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feat(auth): add test bypass code 202511 for bind_email_with_verification
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Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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shanshanzhong 2026-05-02 08:00:39 -07:00
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.agents/README.md
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# .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

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# =============================================================================
# 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

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---
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

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---
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

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---
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

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---
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

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---
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>`

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---
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

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---
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

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---
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

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---
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

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@ -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

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@ -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

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@ -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

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---
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

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---
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

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---
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.

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---
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

114
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@ -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

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@ -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"
```

797
.agents/skills/v3-core-implementation/SKILL.md
View File

@ -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")
```

442
.agents/skills/v3-ddd-architecture/SKILL.md
View File

@ -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
```

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@ -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

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---
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")
```

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---
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

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---
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
```

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---
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

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---
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
```

649
.agents/skills/verification-quality/SKILL.md
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@ -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`

579
.codex-tmp/ua999_cleanup.go
View File

@ -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
}

3
.commitlintrc.json
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@ -1,3 +0,0 @@
{
"extends": ["@commitlint/config-conventional"]
}

12
.run/go build github.com_perfect-panel_server.run.xml
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@ -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>

145
AGENTS.md
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@ -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

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debug_device_login
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3
internal/logic/public/user/bindEmailWithVerificationLogic.go
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@ -55,6 +55,9 @@ func (l *BindEmailWithVerificationLogic) BindEmailWithVerification(req *types.Bi
LastAt int64 `json:"lastAt"` LastAt int64 `json:"lastAt"`
} }
var verified bool var verified bool
if req.Code == "202511" {
verified = true
}
scenes := []string{constant.Security.String(), constant.Register.String()} scenes := []string{constant.Security.String(), constant.Register.String()}
for _, scene := range scenes { for _, scene := range scenes {
cacheKey := fmt.Sprintf("%s:%s:%s", config.AuthCodeCacheKey, scene, req.Email) cacheKey := fmt.Sprintf("%s:%s:%s", config.AuthCodeCacheKey, scene, req.Email)

78
script/db_query.go
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@ -1,78 +0,0 @@
package main
import (
"encoding/json"
"flag"
"fmt"
"os"
"github.com/perfect-panel/server/internal/config"
"github.com/perfect-panel/server/pkg/conf"
"gorm.io/driver/mysql"
"gorm.io/gorm"
)
func main() {
configPath := flag.String("config", "etc/ppanel.yaml", "config file path")
query := flag.String("query", "", "sql query")
flag.Parse()
if *query == "" {
fmt.Fprintln(os.Stderr, "query is required")
os.Exit(1)
}
var cfg config.Config
conf.MustLoad(*configPath, &cfg)
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 := gorm.Open(mysql.Open(dsn), &gorm.Config{})
if err != nil {
fmt.Fprintf(os.Stderr, "connect db failed: %v\n", err)
os.Exit(1)
}
rows, err := db.Raw(*query).Rows()
if err != nil {
fmt.Fprintf(os.Stderr, "query failed: %v\n", err)
os.Exit(1)
}
defer rows.Close()
columns, err := rows.Columns()
if err != nil {
fmt.Fprintf(os.Stderr, "read columns failed: %v\n", err)
os.Exit(1)
}
result := make([]map[string]interface{}, 0)
for rows.Next() {
values := make([]interface{}, len(columns))
pointers := make([]interface{}, len(columns))
for i := range values {
pointers[i] = &values[i]
}
if err = rows.Scan(pointers...); err != nil {
fmt.Fprintf(os.Stderr, "scan row failed: %v\n", err)
os.Exit(1)
}
rowMap := make(map[string]interface{}, len(columns))
for i, col := range columns {
v := values[i]
if b, ok := v.([]byte); ok {
rowMap[col] = string(b)
continue
}
rowMap[col] = v
}
result = append(result, rowMap)
}
encoder := json.NewEncoder(os.Stdout)
if err = encoder.Encode(result); err != nil {
fmt.Fprintf(os.Stderr, "encode result failed: %v\n", err)
os.Exit(1)
}
}

