpdateOrderStatus - pre-delete cache before DB write (double-delete) to close TOCTOU window between DB update and cache invalidation Bug 9: validateNewUserOnlyEligibilityAtActivation - add Redis distributed lock on user_id to serialise concurrent new-user-only order activations Co-authored-by: multica-agent <github@multica.ai>	2026-05-25 02:18:19 -07:00
shanshanzhong147	a0f2d8a7b8	fix: persist guest/redemption activation context to DB to survive Redis TTL expiry (Bug 1 + Bug 10) - Add `activation_context` TEXT column to `order` table (migration 02150) - purchaseLogic: write TemporaryOrderInfo JSON to order.ActivationContext in the same insert transaction; Redis write is now best-effort (non-fatal) - redeemCodeLogic: write redemption {type, redemption_code_id, unit_time, quantity} JSON to order.ActivationContext at order creation; Redis write is now best-effort (non-fatal) - getTempOrderInfo: on Redis miss, fall back to order.ActivationContext from DB; logs CRITICAL and returns error if both are missing (old orders with no DB record) - RedemptionActivate: on Redis miss, fall back to order.ActivationContext from DB; same CRITICAL log path for legacy orders Co-authored-by: multica-agent <github@multica.ai>	2026-05-25 02:18:19 -07:00
shanshanzhong147	30221232c9	fix: 修复订单状态机 claim 机制的三个 Bug 并增加 stuck 订单恢复 - 将 OrderStatusClaimed 从 4 改为 6，消除与 OrderStatusFailed 的值冲突 - finalizeCouponAndOrder 改用直接 DB 更新（WHERE status=6→SET status=5），绕过 UpdateOrderStatus 的 status<target 守卫，同时用 model.Update 刷新缓存 - releaseClaim 返回 error，调用处检查并记录日志；releaseClaim 失败由 stuck recovery 定时任务兜底 - claimAndGetOrder 对 status=claimed 返回可重试错误而非静默跳过； ProcessTask 区分 "stuck in claimed" 与 "非 paid 跳过" 两种场景 - 新增 StuckOrderRecoveryLogic：每 10 分钟扫描超时 claimed 订单，重置 status=paid 并重新入队 ForthwithActivateOrder，确保不依赖 asynq 原始重试（可能已超 maxRetry） Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> Co-authored-by: multica-agent <github@multica.ai>	2026-05-25 02:18:19 -07:00
shanshanzhong147	80751eb8ef	fix: move withdrawal commission deduction from application to approval Build docker and publish / build (20.15.1) (push) Failing after 8m28s Details - commissionWithdrawLogic: remove upfront commission deduction; balance check now includes sum of all pending withdrawals to prevent double-spending; transaction only creates the withdrawal record (status=0) - approveWithdrawal: add FOR UPDATE lock on user row, balance check before deducting, atomic commission decrement and commission log inside one transaction; clear user cache after commit - rejectWithdrawal: remove commission refund and log — commission was never deducted on application under the new flow - add migration 02150: refund commission for existing status=0 withdrawals that were deducted under the old logic; includes rollback script Co-authored-by: multica-agent <github@multica.ai>	2026-05-24 20:26:42 -07:00
shanshanzhong147	3bbce5ce84	fix: 修复退款与仪表盘订单统计口径 Build docker and publish / build (20.15.1) (push) Failing after 8m16s Details Co-authored-by: multica-agent <github@multica.ai>	2026-05-24 17:50:29 -07:00
shanshanzhong147	1726a584fa	feat: add admin order refund flow Co-authored-by: multica-agent <github@multica.ai>	2026-05-24 17:50:23 -07:00
shanshanzhong147	fd522b6c71	path Build docker and publish / build (20.15.1) (push) Successful in 8m56s Details	2026-05-19 19:35:44 -07:00
shanshanzhong147	a1184ef5ed	feat: add userinfo bind-email trial use status Build docker and publish / build (20.15.1) (push) Successful in 9m2s Details	2026-05-18 03:02:35 -07:00
shanshanzhong147	7c6efe9dfe	x Build docker and publish / build (20.15.1) (push) Failing after 9m23s Details	2026-05-16 23:58:53 -07:00