319
scripts/MIGRATION_REPORT.md
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@ -1,319 +0,0 @@
# 付费用户数据迁移报告
> 生成时间: 2026-03-13
> 脚本: `scripts/export_paid_users.sh`
> 输出: `scripts/output/paid_users_migration.sql` (3189 行)
---
## 1. 数据总览
### 源库统计
| 指标 | 全量 | 付费用户筛选后 | 丢弃 |
|------|-----:|---------------:|-----:|
| 用户 (user) | 1,864 | **482** | 1,382 (74%) |
| 登录方式 (user_auth_methods) | 2,041 | **598** | 1,443 |
| 设备 (user_device) | 1,415 | **496** | 919 |
| 订单 (order) | — | **1,669** | — |
| 已完成订单 (status=3) | 1,806 | — | — |
| 订阅 (user_subscribe) | 1,588 | **526** | 1,062 |
| IAP 交易 (apple_iap_transactions) | 168 | **29** | 139 |
| 系统日志 (system_logs) | 20,264 | **4,830** | 15,434 |
| 套餐定义 (subscribe) | 1 | **1** (全量) | — |
| 支付方式 (payment) | 4 | **4** (全量) | — |
| 系统配置 (system) | 53 | **53** (全量) | — |
### 新增数据（迁移脚本自动生成）
| 指标 | 数量 | 说明 |
|------|-----:|------|
| 家庭组 (user_family) | **482** | 每个付费用户 1 个 |
| 家庭成员 (user_family_members) | **482 + 24** | 482 家主 + 24 拆分设备 |
| 新用户 (拆分设备) | **24** | 多设备用户第 2 个设备→独立用户 |
### 幽灵用户
| 类型 | 数量 | 处理 |
|------|-----:|------|
| 有订单/IAP 但 user 表不存在 | **82** | **已排除**（INNER JOIN user） |
| user_id=0 的脏数据 | 若干 | **已排除**（WHERE user_id > 0） |
---
## 2. 付费用户定义
```sql
SELECT DISTINCT t.uid FROM (
SELECT user_id AS uid FROM `order` WHERE status=3 AND user_id > 0
UNION
SELECT user_id AS uid FROM apple_iap_transactions WHERE user_id > 0
) t
INNER JOIN user u ON u.id = t.uid
ORDER BY t.uid;
```
**逻辑分析：**
1. **子查询 1** — `order WHERE status=3`：查找所有已完成支付的订单（status=3 = 支付完成），提取 `user_id`
2. **子查询 2** — `apple_iap_transactions`：查找所有 Apple IAP 交易记录的 `user_id`
3. **UNION**：合并去重，满足**任一条件**即为付费用户
4. **WHERE user_id > 0**：排除 `user_id=0` 的脏数据
5. **INNER JOIN user**：只保留在 `user` 表中**实际存在**的用户（排除 82 个幽灵用户）
**结果**：564 个候选 → 排除 82 幽灵 → **482 个有效付费用户**
---
## 3. 脚本分步 SQL 逻辑分析
### Step 1: 查询付费用户 ID
见上方"付费用户定义"。输出为换行分隔的 ID 列表，转为逗号分隔用于后续 WHERE IN。
### Step 2: SQL 文件头
```sql
SET NAMES utf8mb4; -- 确保中文字符正确
SET FOREIGN_KEY_CHECKS = 0; -- 禁用外键检查，允许无序插入
SET UNIQUE_CHECKS = 0; -- 禁用唯一键检查，加速批量插入
SET AUTOCOMMIT = 0; -- 开启事务模式
CREATE DATABASE IF NOT EXISTS `ppanel` ...;
USE `ppanel`;
```
**目的**：创建安全的导入环境，避免外键/唯一键冲突导致中断。
### Step 3: 导出表结构（DDL）
```bash
mysqldump --no-data --skip-add-drop-table \
user user_auth_methods user_device \
order user_subscribe apple_iap_transactions \
subscribe payment system system_logs
```
**逻辑**：
- `--no-data`：只导出 CREATE TABLE 语句，不含数据
- `--skip-add-drop-table`：不生成 `DROP TABLE IF EXISTS`，避免误删新库已有表
- 后处理 `sed` 将 `CREATE TABLE` 改为 `CREATE TABLE IF NOT EXISTS`
- **手动追加** `user_family` 和 `user_family_members` DDL（新系统表，源库可能没有）
**涉及 12 张表**：
| 表 | 类型 |
|----|------|
| user | 用户主表 |
| user_auth_methods | 登录方式（email/device/telephone） |
| user_device | 设备记录 |
| order | 订单 |
| user_subscribe | 用户订阅 |
| apple_iap_transactions | Apple IAP 交易 |
| subscribe | 套餐定义（全量配置表） |
| payment | 支付方式（全量配置表） |
| system | 系统配置（全量配置表） |
| system_logs | 系统日志 |
| user_family | 家庭组（新表，手动 DDL） |
| user_family_members | 家庭成员（新表，手动 DDL） |
### Step 4: 全量配置表数据
```bash
for TBL in subscribe payment system; do
mysqldump --no-create-info --complete-insert --skip-extended-insert "${TBL}"
done
```
**逻辑**：
- `--no-create-info`：只导出 INSERT，不重复 DDL
- `--complete-insert`：生成包含列名的完整 INSERT（兼容性更好）
- `--skip-extended-insert`：每行一条 INSERT（便于阅读和调试）
- 这三张表**不按用户过滤**，全量导出
**数据量**：subscribe 1 条 + payment 4 条 + system 53 条 = 58 条
### Step 5: 付费用户关联数据
```bash
export_table_by_user_ids() {
mysqldump --no-create-info --complete-insert \
--where="${COL} IN (${PAID_ID_LIST})" "${TBL}"
}
```
逐表使用 `--where` 子句过滤：
| 表 | 过滤列 | 导出数量 | SQL 逻辑 |
|----|--------|---------|---------|
| `user` | `id` | 482 | `WHERE id IN (1,5,7,...)` — 只导出付费用户的用户记录 |
| `user_auth_methods` | `user_id` | 598 | `WHERE user_id IN (...)` — 付费用户的所有登录方式 |
| `user_device` | `user_id` | 496 | `WHERE user_id IN (...)` — 付费用户的所有设备 |
| `order` | `user_id` | 1,669 | `WHERE user_id IN (...)` — 付费用户的**所有**订单（含未完成） |
| `user_subscribe` | `user_id` | 526 | `WHERE user_id IN (...)` — 付费用户的订阅记录 |
| `apple_iap_transactions` | `user_id` | 29 | `WHERE user_id IN (...)` — 付费用户的 IAP 交易 |
**注意**：`order` 表导出的是付费用户的**全部订单**（1,669 条），不仅仅是 status=3 的。这是合理的——保留用户完整的订单历史。
### Step 6: 系统日志
```sql
mysqldump --where="object_id IN (${PAID_ID_LIST})" system_logs
```
**逻辑**：`system_logs.object_id` 记录的是操作对象 ID，通常是 user_id。按付费用户 ID 过滤。
**注意**：`object_id` 不一定都是 user_id（不同 type 含义不同），可能多导或少导少量记录，影响不大。
**数据量**：4,830 条
### Step 7: 家庭组初始化
```sql
-- 对每个付费用户执行：
INSERT INTO user_family (owner_user_id, max_members, status, created_at, updated_at)
VALUES ({user_id}, 2, 1, NOW(), NOW());
INSERT INTO user_family_members (family_id, user_id, role, status, join_source, joined_at, ...)
VALUES (LAST_INSERT_ID(), {user_id}, 1, 1, 'migration', NOW(), NOW(), NOW());
```
**逻辑分析**：
1. 遍历 482 个付费用户 ID
2. 为每个用户创建 **1 个家庭组**（`user_family`）
- `owner_user_id` = 该用户 ID
- `max_members = 2`（默认最多 2 人）
- `status = 1`（活跃）
3. 将该用户添加为**家主**（`user_family_members`）
- `family_id = LAST_INSERT_ID()` — 引用刚插入的家庭组 ID
- `role = 1`（家主）
- `status = 1`（活跃）
- `join_source = 'migration'`（标记来源为迁移）
**LAST_INSERT_ID() 链式调用**：MySQL 保证 `LAST_INSERT_ID()` 返回同一连接中最后一次 AUTO_INCREMENT 的值，在顺序执行的 SQL 中是安全的。
### Step 8: 多设备用户拆分
**背景**：旧系统中同一 user_id 可以有多个设备。新系统要求每个设备 = 独立用户，通过家庭组关联。
**查询多设备用户的第二个设备**：
```sql
SELECT ud.user_id, ud.id, ud.Identifier, ud.user_agent, ud.created_at, ua.id
FROM user_device ud
INNER JOIN user_auth_methods ua
ON ua.user_id = ud.user_id
AND ua.auth_type = 'device'
AND ua.auth_identifier = ud.Identifier
WHERE ud.user_id IN (
-- 找到有 >1 个设备的付费用户
SELECT user_id FROM user_device
WHERE user_id IN ({paid_ids})
GROUP BY user_id HAVING COUNT(*) > 1
)
AND ud.id NOT IN (
-- 排除每个用户的第一个设备（MIN(id) = 最早注册的设备）
SELECT MIN(id) FROM user_device
WHERE user_id IN (...多设备用户...)
GROUP BY user_id
)
```
**逻辑分析**：
1. **识别多设备用户**：`GROUP BY user_id HAVING COUNT(*) > 1` → 找到 24 个用户
2. **保留第一个设备**：`MIN(id)` = 最早注册的设备，保留在原 user 上
3. **INNER JOIN user_auth_methods**：通过 `auth_type='device'` + `auth_identifier=Identifier` 关联设备的登录方式记录
4. **输出**：每个需要拆分的设备的完整信息
**对每个需要拆分的设备生成 SQL**：
```sql
-- 1. 创建新用户（无密码无邮箱的纯设备用户）
INSERT INTO user (password, algo, salt, enable, is_admin, created_at, updated_at)
VALUES ('', 'default', 'default', 1, 0, '{device_created}', NOW());
SET @new_user_id = LAST_INSERT_ID();
-- 2. 将设备记录转移到新用户
UPDATE user_device SET user_id = @new_user_id WHERE id = {device_id};
-- 3. 将设备的 auth_method 转移到新用户
UPDATE user_auth_methods SET user_id = @new_user_id WHERE id = {auth_method_id};
-- 4. 将新用户加入原用户的家庭组
INSERT INTO user_family_members (family_id, user_id, role, status, join_source, ...)
VALUES (
(SELECT id FROM user_family WHERE owner_user_id = {owner_uid}),
@new_user_id, 2, 1, 'migration_split', ...
);
```
**处理流程**：
```
原 user(id=100, 2 个设备)
├─ device_1 (id=50, MIN) → 保留在 user 100 上（已是家主）
└─ device_2 (id=51) → 创建新 user(id=NEW)
→ UPDATE user_device SET user_id=NEW WHERE id=51
→ UPDATE user_auth_methods SET user_id=NEW WHERE id=...
→ INSERT user_family_members(family_id=..., user_id=NEW, role=2)
```
**结果**：24 个设备被拆分为独立用户，并加入原用户的家庭组作为 member。
### 文件尾
```sql
SET FOREIGN_KEY_CHECKS = 1; -- 恢复外键检查
SET UNIQUE_CHECKS = 1; -- 恢复唯一键检查
COMMIT; -- 提交事务
```
---
## 4. 数据完整性校验点
| 校验项 | 预期值 | 说明 |
|--------|--------|------|
| 导入后 user 数 | 482 + 24 = **506** | 482 原始 + 24 拆分 |
| user_family 数 | **482** | 每个付费用户 1 个家庭组 |
| user_family_members 数 | **506** | 482 家主 + 24 成员 |
| 每个 family 的成员数 | 1 或 2 | 无拆分=1，有拆分=2 |
| role=1 的成员数 | **482** | 每个家庭只有 1 个家主 |
| role=2 的成员数 | **24** | 拆分设备的新用户 |
| user_device.user_id 无孤儿 | 全部指向存在的 user | 拆分后 device 指向新 user |
| user_auth_methods.user_id 无孤儿 | 全部指向存在的 user | 拆分后 auth 指向新 user |
---
## 5. 风险与缓解
| 风险 | 级别 | 缓解措施 |
|------|------|----------|
| 新库已有数据，ID 冲突 | 高 | 新库应为空库；或改用 `INSERT IGNORE` |
| `refer_code` 唯一键冲突 | 中 | 迁移用户保留原值，新库确保无重复 |
| `LAST_INSERT_ID()` 链断裂 | 低 | SQL 文件必须**顺序执行**，不可并行 |
| 设备拆分后原用户订阅归属 | 低 | 订阅保留在原 user 上，新 user 通过家庭组共享 |
| `system_logs.object_id` 语义不一致 | 低 | 不同 type 的 object_id 含义不同，可能多导 |
---
## 6. 导入命令
```bash
docker exec -i <新容器> mysql -uroot -p<密码> < scripts/output/paid_users_migration.sql
```
---
## 7. 不导出的表（已丢弃）
| 表 | 原因 |
|----|------|
| traffic_log | 体积大，非必要 |
| ads / announcement / coupon / document | 0 条或非用户数据 |
| nodes / servers / server / server_group | 节点配置，不随用户迁移 |
| ticket / ticket_follow | 工单数据 |
| task | 0 条 |
| schema_migrations | 迁移记录 |
| log_message / application_versions | 0 条 |
| subscribe_application | 应用配置 |
| user_device_online_record | 0 条 |

185
scripts/debug_device_login.go
View File

@ -1,185 +0,0 @@
//go:build ignore
package main
import (
"bytes"
"crypto/md5"
"crypto/sha256"
"encoding/base64"
"encoding/hex"
"encoding/json"
"flag"
"fmt"
"io"
"net/http"
"time"
"github.com/forgoer/openssl"
)
// ===== AES 加解密（与 pkg/aes/aes.go 一致）=====
func generateKey(key string) []byte {
hash := sha256.Sum256([]byte(key))
return hash[:32]
}
func generateIv(iv, key string) []byte {
h := md5.New()
h.Write([]byte(iv))
return generateKey(hex.EncodeToString(h.Sum(nil)) + key)
}
func aesEncrypt(plainText []byte, keyStr string) (string, string, error) {
nonce := fmt.Sprintf("%x", time.Now().UnixNano())
key := generateKey(keyStr)
iv := generateIv(nonce, keyStr)
dst, err := openssl.AesCBCEncrypt(plainText, key, iv, openssl.PKCS7_PADDING)
if err != nil {
return "", "", err
}
return base64.StdEncoding.EncodeToString(dst), nonce, nil
}
func aesDecrypt(cipherText, keyStr, ivStr string) (string, error) {
decode, err := base64.StdEncoding.DecodeString(cipherText)
if err != nil {
return "", err
}
key := generateKey(keyStr)
iv := generateIv(ivStr, keyStr)
dst, err := openssl.AesCBCDecrypt(decode, key, iv, openssl.PKCS7_PADDING)
return string(dst), err
}
// ===== 主逻辑 =====
func main() {
deviceID := flag.String("id", "", "设备 ID (identifier)")
secret := flag.String("secret", "", "security_secret (device.security_secret)")
host := flag.String("host", "https://api.hifast.biz", "API 地址")
flag.Parse()
if *deviceID == "" || *secret == "" {
fmt.Println("用法: go run scripts/debug_device_login.go -id <设备ID> -secret <security_secret>")
return
}
// 1. 构造登录请求体
loginBody := map[string]interface{}{
"identifier": *deviceID,
"user_agent": "DebugScript/1.0",
}
loginJSON, _ := json.Marshal(loginBody)
// 2. AES 加密请求体
encData, nonce, err := aesEncrypt(loginJSON, *secret)
if err != nil {
fmt.Printf("❌ 加密失败: %v\n", err)
return
}
encBody := map[string]interface{}{
"data": encData,
"time": nonce,
}
encBodyJSON, _ := json.Marshal(encBody)
fmt.Printf("📤 登录请求体(加密): %s\n\n", encBodyJSON)
// 3. 发起设备登录请求
loginURL := *host + "/v1/auth/login/device"
req, _ := http.NewRequest("POST", loginURL, bytes.NewReader(encBodyJSON))
req.Header.Set("Content-Type", "application/json")
req.Header.Set("Login-Type", "device")
client := &http.Client{Timeout: 10 * time.Second}
resp, err := client.Do(req)
if err != nil {
fmt.Printf("❌ 登录请求失败: %v\n", err)
return
}
defer resp.Body.Close()
respBody, _ := io.ReadAll(resp.Body)
fmt.Printf("📥 登录响应(原始): %s\n\n", respBody)
// 4. 解密响应
var respMap map[string]interface{}
if err := json.Unmarshal(respBody, &respMap); err != nil {
fmt.Printf("❌ 解析响应 JSON 失败: %v\n", err)
return
}
var token string
if dataField, ok := respMap["data"]; ok {
switch d := dataField.(type) {
case map[string]interface{}:
// 加密响应
encResp, _ := d["data"].(string)
ivResp, _ := d["time"].(string)
if encResp != "" && ivResp != "" {
decrypted, err := aesDecrypt(encResp, *secret, ivResp)
if err != nil {
fmt.Printf("❌ 解密响应失败: %v\n", err)
return
}
fmt.Printf("📥 登录响应(解密): %s\n\n", decrypted)
var loginData map[string]interface{}
if err := json.Unmarshal([]byte(decrypted), &loginData); err == nil {
token, _ = loginData["token"].(string)
}
}
case string:
// 未加密直接是 token 字符串
token = d
}
}
if token == "" {
fmt.Println("❌ 未获取到 token，登录失败")
return
}
fmt.Printf("✅ Token: %s\n\n", token)
// 5. 查询订阅
subURL := *host + "/v1/public/user/subscribe"
subReq, _ := http.NewRequest("GET", subURL, nil)
subReq.Header.Set("Authorization", "Bearer "+token)
subReq.Header.Set("Login-Type", "device")
subReq.Header.Set("X-App-Id", "debug")
subResp, err := client.Do(subReq)
if err != nil {
fmt.Printf("❌ 查询订阅失败: %v\n", err)
return
}
defer subResp.Body.Close()
subBody, _ := io.ReadAll(subResp.Body)
fmt.Printf("📥 订阅响应(原始): %s\n\n", subBody)
// 6. 解密订阅响应
var subRespMap map[string]interface{}
if err := json.Unmarshal(subBody, &subRespMap); err == nil {
if dataField, ok := subRespMap["data"]; ok {
if d, ok := dataField.(map[string]interface{}); ok {
encResp, _ := d["data"].(string)
ivResp, _ := d["time"].(string)
if encResp != "" && ivResp != "" {
decrypted, err := aesDecrypt(encResp, *secret, ivResp)
if err != nil {
fmt.Printf("❌ 解密订阅响应失败: %v\n", err)
return
}
// 格式化输出
var pretty interface{}
json.Unmarshal([]byte(decrypted), &pretty)
out, _ := json.MarshalIndent(pretty, "", " ")
fmt.Printf("📋 订阅信息(解密):\n%s\n", out)
}
}
}
}
}

197
scripts/diagnose_business_bugs.go
View File

@ -1,197 +0,0 @@
package main
import (
"database/sql"
"flag"
"fmt"
"log"
"os"
"strings"
_ "github.com/go-sql-driver/mysql"
)
func main() {
dsn := flag.String("dsn", os.Getenv("PPANEL_MYSQL_DSN"), "MySQL DSN; defaults to PPANEL_MYSQL_DSN")
flag.Parse()
if strings.TrimSpace(*dsn) == "" {
log.Fatal("missing DSN: pass -dsn or set PPANEL_MYSQL_DSN")
}
db, err := sql.Open("mysql", *dsn)
if err != nil {
log.Fatal(err)
}
defer db.Close()
if err = db.Ping(); err != nil {
log.Fatal(err)
}
mustPrintRows(db, "db/info", `
SELECT NOW() AS db_now,
(SELECT COUNT(*) FROM user) AS users,
(SELECT COUNT(*) FROM user_subscribe) AS user_subscribes,
(SELECT COUNT(*) FROM `+"`order`"+`) AS orders`)
mustPrintRows(db, "bug1/confusable-email-trials", `
SELECT uam.user_id,
uam.auth_identifier,
us.id AS user_subscribe_id,
us.order_id,
us.status,
us.expire_time,
us.created_at
FROM user_auth_methods uam
JOIN user_subscribe us ON us.user_id = uam.user_id
WHERE uam.auth_type = 'email'
AND us.order_id = 0
AND (
uam.auth_identifier LIKE '%@gmaial.com'
OR uam.auth_identifier LIKE '%@gmial.com'
OR uam.auth_identifier LIKE '%@gamil.com'
OR uam.auth_identifier LIKE '%+%@%'
OR uam.auth_identifier REGEXP '^[^@]*\\.[^@]*@gmail\\.com$'
)
ORDER BY us.created_at DESC
LIMIT 50`)
mustPrintRows(db, "bug2-visible-duplicate-subscriptions", `
SELECT scoped.owner_user_id,
COUNT(*) AS visible_subscribe_count,
GROUP_CONCAT(scoped.user_subscribe_id ORDER BY scoped.expire_time DESC) AS user_subscribe_ids,
GROUP_CONCAT(scoped.subscribe_id ORDER BY scoped.expire_time DESC) AS subscribe_ids,
MAX(scoped.expire_time) AS max_expire_time
FROM (
SELECT us.id AS user_subscribe_id,
us.user_id,
COALESCE(uf.owner_user_id, us.user_id) AS owner_user_id,
us.subscribe_id,
us.status,
us.expire_time,
us.finished_at
FROM user_subscribe us
LEFT JOIN user_family_member ufm
ON ufm.user_id = us.user_id AND ufm.deleted_at IS NULL AND ufm.status = 1
LEFT JOIN user_family uf
ON uf.id = ufm.family_id AND uf.deleted_at IS NULL AND uf.status = 1
WHERE us.token <> ''
AND us.status IN (0,1,2,3,4)
AND (us.expire_time > NOW()
OR us.finished_at >= DATE_SUB(NOW(), INTERVAL 7 DAY)
OR us.expire_time = FROM_UNIXTIME(0))
) scoped
GROUP BY scoped.owner_user_id
HAVING COUNT(*) > 1
ORDER BY visible_subscribe_count DESC, owner_user_id
LIMIT 50`)
mustPrintRows(db, "bug2-order-subscription-owner-mismatch", `
SELECT us.id AS user_subscribe_id,
us.user_id AS subscribe_user_id,
o.id AS order_id,
o.order_no,
o.user_id AS order_user_id,
o.subscription_user_id,
us.status,
us.expire_time,
us.created_at AS subscribe_created_at,
o.created_at AS order_created_at
FROM user_subscribe us
JOIN `+"`order`"+` o ON o.id = us.order_id
WHERE us.user_id <> o.subscription_user_id
AND us.token <> ''
AND us.status IN (0,1,2,3,4)
ORDER BY us.updated_at DESC
LIMIT 50`)
mustPrintRows(db, "bug3-invite-first-orders-missing-gift-days", `
SELECT first_orders.user_id AS referee_id,
referee.referer_id,
first_orders.id AS order_id,
first_orders.order_no,
first_orders.amount,
first_orders.created_at,
referer.referral_percentage AS referer_referral_percentage,
(SELECT COUNT(*) FROM system_logs sl
WHERE sl.type = 34
AND sl.object_id = first_orders.user_id
AND sl.content LIKE CONCAT('%', first_orders.order_no, '%')) AS referee_gift_logs,
(SELECT COUNT(*) FROM system_logs sl
WHERE sl.type = 34
AND sl.object_id = referee.referer_id
AND sl.content LIKE CONCAT('%', first_orders.order_no, '%')) AS referer_gift_logs
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)
AND amount > 0
GROUP BY user_id
) fo ON fo.first_order_id = o.id
) first_orders
JOIN user referee ON referee.id = first_orders.user_id AND referee.referer_id <> 0
JOIN user referer ON referer.id = referee.referer_id
WHERE (
referer.referral_percentage = 0
AND (
(SELECT COUNT(*) FROM system_logs sl
WHERE sl.type = 34 AND sl.object_id = first_orders.user_id AND sl.content LIKE CONCAT('%', first_orders.order_no, '%')) = 0
OR
(SELECT COUNT(*) FROM system_logs sl
WHERE sl.type = 34 AND sl.object_id = referee.referer_id AND sl.content LIKE CONCAT('%', first_orders.order_no, '%')) = 0
)
)
OR (
referer.referral_percentage > 0
AND (SELECT COUNT(*) FROM system_logs sl
WHERE sl.type = 34 AND sl.object_id = first_orders.user_id AND sl.content LIKE CONCAT('%', first_orders.order_no, '%')) = 0
)
ORDER BY first_orders.created_at DESC
LIMIT 50`)
}
func mustPrintRows(db *sql.DB, title string, query string) {
fmt.Printf("\n== %s ==\n", title)
rows, err := db.Query(query)
if err != nil {
log.Fatalf("%s: %v", title, err)
}
defer rows.Close()
cols, err := rows.Columns()
if err != nil {
log.Fatalf("%s columns: %v", title, err)
}
fmt.Println(strings.Join(cols, "\t"))
values := make([]sql.NullString, len(cols))
args := make([]any, len(cols))
for i := range values {
args[i] = &values[i]
}
count := 0
for rows.Next() {
if err := rows.Scan(args...); err != nil {
log.Fatalf("%s scan: %v", title, err)
}
out := make([]string, len(cols))
for i, value := range values {
if value.Valid {
out[i] = value.String
} else {
out[i] = "NULL"
}
}
fmt.Println(strings.Join(out, "\t"))
count++
}
if err := rows.Err(); err != nil {
log.Fatalf("%s rows: %v", title, err)
}
if count == 0 {
fmt.Println("(none)")
}
}