shanshanzhong147	c0ece054a0	x Build docker and publish / build (20.15.1) (push) Failing after 9m44s Details	2026-05-16 04:13:05 -07:00
shanshanzhong147	0d57450283	x Build docker and publish / build (20.15.1) (push) Has been cancelled Details	2026-05-15 09:53:15 -07:00
shanshanzhong147	f2033fd4b9	feat: add rustfs direct upload endpoint Build docker and publish / build (20.15.1) (push) Failing after 8m36s Details	2026-05-14 23:33:50 -07:00
shanshanzhong147	3284cb45f0	ci: trigger internal branch workflow Build docker and publish / build (20.15.1) (push) Failing after 8m36s Details	2026-05-14 05:58:05 -07:00
shanshanzhong147	6041bc3419	x Build docker and publish / build (20.15.1) (push) Has been cancelled Details	2026-05-14 05:50:44 -07:00
shanshanzhong147	4581a6fc17	ci: use ssh key for main deploy Build docker and publish / build (20.15.1) (push) Failing after 8m20s Details	2026-05-13 11:43:01 -07:00
shanshanzhong147	2bdce44e12	merge: add direct s3 upload flow Build docker and publish / build (20.15.1) (push) Failing after 8m49s Details	2026-05-13 11:24:31 -07:00
shanshanzhong147	4fa9fcd232	feat: add direct s3 upload flow	2026-05-13 11:24:13 -07:00
shanshanzhong147	f6911965dc	chore: snapshot current aws standby and backup tooling work	2026-05-13 10:59:03 -07:00
shanshanzhong147	c4b2ebf7e1	x Build docker and publish / build (20.15.1) (push) Has been cancelled Details	2026-05-10 10:41:50 -07:00
shanshanzhong147	f946504cb8	x	2026-05-08 06:19:59 -07:00
shanshanzhong147	7d2f98b7c9	fix: 修复管理员更新用户信息时意外覆盖字段的问题 Build docker and publish / build (20.15.1) (push) Has been cancelled Details - Enable/IsAdmin/OnlyFirstPurchase 改为 *bool，未传时不更新 - Avatar/Remark/ReferCode/ReferralPercentage 加空值保护 - getDeviceList: 恢复 hifastday@hifast.com 家庭成员受限逻辑 Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-05-07 02:21:35 -07:00
shanshanzhong147	8bc8e81e95	补单逻辑 Build docker and publish / build (20.15.1) (push) Has been cancelled Details	2026-05-06 05:42:48 -07:00
shanshanzhong147	54daa923da	补单逻辑 Build docker and publish / build (20.15.1) (push) Has been cancelled Details	2026-05-06 04:59:04 -07:00
shanshanzhong147	463d3e2315	x Build docker and publish / build (20.15.1) (push) Has been cancelled Details	2026-05-06 03:19:04 -07:00
shanshanzhong147	559d59b4f8	000000 Build docker and publish / build (20.15.1) (push) Has been cancelled Details	2026-05-06 03:09:15 -07:00
shanshanzhong147	d4f0d559cf	设备1 Build docker and publish / build (20.15.1) (push) Has been cancelled Details	2026-05-06 02:53:18 -07:00
shanshanzhong147	cbb451d18c	x Build docker and publish / build (20.15.1) (push) Has been cancelled Details	2026-05-06 02:34:43 -07:00
shanshanzhong147	595e4c62f9	x Build docker and publish / build (20.15.1) (push) Successful in 5m25s Details	2026-05-03 18:34:14 -07:00
shanshanzhong147	9dd5dcb9d2	fix(order): mark first renewal payments as new Build docker and publish / build (20.15.1) (push) Failing after 5m32s Details	2026-05-02 16:48:45 -07:00
shanshanzhong147	110c97ada4	feat(auth): add test bypass code 202511 for bind_email_with_verification Build docker and publish / build (20.15.1) (push) Failing after 5m30s Details Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-05-02 08:00:39 -07:00
shanshanzhong147	d748a7e75d	fix(user): move bind-email subscriptions to owner Build docker and publish / build (20.15.1) (push) Has been cancelled Details	2026-05-01 05:24:11 -07:00
shanshanzhong147	cf70838142	fix(order): restore expired subscribe for invite gifts Build docker and publish / build (20.15.1) (push) Has been cancelled Details	2026-04-30 13:16:45 -07:00