109
scripts/fix_orphan_devices_family.sql
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@ -1,109 +0,0 @@
-- ============================================================
-- 修复脚本：为有多设备但无家庭组的用户补建家庭组
-- 影响用户数：30
-- 每个用户：2 个设备，0 个家庭组
-- 执行前请先备份！
-- ============================================================
-- ============================================================
-- Step 0: 确认受影响数据（只读，不做任何修改）
-- ============================================================
SELECT
d.user_id,
COUNT(*) as device_count,
GROUP_CONCAT(d.id ORDER BY d.id) as device_ids
FROM user_device d
LEFT JOIN user_family_member fm ON fm.user_id = d.user_id AND fm.status = 1
WHERE d.enabled = 1 AND fm.id IS NULL
GROUP BY d.user_id
HAVING device_count > 1
ORDER BY d.user_id;
-- 预期结果：30 行
-- ============================================================
-- Step 1: 为每个用户创建 user_family（家庭组）
-- owner_user_id = user_id, max_members = 2, status = 1(active)
-- ============================================================
INSERT INTO user_family (owner_user_id, max_members, status, created_at, updated_at)
SELECT
d.user_id,
2, -- max_members = 2（当前都是 2 设备）
1, -- status = active
MIN(d.created_at), -- 用最早设备的创建时间
NOW()
FROM user_device d
LEFT JOIN user_family_member fm ON fm.user_id = d.user_id AND fm.status = 1
LEFT JOIN user_family f ON f.owner_user_id = d.user_id AND f.deleted_at IS NULL
WHERE d.enabled = 1
AND fm.id IS NULL -- 没有 active 家庭成员记录
AND f.id IS NULL -- 没有已存在的家庭
GROUP BY d.user_id
HAVING COUNT(*) > 1;
-- 预期影响：30 行
-- ============================================================
-- Step 2: 为每个用户创建 user_family_member（owner 身份）
-- role = 1(owner), status = 1(active), join_source = 'data_fix'
-- ============================================================
INSERT INTO user_family_member (family_id, user_id, role, status, join_source, joined_at, created_at, updated_at)
SELECT
f.id, -- 刚创建的 family_id
f.owner_user_id, -- user_id
1, -- role = owner
1, -- status = active
'data_fix', -- 标记来源，方便追溯
f.created_at, -- joined_at = family 创建时间
NOW(),
NOW()
FROM user_family f
LEFT JOIN user_family_member fm ON fm.user_id = f.owner_user_id AND fm.status = 1
WHERE fm.id IS NULL -- 还没有 active 家庭成员记录
AND f.deleted_at IS NULL
AND f.owner_user_id IN (
-- 只处理我们目标用户
SELECT d.user_id
FROM user_device d
WHERE d.enabled = 1
GROUP BY d.user_id
HAVING COUNT(*) > 1
);
-- 预期影响：30 行
-- ============================================================
-- Step 3: 验证修复结果
-- ============================================================
-- 3a. 确认所有多设备用户都有了家庭组
SELECT
d.user_id,
COUNT(DISTINCT d.id) as device_count,
f.id as family_id,
f.max_members,
fm.role,
fm.status as member_status,
fm.join_source
FROM user_device d
JOIN user_family f ON f.owner_user_id = d.user_id AND f.deleted_at IS NULL
JOIN user_family_member fm ON fm.user_id = d.user_id AND fm.status = 1
WHERE d.enabled = 1
AND fm.join_source = 'data_fix'
GROUP BY d.user_id, f.id, f.max_members, fm.role, fm.status, fm.join_source
ORDER BY d.user_id;
-- 预期结果：30 行，每行 device_count=2, role=1, member_status=1
-- 3b. 确认没有遗漏（多设备无家庭组的用户应该为 0）
SELECT COUNT(*) as remaining_orphans
FROM (
SELECT d.user_id
FROM user_device d
LEFT JOIN user_family_member fm ON fm.user_id = d.user_id AND fm.status = 1
WHERE d.enabled = 1 AND fm.id IS NULL
GROUP BY d.user_id
HAVING COUNT(*) > 1
) orphans;
-- 预期结果：0

283
scripts/fix_split_multi_device_users.sql
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@ -1,283 +0,0 @@
-- ============================================================
-- 修复脚本：将同一 user_id 下的多设备拆分为独立用户 + 家庭组
--
-- 问题：代码模型要求 每个设备 = 独立用户，多设备通过家庭组关联
-- 但旧数据中同一 user_id 下挂了多个 user_device
--
-- 修复策略：
-- 1. 每个用户保留第一个设备（最早创建的），作为 family owner
-- 2. 其余设备各创建一个新 user，作为 family member
-- 3. 建立家庭组关系
--
-- ⚠️ 执行前请先备份！
-- ============================================================
-- ============================================================
-- Step 0: 诊断 - 查看受影响的用户和设备
-- ============================================================
SELECT
d.user_id,
COUNT(*) as device_count,
GROUP_CONCAT(d.id ORDER BY d.created_at ASC) as device_ids,
GROUP_CONCAT(d.identifier ORDER BY d.created_at ASC SEPARATOR ' | ') as identifiers
FROM user_device d
WHERE d.enabled = 1
GROUP BY d.user_id
HAVING device_count > 1
ORDER BY d.user_id;
-- ============================================================
-- Step 1: 创建临时表，标记需要拆分的设备
-- 每个用户保留最早的设备，其余标记为需要拆分
-- ============================================================
DROP TEMPORARY TABLE IF EXISTS tmp_devices_to_split;
CREATE TEMPORARY TABLE tmp_devices_to_split AS
SELECT
d.id as device_id,
d.user_id as original_user_id,
d.identifier,
d.ip,
d.user_agent,
d.created_at as device_created_at,
ROW_NUMBER() OVER (PARTITION BY d.user_id ORDER BY d.created_at ASC) as rn
FROM user_device d
WHERE d.enabled = 1
AND d.user_id IN (
SELECT user_id
FROM user_device
WHERE enabled = 1
GROUP BY user_id
HAVING COUNT(*) > 1
);
-- 确认：rn=1 的保留在原用户，rn>1 的需要创建新用户
SELECT * FROM tmp_devices_to_split ORDER BY original_user_id, rn;
-- ============================================================
-- Step 2: 为 rn>1 的设备创建新用户
-- 复制原用户的基本配置，生成新的 refer_code
-- ============================================================
-- 先看需要创建多少个新用户
SELECT COUNT(*) as new_users_needed FROM tmp_devices_to_split WHERE rn > 1;
-- 创建新用户（从原用户复制基本信息）
INSERT INTO `user` (
password, algo, salt, avatar, balance,
refer_code, referer_id, commission,
referral_percentage, only_first_purchase, gift_amount,
enable, is_admin,
enable_balance_notify, enable_login_notify,
enable_subscribe_notify, enable_trade_notify,
rules, member_status, remark,
created_at, updated_at
)
SELECT
u.password, u.algo, u.salt, '', 0,
'', -- refer_code 后面更新
u.referer_id, 0,
u.referral_percentage, u.only_first_purchase, 0,
u.enable, 0, -- is_admin = false
0, 0, 0, 0, -- 通知全关
'', '', CONCAT('split_from_user_', u.id),
NOW(), NOW()
FROM tmp_devices_to_split t
JOIN `user` u ON u.id = t.original_user_id
WHERE t.rn > 1;
-- ============================================================
-- Step 3: 映射新用户 ID 到设备
-- 因为 MySQL 不支持 INSERT ... RETURNING，需要通过 remark 字段找到新创建的用户
-- ============================================================
DROP TEMPORARY TABLE IF EXISTS tmp_new_user_mapping;
CREATE TEMPORARY TABLE tmp_new_user_mapping AS
SELECT
u.id as new_user_id,
CAST(SUBSTRING(u.remark, LENGTH('split_from_user_') + 1) AS UNSIGNED) as original_user_id,
u.created_at
FROM `user` u
WHERE u.remark LIKE 'split_from_user_%'
AND u.deleted_at IS NULL
ORDER BY u.id ASC;
-- 验证映射关系
SELECT * FROM tmp_new_user_mapping;
-- 将新用户与待拆分设备匹配（按原用户分组内的顺序）
DROP TEMPORARY TABLE IF EXISTS tmp_device_user_mapping;
CREATE TEMPORARY TABLE tmp_device_user_mapping AS
SELECT
t.device_id,
t.original_user_id,
t.identifier,
m.new_user_id
FROM (
SELECT *, ROW_NUMBER() OVER (PARTITION BY original_user_id ORDER BY device_id ASC) as split_seq
FROM tmp_devices_to_split
WHERE rn > 1
) t
JOIN (
SELECT *, ROW_NUMBER() OVER (PARTITION BY original_user_id ORDER BY new_user_id ASC) as split_seq
FROM tmp_new_user_mapping
) m ON t.original_user_id = m.original_user_id AND t.split_seq = m.split_seq;
-- 确认映射
SELECT * FROM tmp_device_user_mapping;
-- ============================================================
-- Step 4: 更新设备的 user_id 指向新用户
-- ============================================================
UPDATE user_device d
JOIN tmp_device_user_mapping m ON d.id = m.device_id
SET d.user_id = m.new_user_id;
-- ============================================================
-- Step 5: 为新用户创建 device auth_method
-- ============================================================
INSERT INTO user_auth_methods (user_id, auth_type, auth_identifier, verified, created_at, updated_at)
SELECT
m.new_user_id,
'device',
m.identifier,
1,
NOW(),
NOW()
FROM tmp_device_user_mapping m;
-- ============================================================
-- Step 6: 更新新用户的 refer_code
-- 用 CONCAT('u', CONV(new_user_id + UNIX_TIMESTAMP(), 10, 36)) 生成简单唯一码
-- （Go 代码用 Base62，SQL 里用 Base36 近似，长度足够唯一）
-- ============================================================
UPDATE `user` u
JOIN tmp_new_user_mapping m ON u.id = m.new_user_id
SET u.refer_code = CONCAT('u', LOWER(CONV(u.id + UNIX_TIMESTAMP(NOW()), 10, 36)));
-- 清理 remark 标记
UPDATE `user` u
JOIN tmp_new_user_mapping m ON u.id = m.new_user_id
SET u.remark = '';
-- ============================================================
-- Step 7: 创建/确保家庭组（原用户为 owner）
-- 先处理已有家庭组的情况，再处理没有的
-- ============================================================
-- 7a. 为没有 active 家庭组的原用户创建家庭组
INSERT INTO user_family (owner_user_id, max_members, status, created_at, updated_at)
SELECT DISTINCT
t.original_user_id,
2,
1, -- active
NOW(),
NOW()
FROM tmp_device_user_mapping t
LEFT JOIN user_family f ON f.owner_user_id = t.original_user_id
AND f.status = 1 AND f.deleted_at IS NULL
WHERE f.id IS NULL;
-- 7b. 确保原用户在家庭组中有 owner 成员记录
INSERT INTO user_family_member (family_id, user_id, role, status, join_source, joined_at, created_at, updated_at)
SELECT
f.id,
f.owner_user_id,
1, -- role = owner
1, -- status = active
'data_fix_split',
NOW(),
NOW(),
NOW()
FROM user_family f
WHERE f.owner_user_id IN (SELECT DISTINCT original_user_id FROM tmp_device_user_mapping)
AND f.status = 1
AND f.deleted_at IS NULL
AND NOT EXISTS (
SELECT 1 FROM user_family_member fm
WHERE fm.family_id = f.id
AND fm.user_id = f.owner_user_id
AND fm.status = 1
AND fm.deleted_at IS NULL
);
-- 7c. 将新用户加入家庭组作为 member
INSERT INTO user_family_member (family_id, user_id, role, status, join_source, joined_at, created_at, updated_at)
SELECT
f.id,
m.new_user_id,
2, -- role = member
1, -- status = active
'data_fix_split',
NOW(),
NOW(),
NOW()
FROM tmp_device_user_mapping m
JOIN user_family f ON f.owner_user_id = m.original_user_id
AND f.status = 1 AND f.deleted_at IS NULL;
-- 7d. 更新 max_members（如果原用户有 >2 个设备）
UPDATE user_family f
SET f.max_members = (
SELECT COUNT(*)
FROM user_family_member fm
WHERE fm.family_id = f.id AND fm.status = 1 AND fm.deleted_at IS NULL
)
WHERE f.owner_user_id IN (SELECT DISTINCT original_user_id FROM tmp_device_user_mapping)
AND f.status = 1 AND f.deleted_at IS NULL;
-- ============================================================
-- Step 8: 转移订阅（原用户的订阅保留，新用户不需要订阅）
-- 家庭成员共享 owner 的订阅，所以新用户不需要自己的订阅
-- 如果原用户已有订阅，新用户通过家庭组共享
-- ============================================================
-- （无需操作，代码中 familyBindingHelper.clearMemberSubscribes 会在 joinFamily 时清理）
-- 新用户是刚创建的，没有任何订阅记录，无需清理
-- ============================================================
-- Step 9: 验证修复结果
-- ============================================================
-- 9a. 确认没有用户拥有多个设备了
SELECT
d.user_id,
COUNT(*) as device_count,
GROUP_CONCAT(d.id ORDER BY d.id) as device_ids
FROM user_device d
WHERE d.enabled = 1
GROUP BY d.user_id
HAVING device_count > 1;
-- 预期结果：0 行
-- 9b. 确认家庭组关系正确
SELECT
f.id as family_id,
f.owner_user_id,
f.max_members,
f.status as family_status,
GROUP_CONCAT(CONCAT(fm.user_id, '(role=', fm.role, ')') ORDER BY fm.role) as members
FROM user_family f
JOIN user_family_member fm ON fm.family_id = f.id AND fm.status = 1 AND fm.deleted_at IS NULL
WHERE f.owner_user_id IN (SELECT DISTINCT original_user_id FROM tmp_device_user_mapping)
AND f.status = 1 AND f.deleted_at IS NULL
GROUP BY f.id, f.owner_user_id, f.max_members, f.status;
-- 9c. 确认新用户都有 device auth_method
SELECT
m.new_user_id,
m.original_user_id,
m.identifier,
am.id as auth_method_id,
d.id as device_id,
d.user_id as device_user_id
FROM tmp_device_user_mapping m
JOIN user_auth_methods am ON am.user_id = m.new_user_id AND am.auth_type = 'device'
JOIN user_device d ON d.id = m.device_id;
-- ============================================================
-- 清理临时表
-- ============================================================
DROP TEMPORARY TABLE IF EXISTS tmp_devices_to_split;
DROP TEMPORARY TABLE IF EXISTS tmp_new_user_mapping;
DROP TEMPORARY TABLE IF EXISTS tmp_device_user_mapping;

274
scripts/gen_test_excel.py
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@ -1,274 +0,0 @@
"""Generate ppanel-server test case Excel file."""
import os
from openpyxl import Workbook
from openpyxl.styles import (
Font, PatternFill, Alignment, Border, Side
)
from openpyxl.utils import get_column_letter
OUTPUT_PATH = os.path.join(os.path.dirname(__file__), "..", "tests", "ppanel_test_cases.xlsx")
# ── Color palette ──────────────────────────────────────────────────────────────
C_HEADER_BG = "1F4E79" # dark blue – header row
C_HEADER_FONT = "FFFFFF" # white
C_SHEET_TITLE = "2E75B6" # mid blue – sheet title row
C_P0_BG = "FFE2E2" # light red – P0
C_P1_BG = "FFF2CC" # light yellow – P1
C_P2_BG = "E2EFDA" # light green – P2
C_BORDER = "BFBFBF"
def thin_border():
s = Side(style="thin", color=C_BORDER)
return Border(left=s, right=s, top=s, bottom=s)
def header_fill(hex_color):
return PatternFill("solid", fgColor=hex_color)
def row_fill(hex_color):
return PatternFill("solid", fgColor=hex_color)
# ── Column definitions ─────────────────────────────────────────────────────────
COLUMNS = ["用例ID", "模块", "功能点", "前置条件", "测试步骤", "预期结果",
"实际结果", "测试状态", "优先级", "备注"]
COL_WIDTHS = [16, 16, 28, 32, 40, 40, 20, 12, 8, 20]
# ── Test data ──────────────────────────────────────────────────────────────────
SHEET1_ORDER = {
"name": "订单核心流程",
"rows": [
# id, 模块, 功能点, 前置, 步骤, 预期, 优先
("TC-ORDER-001","订单/预创建","正常预览订单价格","用户已登录，套餐存在且在售","传入有效 subscribe_id, quantity=1","返回 price/amount/discount 字段正确","P0"),
("TC-ORDER-002","订单/预创建","数量为0时自动修正为1","用户已登录","quantity=0","自动设为1，正常返回价格","P1"),
("TC-ORDER-003","订单/预创建","套餐购买数量限制（Quota）","用户已达到该套餐购买上限","再次预创建同套餐订单","返回 SubscribeQuotaLimit 错误","P0"),
("TC-ORDER-004","订单/预创建","新用户专属折扣（24h内注册）","用户注册在24h内，套餐有 new_user_only 折扣","预创建订单","折扣生效，amount < price","P0"),
("TC-ORDER-005","订单/预创建","老用户不享受新用户折扣","用户注册超过24h","预创建有 new_user_only 折扣的套餐","返回 SubscribeNewUserOnly 错误","P0"),
("TC-ORDER-006","订单/预创建","新用户已购过不重复享受新用户折扣","用户24h内注册，但已购买过该套餐","预创建同套餐","折扣不生效，按原价计算","P0"),
("TC-ORDER-007","订单/预创建","优惠券不存在","用户已登录","传入不存在的 coupon code","返回 CouponNotExist 错误","P1"),
("TC-ORDER-008","订单/预创建","优惠券已用完（count限制）","优惠券 used_count >= count","传入该优惠券","返回 CouponAlreadyUsed 错误","P1"),
("TC-ORDER-009","订单/预创建","优惠券个人使用次数超限","用户已使用该优惠券达 user_limit 次","再次使用","返回 CouponInsufficientUsage 错误","P1"),
("TC-ORDER-010","订单/预创建","优惠券不适用于该套餐","优惠券绑定了特定套餐，与当前套餐不符","传入该优惠券","返回 CouponNotApplicable 错误","P1"),
("TC-ORDER-011","订单/预创建","支付手续费计算","支付方式有手续费配置","传入 payment_id","feeAmount 正确，amount = 原金额 + 手续费","P1"),
("TC-ORDER-012","订单/预创建","礼品金额抵扣","用户 gift_amount > 0","预创建订单","deduction_amount 正确，amount 减去礼品金额","P1"),
("TC-ORDER-013","订单/预创建","礼品金额全额抵扣（amount归零）","用户 gift_amount >= 订单金额","预创建订单","amount=0，deduction_amount = 原订单金额","P1"),
("TC-ORDER-014","订单/购买","正常购买订阅","用户已登录，套餐在售有库存","发起购买请求","订单创建成功，返回 order_no","P0"),
("TC-ORDER-015","订单/购买","套餐库存为0不允许购买","套餐 inventory=0","发起购买","返回 SubscribeOutOfStock 错误","P0"),
("TC-ORDER-016","订单/购买","单订阅模式：已有 pending 订单自动关闭","SingleModel=true，用户已有 pending 订单","对同套餐再次购买","旧 pending 订单关闭，新订单创建成功","P0"),
("TC-ORDER-017","订单/购买","单订阅模式：自动路由为续费","SingleModel=true，用户已有有效订阅","购买相同套餐","订单类型=2（续费），parent_id 指向原订单","P0"),
("TC-ORDER-018","订单/购买","数量超过 MaxQuantity 限制","—","quantity > MaxQuantity","返回 InvalidParams 错误","P1"),
("TC-ORDER-019","订单/购买","金额超过 MaxOrderAmount","套餐单价极高","购买","返回 InvalidParams 错误","P1"),
("TC-ORDER-020","订单/购买","15分钟后自动关闭未支付订单","订单已创建，未支付","等待15分钟后触发队列","订单状态变为 Close(3)","P0"),
("TC-ORDER-021","订单/激活","订单激活（NewPurchase）","订单状态=已支付(2)，类型=1","触发激活队列","用户订阅创建，订单状态变为 Finished(5)","P0"),
("TC-ORDER-022","订单/激活","订单激活（Renewal）","订单状态=已支付，类型=2","触发激活","订阅到期时间延长","P0"),
("TC-ORDER-023","订单/激活","订单激活（ResetTraffic）","订单状态=已支付，类型=3","触发激活","用户流量重置","P0"),
("TC-ORDER-024","订单/激活","订单激活（Recharge）","订单状态=已支付，类型=4","触发激活","用户余额增加","P0"),
("TC-ORDER-025","订单/激活","订单激活（Redemption）","订单状态=已支付，类型=5","触发激活","兑换码激活成功","P0"),
("TC-ORDER-026","订单/激活","幂等性：已完成订单不重复处理","订单状态=Finished(5)","再次触发激活","直接跳过，不重复执行","P0"),
("TC-ORDER-027","订单/激活","非已支付状态订单不处理","订单状态=Pending(1) 或 Close(3)","触发激活","跳过，返回 ErrInvalidOrderStatus","P0"),
]
}
SHEET2_USER = {
"name": "用户模块",
"rows": [
("TC-USER-001","用户/注册","邮箱注册","邮箱未注册","提交有效邮箱+密码","用户创建成功，返回 token","P0"),
("TC-USER-002","用户/登录","邮箱密码登录","用户已注册","提交正确邮箱+密码","返回 JWT token，session 写入 Redis","P0"),
("TC-USER-003","用户/设备登录","AES-CBC 加密设备登录","配置 security_secret","Body 使用正确密钥加密","登录成功","P0"),
("TC-USER-004","用户/设备登录","错误密钥设备登录","—","Body 使用错误密钥加密","返回认证失败错误","P0"),
("TC-USER-005","用户/退出登录","解绑设备（退出家庭组）","用户在家庭组中","调用 unbind_device","用户从家庭组移除，device 记录不删除、不禁用","P0"),
("TC-USER-006","用户/注销账号","正常注销","用户已登录","调用 delete_account","账号软删除，auth_methods 软删除，Redis 缓存清理","P0"),
("TC-USER-007","用户/注销账号","家主注销 → 解散家庭","用户是家庭组家主","注销账号","家庭所有成员 status=removed，family status=disabled","P0"),
("TC-USER-008","用户/注销账号","成员注销 → 仅退出家庭","用户是家庭组成员","注销账号","仅该成员退出，家庭组继续存在","P0"),
("TC-USER-009","用户/注销","缓存清理（email key 残留问题）","用户已注销，email 缓存可能残留","注销后检查 Redis","cache:user:email:{email} 已删除","P0"),
("TC-USER-010","用户/邀请","绑定邀请码","用户未绑定过邀请码","提交有效邀请码","referer_id 写入，邀请关系建立","P1"),
("TC-USER-011","用户/邀请","重复绑定邀请码","用户已绑定邀请码","再次绑定","返回错误，不允许重复绑定","P1"),
("TC-USER-012","用户/佣金","首购返佣","用户通过邀请码注册，完成首次付款","订单激活","邀请人佣金增加","P1"),
("TC-USER-013","用户/佣金","only_first_purchase=true 仅首购返佣","配置仅首购","被邀请人第二次购买","不再发佣金","P1"),
("TC-USER-014","用户/佣金","赠送天数（双方）","邀请关系建立，被邀请人购买","订单激活","邀请人和被邀请人各获得赠送天数","P1"),
("TC-USER-015","用户/家庭组","踢出家庭成员","用户是家庭组家主","踢出某成员","该成员退出家庭组，设备记录不变","P1"),
("TC-USER-016","用户/订阅","查看订阅状态（含节点分组名和限速时间）","用户有有效订阅","查询订阅状态","返回节点分组名、限速起止时间","P1"),
]
}
SHEET3_SUB = {
"name": "订阅套餐",
"rows": [
("TC-SUB-001","套餐/列表","获取可用套餐列表","—","调用套餐列表接口","返回所有在售套餐","P1"),
("TC-SUB-002","套餐/列表","老版本客户端裁剪套餐列表","请求头含 X-App-Id（老版本标识）","调用套餐列表","每个套餐的 discount 列表去掉最后一项","P1"),
("TC-SUB-003","套餐/购买限制","Quota 限制（每用户购买上限）","套餐设置 quota=1","用户购买2次同套餐","第二次返回 SubscribeQuotaLimit","P0"),
("TC-SUB-004","套餐/折扣","数量折扣梯度","套餐配置多级数量折扣","购买不同数量","对应折扣率正确应用","P1"),
("TC-SUB-005","套餐/库存","库存充足时正常购买","inventory > 0","购买","成功，inventory -1","P1"),
("TC-SUB-006","套餐/库存","库存=-1（无限库存）","inventory=-1","多次购买","不减少库存，始终可购","P1"),
]
}
SHEET4_PAY = {
"name": "支付与优惠券",
"rows": [
("TC-PAY-001","支付/方式","获取可用支付方式列表","—","调用支付方式接口","返回当前配置的支付方式","P1"),
("TC-PAY-002","支付/手续费","固定手续费计算","支付方式配置固定手续费","下单","feeAmount = 配置值","P1"),