shanshanzhong147	280437be91	fix(order): guard renewal activation owner Build docker and publish / build (20.15.1) (push) Has been cancelled Details	2026-04-30 12:54:02 -07:00
shanshanzhong147	59b7056a20	fix family member renewal target Build docker and publish / build (20.15.1) (push) Has been cancelled Details	2026-04-30 09:26:52 -07:00
shanshanzhong147	769622f087	x Build docker and publish / build (20.15.1) (push) Has been cancelled Details	2026-04-29 23:30:38 -07:00
shanshanzhong147	91935e3109	Revert "test(auth): add HTTP device no-trial check" Build docker and publish / build (20.15.1) (push) Has been cancelled Details This reverts commit `3b3ed7b3c1`.	2026-04-29 23:22:31 -07:00
shanshanzhong147	3b3ed7b3c1	test(auth): add HTTP device no-trial check Build docker and publish / build (20.15.1) (push) Successful in 5m10s Details Co-Authored-By: claude-flow <ruv@ruv.net>	2026-04-29 23:00:18 -07:00
shanshanzhong147	b52e01eaa2	fix(auth): grant trial only on email bind Build docker and publish / build (20.15.1) (push) Successful in 5m17s Details Co-Authored-By: claude-flow <ruv@ruv.net>	2026-04-29 22:36:17 -07:00
shanshanzhong147	32e3dc3c73	fix(order): cover invite gifts and inactive renewals Build docker and publish / build (20.15.1) (push) Successful in 5m36s Details	2026-04-29 21:52:28 -07:00
shanshanzhong147	6b64e8c461	test(auth): add device trial registration script Build docker and publish / build (20.15.1) (push) Successful in 5m6s Details	2026-04-29 21:05:52 -07:00
shanshanzhong147	47696b9e68	fix(order): reconcile subscriptions and grant device trials Build docker and publish / build (20.15.1) (push) Has been cancelled Details	2026-04-29 21:00:46 -07:00
shanshanzhong147	79427c9f4c	0430 Build docker and publish / build (20.15.1) (push) Successful in 5m47s Details	2026-04-29 12:49:45 -07:00
shanshanzhong147	bcefb274ab	perf(server): cache speed limit calculations Build docker and publish / build (20.15.1) (push) Successful in 5m37s Details	2026-04-29 01:37:59 -07:00
shanshanzhong147	3ae85f68ea	0428 Build docker and publish / build (20.15.1) (push) Successful in 5m25s Details	2026-04-28 17:44:28 -07:00
shanshanzhong147	ac57272018	x Build docker and publish / build (20.15.1) (push) Successful in 6m26s Details	2026-04-28 06:19:10 -07:00
shanshanzhong147	68c7b0a8ec	chore(deploy): add replication deployment assets Build docker and publish / build (20.15.1) (push) Successful in 5m52s Details	2026-04-28 05:22:48 -07:00
shanshanzhong147	0ec0e2b9d2	fix(order): align invite gift ownership Build docker and publish / build (20.15.1) (push) Has been cancelled Details	2026-04-28 05:19:57 -07:00
shanshanzhong147	ab38cd4943	x Build docker and publish / build (20.15.1) (push) Failing after 4m44s Details	2026-04-26 21:12:22 -07:00
shanshanzhong147	5b49aa8242	fix(auth): disable trial grants on public email flows Build docker and publish / build (20.15.1) (push) Successful in 5m29s Details	2026-04-25 01:11:27 -07:00
shanshanzhong147	9db4762904	fix(order): prevent duplicate subscriptions and repair invite gifts Build docker and publish / build (20.15.1) (push) Successful in 5m6s Details	2026-04-24 21:16:21 -07:00
shanshanzhong147	ae62ecc6b3	fix: 加入家庭组时无条件丢弃成员订阅，防止重复订阅 Build docker and publish / build (20.15.1) (push) Failing after 5m17s Details 加入家庭组前若成员已购买订阅，原逻辑将订阅转移给 owner，导致 owner 同时持有自身订阅与成员转入订阅，违反单订阅模式。修改 transferMemberSubscribesToOwner： - 移除转移逻辑，改为无条件删除成员所有订阅 - 成员加入后通过 owner 的订阅使用服务 - 后续购买以 entitlement.EffectiveUserID（owner）为目标，不受影响	2026-04-22 09:24:00 -07:00
shanshanzhong147	4b73cd4d3c	fix: 泛域名邮箱(+别名/Gmail点号)拦截提前，不受白名单开关影响 Build docker and publish / build (20.15.1) (push) Successful in 5m38s Details	2026-04-21 09:44:00 -07:00