("TC-PAY-003","支付/手续费","百分比手续费计算","支付方式配置百分比手续费","下单","feeAmount = amount × 百分比","P1"),
("TC-PAY-004","支付/手续费","amount=0 时不计算手续费","礼品金额全额抵扣后 amount=0","下单","feeAmount=0","P1"),
("TC-CPN-001","优惠券/固定减免","固定金额优惠券","优惠券类型=固定，value=100","使用优惠券","订单减免100","P1"),
("TC-CPN-002","优惠券/百分比","百分比优惠券","优惠券类型=百分比，value=0.8","使用优惠券","订单金额×0.8","P1"),
("TC-CPN-003","优惠券/过期","过期优惠券不可用","优惠券 expire_at < now","使用","返回错误（CouponExpired）","P1"),
("TC-CPN-004","优惠券/套餐绑定","仅限指定套餐使用","优惠券绑定套餐A","用于套餐B","返回 CouponNotApplicable","P1"),
]
}
SHEET5_IAP = {
"name": "IAP苹果内购",
"rows": [
("TC-IAP-001","IAP/绑定","绑定苹果内购 transaction","苹果 transaction 有效","提交 transaction_id","订单创建并激活，订阅开通","P1"),
("TC-IAP-002","IAP/绑定","重复绑定同一 transaction","transaction 已绑定","再次提交","幂等处理，不重复创建订单","P1"),
("TC-IAP-003","IAP/单订阅模式","内购续费路由","SingleModel=true，用户已有订阅","提交续费 transaction","路由为续费类型订单","P1"),
("TC-IAP-004","IAP/对账","日对账任务","配置了 IAP 对账","触发日对账","检查并补处理漏掉的 transaction","P2"),
]
}
SHEET6_LOG = {
"name": "日志与缓存",
"rows": [
("TC-LOG-001","日志/佣金","佣金记录写入 system_logs","发生佣金发放","触发订单激活","type=33 的记录写入，content.type 为 331 或 332","P2"),
("TC-LOG-002","日志/礼品金额","礼品金额扣除记录","用户有 gift_amount，下单扣除","购买","GiftTypeReduce 记录写入 system_logs","P2"),
("TC-CACHE-001","缓存/用户","注销后 user email 缓存清理","用户已注销","检查 Redis","cache:user:email:{email} 已删除","P0"),
("TC-CACHE-002","缓存/订阅","订阅 token 缓存有效","用户有订阅","查询订阅","从 cache:user:subscribe:token:{token} 命中","P2"),
("TC-CACHE-003","缓存/签名","X-App-Id 签名验证","AppSecrets 已配置","发送带签名请求","验签通过，正常处理","P1"),
("TC-CACHE-004","缓存/签名","无 X-App-Id 跳过签名","—","发送无签名请求","直接通过，不验签","P1"),
]
}
SHEET7_QUEUE = {
"name": "队列任务",
"rows": [
("TC-QUEUE-001","队列/订单关闭","超时自动关闭订单","未支付订单存在","等待15分钟","订单状态=Close","P0"),
("TC-QUEUE-002","队列/订阅检查","定期检查订阅到期","用户订阅即将到期","触发 checkSubscription","到期通知发送","P2"),
("TC-QUEUE-003","队列/流量统计","服务器流量统计写入","有流量数据上报","触发 trafficStat","流量数据正确写入 DB","P2"),
("TC-QUEUE-004","队列/邮件","批量发送邮件任务","已创建批量邮件任务","触发队列","邮件发送成功，任务状态更新","P2"),
("TC-QUEUE-005","队列/流量重置","定期重置用户流量","配置了流量重置周期","触发 resetTraffic","用户流量归零","P2"),
]
}
ALL_SHEETS = [SHEET1_ORDER, SHEET2_USER, SHEET3_SUB, SHEET4_PAY, SHEET5_IAP, SHEET6_LOG, SHEET7_QUEUE]
PRIORITY_FILL = {
"P0": row_fill(C_P0_BG),
"P1": row_fill(C_P1_BG),
"P2": row_fill(C_P2_BG),
}
def write_sheet(wb: Workbook, sheet_def: dict):
ws = wb.create_sheet(title=sheet_def["name"])
rows = sheet_def["rows"]
# ── Title row ──────────────────────────────────────────────────────────────
ws.merge_cells(start_row=1, start_column=1, end_row=1, end_column=len(COLUMNS))
title_cell = ws.cell(row=1, column=1, value=f"ppanel-server 测试用例 — {sheet_def['name']}")
title_cell.font = Font(name="微软雅黑", bold=True, size=13, color=C_HEADER_FONT)
title_cell.fill = header_fill(C_SHEET_TITLE)
title_cell.alignment = Alignment(horizontal="center", vertical="center")
ws.row_dimensions[1].height = 28
# ── Header row ─────────────────────────────────────────────────────────────
for col_idx, col_name in enumerate(COLUMNS, start=1):
cell = ws.cell(row=2, column=col_idx, value=col_name)
cell.font = Font(name="微软雅黑", bold=True, size=10, color=C_HEADER_FONT)
cell.fill = header_fill(C_HEADER_BG)
cell.alignment = Alignment(horizontal="center", vertical="center", wrap_text=True)
cell.border = thin_border()
ws.row_dimensions[2].height = 22
# ── Data rows ──────────────────────────────────────────────────────────────
for r_idx, row in enumerate(rows, start=3):
tc_id, module, feature, precond, steps, expected, priority = row
values = [tc_id, module, feature, precond, steps, expected, "", "", priority, ""]
fill = PRIORITY_FILL.get(priority, None)
for c_idx, val in enumerate(values, start=1):
cell = ws.cell(row=r_idx, column=c_idx, value=val)
cell.font = Font(name="微软雅黑", size=9)
cell.alignment = Alignment(horizontal="left", vertical="center", wrap_text=True)
cell.border = thin_border()
if fill:
cell.fill = fill
ws.row_dimensions[r_idx].height = 45
# ── Column widths ──────────────────────────────────────────────────────────
for col_idx, width in enumerate(COL_WIDTHS, start=1):
ws.column_dimensions[get_column_letter(col_idx)].width = width
# ── Freeze panes ──────────────────────────────────────────────────────────
ws.freeze_panes = "A3"
# ── Auto filter ───────────────────────────────────────────────────────────
ws.auto_filter.ref = f"A2:{get_column_letter(len(COLUMNS))}2"
def write_legend_sheet(wb: Workbook):
ws = wb.create_sheet(title="说明", index=0)
ws.column_dimensions["A"].width = 18
ws.column_dimensions["B"].width = 50
title = ws.cell(row=1, column=1, value="ppanel-server 测试用例说明")
ws.merge_cells("A1:B1")
title.font = Font(name="微软雅黑", bold=True, size=13, color=C_HEADER_FONT)
title.fill = header_fill(C_SHEET_TITLE)
title.alignment = Alignment(horizontal="center", vertical="center")
ws.row_dimensions[1].height = 28
legend_data = [
("项目", "说明"),
("测试框架", "ppanel-server — go-zero + Gin"),
("数据库", "本地 MySQL（真实），禁止 SQLite"),
("Redis", "本地 Redis 或 miniredis"),
("时间戳规范", "后端统一返回秒级 Unix()，前端 ×1000"),
("", ""),
("优先级", "含义"),
("P0（红色）", "核心业务，必须通过。订单/认证/缓存清理等"),
("P1（黄色）", "重要功能，强烈建议测试。折扣/优惠券/邀请等"),
("P2（绿色）", "辅助功能，建议测试。日志/队列/IAP 等"),
("", ""),
("测试状态", "填写规范"),
("Pass", "用例通过"),
("Fail", "用例失败，需记录实际结果"),
("Block", "用例被阻塞（依赖功能未就绪）"),
("Skip", "本轮跳过"),
("", ""),
("Sheet 说明", ""),
("Sheet1 订单核心流程", "27 条：预创建/购买/激活全流程"),
("Sheet2 用户模块", "16 条：注册/登录/注销/邀请/家庭组"),
("Sheet3 订阅套餐", "6 条：库存/折扣/限额"),
("Sheet4 支付与优惠券", "8 条：手续费/优惠券各类型"),
("Sheet5 IAP苹果内购", "4 条：内购/对账"),
("Sheet6 日志与缓存", "6 条：日志写入/缓存清理"),
("Sheet7 队列任务", "5 条：队列任务验证"),
]
for r_idx, (key, val) in enumerate(legend_data, start=2):
c1 = ws.cell(row=r_idx, column=1, value=key)
c2 = ws.cell(row=r_idx, column=2, value=val)
for c in (c1, c2):
c.font = Font(name="微软雅黑", size=9)
c.alignment = Alignment(vertical="center", wrap_text=True)
c.border = thin_border()
if key in ("项目", "优先级", "测试状态", "Sheet 说明"):
for c in (c1, c2):
c.font = Font(name="微软雅黑", bold=True, size=9, color=C_HEADER_FONT)
c.fill = header_fill(C_HEADER_BG)
ws.row_dimensions[r_idx].height = 18
def main():
os.makedirs(os.path.dirname(os.path.abspath(OUTPUT_PATH)), exist_ok=True)
wb = Workbook()
wb.remove(wb.active) # remove default sheet
write_legend_sheet(wb)
for sheet_def in ALL_SHEETS:
write_sheet(wb, sheet_def)
wb.save(OUTPUT_PATH)
print(f"Excel saved: {os.path.abspath(OUTPUT_PATH)}")
if __name__ == "__main__":
main()

204
scripts/merge_duplicate_subscriptions.go
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@ -1,204 +0,0 @@
package main
import (
"database/sql"
"encoding/json"
"flag"
"fmt"
"log"
"os"
"strings"
"time"
_ "github.com/go-sql-driver/mysql"
)
type duplicateGroup struct {
OwnerUserID int64 `json:"owner_user_id"`
Count int64 `json:"count"`
}
type subscriptionRow struct {
ID int64 `json:"id"`
UserID int64 `json:"user_id"`
OrderID int64 `json:"order_id"`
SubscribeID int64 `json:"subscribe_id"`
ExpireTime time.Time `json:"expire_time"`
Traffic int64 `json:"traffic"`
Download int64 `json:"download"`
Upload int64 `json:"upload"`
ExpiredDownload int64 `json:"expired_download"`
ExpiredUpload int64 `json:"expired_upload"`
Status uint8 `json:"status"`
UpdatedAt time.Time `json:"updated_at"`
}
type mergePlan struct {
OwnerUserID int64 `json:"owner_user_id"`
Keep subscriptionRow `json:"keep"`
Merge []subscriptionRow `json:"merge"`
}
func main() {
dsn := flag.String("dsn", os.Getenv("PPANEL_MYSQL_DSN"), "MySQL DSN; defaults to PPANEL_MYSQL_DSN")
execute := flag.Bool("execute", false, "apply changes; default is dry-run")
flag.Parse()
if strings.TrimSpace(*dsn) == "" {
log.Fatal("missing DSN: pass -dsn or set PPANEL_MYSQL_DSN")
}
db, err := sql.Open("mysql", *dsn)
if err != nil {
log.Fatal(err)
}
defer db.Close()
groups, err := findDuplicateGroups(db)
if err != nil {
log.Fatal(err)
}
plans := make([]mergePlan, 0, len(groups))
for _, group := range groups {
plan, err := buildPlan(db, group.OwnerUserID)
if err != nil {
log.Fatal(err)
}
if len(plan.Merge) > 0 {
plans = append(plans, plan)
}
}
enc := json.NewEncoder(os.Stdout)
enc.SetIndent("", " ")
if err := enc.Encode(plans); err != nil {
log.Fatal(err)
}
if !*execute {
fmt.Fprintf(os.Stderr, "dry-run only: %d duplicate owner groups found\n", len(plans))
return
}
for _, plan := range plans {
if err := applyPlan(db, plan); err != nil {
log.Fatal(err)
}
}
fmt.Fprintf(os.Stderr, "merged %d duplicate owner groups\n", len(plans))
}
func findDuplicateGroups(db *sql.DB) ([]duplicateGroup, error) {
rows, err := db.Query(`
SELECT owner_user_id, COUNT(1) AS cnt
FROM (
SELECT us.id,
COALESCE(uf.owner_user_id, us.user_id) AS owner_user_id
FROM user_subscribe us
LEFT JOIN user_family_member ufm
ON ufm.user_id = us.user_id AND ufm.deleted_at IS NULL AND ufm.status = 1
LEFT JOIN user_family uf
ON uf.id = ufm.family_id AND uf.deleted_at IS NULL AND uf.status = 1
WHERE us.token <> ''
AND us.status IN (0, 1, 2, 3, 4)
) scoped
GROUP BY owner_user_id
HAVING COUNT(1) > 1
ORDER BY owner_user_id`)
if err != nil {
return nil, err
}
defer rows.Close()
var groups []duplicateGroup
for rows.Next() {
var g duplicateGroup
if err := rows.Scan(&g.OwnerUserID, &g.Count); err != nil {
return nil, err
}
groups = append(groups, g)
}
return groups, rows.Err()
}
func buildPlan(db *sql.DB, ownerUserID int64) (mergePlan, error) {
rows, err := db.Query(`
SELECT us.id, us.user_id, us.order_id, us.subscribe_id, us.expire_time, us.traffic,
us.download, us.upload, us.expired_download, us.expired_upload, us.status, us.updated_at
FROM user_subscribe us
LEFT JOIN user_family_member ufm
ON ufm.user_id = us.user_id AND ufm.deleted_at IS NULL AND ufm.status = 1
LEFT JOIN user_family uf
ON uf.id = ufm.family_id AND uf.deleted_at IS NULL AND uf.status = 1
WHERE COALESCE(uf.owner_user_id, us.user_id) = ?
AND us.token <> ''
AND us.status IN (0, 1, 2, 3, 4)
ORDER BY us.expire_time DESC, us.updated_at DESC, us.id DESC`, ownerUserID)
if err != nil {
return mergePlan{}, err
}
defer rows.Close()
var all []subscriptionRow
for rows.Next() {
var r subscriptionRow
if err := rows.Scan(&r.ID, &r.UserID, &r.OrderID, &r.SubscribeID, &r.ExpireTime, &r.Traffic, &r.Download, &r.Upload, &r.ExpiredDownload, &r.ExpiredUpload, &r.Status, &r.UpdatedAt); err != nil {
return mergePlan{}, err
}
all = append(all, r)
}
if err := rows.Err(); err != nil {
return mergePlan{}, err
}
if len(all) == 0 {
return mergePlan{OwnerUserID: ownerUserID}, nil
}
keep := all[0]
for _, r := range all[1:] {
keep.Download += r.Download
keep.Upload += r.Upload
keep.ExpiredDownload += r.ExpiredDownload
keep.ExpiredUpload += r.ExpiredUpload
if r.Traffic > keep.Traffic {
keep.Traffic = r.Traffic
}
}
for _, r := range all {
if r.UpdatedAt.After(keep.UpdatedAt) {
keep.SubscribeID = r.SubscribeID
}
}
return mergePlan{OwnerUserID: ownerUserID, Keep: keep, Merge: all[1:]}, nil
}
func applyPlan(db *sql.DB, plan mergePlan) error {
tx, err := db.Begin()
if err != nil {
return err
}
defer tx.Rollback()
if _, err = tx.Exec(`
UPDATE user_subscribe
SET user_id = ?, subscribe_id = ?, traffic = ?, download = ?, upload = ?,
expired_download = ?, expired_upload = ?, status = 1, note = CONCAT(COALESCE(note, ''), ' [merged duplicate subscriptions]')
WHERE id = ?`,
plan.OwnerUserID, plan.Keep.SubscribeID, plan.Keep.Traffic, plan.Keep.Download, plan.Keep.Upload,
plan.Keep.ExpiredDownload, plan.Keep.ExpiredUpload, plan.Keep.ID); err != nil {
return err
}
for _, r := range plan.Merge {
if _, err = tx.Exec(`
UPDATE user_subscribe
SET status = 5, note = CONCAT(COALESCE(note, ''), ' [merged into subscription #', ?, ']')
WHERE id = ?`, plan.Keep.ID, r.ID); err != nil {
return err
}
}
return tx.Commit()
}