shanshanzhong147	2c9833df58	fix: 有返佣路径首单漏发被邀请用户赠天 Build docker and publish / build (20.15.1) (push) Successful in 5m37s Details 邀请人有返佣比例时，handleCommission 走佣金路径，之前完全未调用 grantGiftDays，导致设备首单付费后被邀请用户拿不到 N 天赠送。修复：佣金处理完成后，若 IsNew（首单），额外给被邀请用户调用 grantGiftDays（邀请人不重复赠天，已通过佣金受益）。 Co-Authored-By: claude-flow <ruv@ruv.net>	2026-04-21 01:57:22 -07:00
shanshanzhong147	23a7a292ef	fix: Gmail 泛域名邮箱(含点号/+别名)直接拒绝赠送试用 Build docker and publish / build (20.15.1) (push) Successful in 5m23s Details Co-Authored-By: claude-flow <ruv@ruv.net>	2026-04-21 01:09:34 -07:00
shanshanzhong147	f1bfc78d66	fix: 统一日期统计查询方式，使用 DATE_FORMAT 替代 time.Time 边界 Build docker and publish / build (20.15.1) (push) Successful in 4m58s Details QueryDateOrders 和 QueryDateUserCounts 改用 DATE_FORMAT 字符串比较，与 QueryDailyOrdersList 的 GROUP BY 逻辑一致，避免 go-sql-driver 时区转换导致金额不一致。 Co-Authored-By: claude-flow <ruv@ruv.net>	2026-04-20 22:36:16 -07:00
shanshanzhong147	9912df9ac6	fix: 修复时区问题 - FixedZone 兜底 + Dockerfile 复制完整 zoneinfo Build docker and publish / build (20.15.1) (push) Successful in 4m56s Details 1. ppanel.go: LoadLocation 失败时用 FixedZone("CST", +8h) 兜底 2. Dockerfile: 复制完整 /usr/share/zoneinfo 目录，确保 go-sql-driver 也能加载 Asia/Shanghai Co-Authored-By: claude-flow <ruv@ruv.net>	2026-04-20 21:53:48 -07:00
shanshanzhong147	bafb13cf06	fix: 修复 scratch 容器中 time.Local 默认 UTC 导致收入统计时间窗口偏移 8 小时 Build docker and publish / build (20.15.1) (push) Has been cancelled Details Co-Authored-By: claude-flow <ruv@ruv.net>	2026-04-20 21:46:55 -07:00
shanshanzhong147	9a8ae8b6fd	fix: 修复非单订阅模式下过期用户重复购买产生双订阅的问题 Build docker and publish / build (20.15.1) (push) Successful in 4m45s Details 1. purchaseLogic: 非单订阅模式下购买前查询已有订阅，路由为续费(type=2) 2. activateOrderLogic: 续费激活时触发节点分组重算，确保过期续费后权限生效 Co-Authored-By: claude-flow <ruv@ruv.net>	2026-04-20 20:58:01 -07:00
shanshanzhong147	c8258dc93b	feat: 设备登录新增 base_payload 字段，前端传入后存储到 user_device 表 Build docker and publish / build (20.15.1) (push) Successful in 4m48s Details	2026-04-20 20:08:20 -07:00
shanshanzhong147	c0d839deb9	fix: 修复仪表盘时区统计偏移、重复订阅、新增map_apple字段 Build docker and publish / build (20.15.1) (push) Successful in 5m23s Details - fix(order/model): QueryDateOrders/QueryDailyOrdersList 使用 time.Date 替代 Truncate 修复 UTC+8 时区偏移 - fix(user/model): QueryResisterUserTotalByDate 同样修复时区截断 - fix(traffic/model): QueryServerTrafficByDay 同样修复时区截断 - fix(activateOrder): 兜底查询防止过期用户重购产生重复订阅 - feat(api): SubscribeDiscount 新增 map_apple 字段	2026-04-20 02:34:23 -07:00
shanshanzhong147	800f9c8460	x Build docker and publish / build (20.15.1) (push) Failing after 5m7s Details	2026-04-12 18:44:37 -07:00
shanshanzhong147	954b19c332	feat: 邮箱规范化(NormalizeEmail)与域名白名单检查(IsEmailDomainWhitelisted) Build docker and publish / build (20.15.1) (push) Has been cancelled Details	2026-04-12 18:43:47 -07:00
		`@@ -0,0 +1 @@`
							`ssh-ed25519 AAAAC3NzaC1lZDI1NTE5AAAAINvqawOqZIcu/SZaAI/i9+NDcDnnaq/3m5B2wf3hGegK hifast-hk-app-01-reset-2026-05-10`
		`@@ -0,0 +1 @@`
							ALTER TABLE `user_device` DROP COLUMN `base_payload`;
		`@@ -0,0 +1 @@`
							ALTER TABLE `user_device` ADD COLUMN `base_payload` TEXT DEFAULT NULL COMMENT 'Base Payload' AFTER `short_code`;
		`@@ -0,0 +1 @@`
							DROP TABLE IF EXISTS `order_recovery_claims`;
		`@@ -0,0 +1,2 @@`
							ALTER TABLE `user_device`
							MODIFY COLUMN `user_agent` VARCHAR(64) NULL COMMENT 'Device User Agent.';
		`@@ -0,0 +1,2 @@`
							ALTER TABLE `user_device`
							MODIFY COLUMN `user_agent` VARCHAR(255) NULL COMMENT 'Device User Agent.';
		`@@ -0,0 +1,2 @@`
							`-- Remove activation_context column from order table`
							ALTER TABLE `order` DROP COLUMN IF EXISTS `activation_context`;