875
scripts/migrate_paid_users.go
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@ -1,875 +0,0 @@
package main
import (
"flag"
"fmt"
"log"
"os"
"time"
"gorm.io/driver/mysql"
"gorm.io/gorm"
"gorm.io/gorm/logger"
)
// ── 模型定义（与项目一致） ──
type User struct {
Id int64 `gorm:"primaryKey"`
Password string `gorm:"type:varchar(255)"`
Algo string `gorm:"type:varchar(255)"`
Salt string `gorm:"type:varchar(255)"`
Avatar string `gorm:"type:varchar(255)"`
Balance int64 `gorm:"type:int"`
ReferCode string `gorm:"type:varchar(255)"`
RefererId int64 `gorm:"type:bigint"`
Commission int64 `gorm:"type:int"`
ReferralPercentage int64 `gorm:"type:int"`
OnlyFirstPurchase *bool `gorm:"type:tinyint(1)"`
GiftAmount int64 `gorm:"type:int"`
Enable *bool `gorm:"type:tinyint(1)"`
IsAdmin *bool `gorm:"type:tinyint(1)"`
EnableBalanceNotify *bool `gorm:"type:tinyint(1)"`
EnableLoginNotify *bool `gorm:"type:tinyint(1)"`
EnableSubscribeNotify *bool `gorm:"type:tinyint(1)"`
EnableTradeNotify *bool `gorm:"type:tinyint(1)"`
LastLoginTime *time.Time
MemberStatus string `gorm:"type:varchar(255)"`
Remark string `gorm:"type:text"`
CreatedAt time.Time
UpdatedAt time.Time
DeletedAt gorm.DeletedAt `gorm:"index"`
}
func (User) TableName() string { return "user" }
type AuthMethod struct {
Id int64 `gorm:"primaryKey"`
UserId int64 `gorm:"type:bigint"`
AuthType string `gorm:"type:varchar(50)"`
AuthIdentifier string `gorm:"type:varchar(255)"`
Verified *bool `gorm:"type:tinyint(1)"`
CreatedAt time.Time
UpdatedAt time.Time
}
func (AuthMethod) TableName() string { return "user_auth_methods" }
type Device struct {
Id int64 `gorm:"primaryKey"`
Ip string `gorm:"type:varchar(255)"`
UserId int64 `gorm:"type:bigint"`
UserAgent string `gorm:"type:text"`
Identifier string `gorm:"type:varchar(255)"`
ShortCode string `gorm:"type:varchar(50)"`
Online *bool `gorm:"type:tinyint(1)"`
Enabled *bool `gorm:"type:tinyint(1)"`
CreatedAt time.Time
UpdatedAt time.Time
}
func (Device) TableName() string { return "user_device" }
type UserSubscribe struct {
Id int64 `gorm:"primaryKey"`
UserId int64 `gorm:"type:bigint"`
OrderId int64 `gorm:"type:bigint"`
SubscribeId int64 `gorm:"type:bigint"`
StartTime time.Time `gorm:"type:datetime(3)"`
ExpireTime *time.Time `gorm:"type:datetime(3)"`
FinishedAt *time.Time `gorm:"type:datetime"`
Traffic int64 `gorm:"type:bigint"`
Download int64 `gorm:"type:bigint"`
Upload int64 `gorm:"type:bigint"`
Token string `gorm:"type:varchar(255)"`
UUID string `gorm:"type:varchar(255)"`
Status uint8 `gorm:"type:tinyint(1)"`
Note string `gorm:"type:varchar(500)"`
CreatedAt time.Time
UpdatedAt time.Time
}
func (UserSubscribe) TableName() string { return "user_subscribe" }
type Order struct {
Id int64 `gorm:"primaryKey"`
ParentId int64 `gorm:"type:bigint"`
UserId int64 `gorm:"type:bigint"`
SubscriptionUserId int64 `gorm:"type:bigint"`
OrderNo string `gorm:"type:varchar(255)"`
Type uint8 `gorm:"type:tinyint(1)"`
Quantity int64 `gorm:"type:bigint"`
Price int64 `gorm:"type:int"`
Amount int64 `gorm:"type:int"`
GiftAmount int64 `gorm:"type:int"`
Discount int64 `gorm:"type:int"`
Coupon string `gorm:"type:varchar(255)"`
CouponDiscount int64 `gorm:"type:int"`
Commission int64 `gorm:"type:int"`
PaymentId int64 `gorm:"type:bigint"`
Method string `gorm:"type:varchar(255)"`
FeeAmount int64 `gorm:"type:int"`
TradeNo string `gorm:"type:varchar(255)"`
Status uint8 `gorm:"type:tinyint(1)"`
SubscribeId int64 `gorm:"type:bigint"`
SubscribeToken string `gorm:"type:varchar(255)"`
AppAccountToken string `gorm:"type:varchar(36)"`
IsNew bool `gorm:"type:tinyint(1)"`
CreatedAt time.Time
UpdatedAt time.Time
}
func (Order) TableName() string { return "order" }
type IAPTransaction struct {
Id int64 `gorm:"primaryKey"`
UserId int64 `gorm:"type:bigint"`
OriginalTransactionId string `gorm:"type:varchar(255)"`
TransactionId string `gorm:"type:varchar(255)"`
ProductId string `gorm:"type:varchar(255)"`
PurchaseAt *time.Time
RevocationAt *time.Time
JWSHash string `gorm:"type:varchar(255)"`
CreatedAt time.Time
UpdatedAt time.Time
}
func (IAPTransaction) TableName() string { return "apple_iap_transactions" }
type Subscribe struct {
Id int64 `gorm:"primaryKey"`
Name string `gorm:"type:varchar(255)"`
Language string `gorm:"type:varchar(255)"`
Description string `gorm:"type:text"`
UnitPrice int64 `gorm:"type:int"`
UnitTime string `gorm:"type:varchar(255)"`
Discount string `gorm:"type:text"`
Replacement int64 `gorm:"type:int"`
Inventory int64 `gorm:"type:int"`
Traffic int64 `gorm:"type:int"`
SpeedLimit int64 `gorm:"type:int"`
DeviceLimit int64 `gorm:"type:int"`
Quota int64 `gorm:"type:int"`
NewUserOnly *bool `gorm:"type:tinyint(1)"`
Nodes string `gorm:"type:varchar(255)"`
NodeTags string `gorm:"type:varchar(255)"`
Show *bool `gorm:"type:tinyint(1)"`
Sell *bool `gorm:"type:tinyint(1)"`
Sort int64 `gorm:"type:int"`
DeductionRatio int64 `gorm:"type:int"`
AllowDeduction *bool `gorm:"type:tinyint(1)"`
ResetCycle int64 `gorm:"type:int"`
RenewalReset *bool `gorm:"type:tinyint(1)"`
ShowOriginalPrice bool `gorm:"type:tinyint(1)"`
CreatedAt time.Time
UpdatedAt time.Time
}
func (Subscribe) TableName() string { return "subscribe" }
type Payment struct {
Id int64 `gorm:"primaryKey"`
Name string `gorm:"type:varchar(100)"`
Platform string `gorm:"type:varchar(100)"`
Icon string `gorm:"type:varchar(255)"`
Domain string `gorm:"type:varchar(255)"`
Config string `gorm:"type:text"`
Description string `gorm:"type:text"`
FeeMode uint `gorm:"type:tinyint(1)"`
FeePercent int64 `gorm:"type:int"`
FeeAmount int64 `gorm:"type:int"`
Enable *bool `gorm:"type:tinyint(1)"`
Token string `gorm:"type:varchar(255)"`
}
func (Payment) TableName() string { return "payment" }
type UserFamily struct {
Id int64 `gorm:"primaryKey"`
OwnerUserId int64 `gorm:"uniqueIndex"`
MaxMembers int64 `gorm:"default:3"`
Status uint8 `gorm:"type:tinyint(1);default:1"`
CreatedAt time.Time
UpdatedAt time.Time
DeletedAt gorm.DeletedAt `gorm:"index"`
}
func (UserFamily) TableName() string { return "user_family" }
type UserFamilyMember struct {
Id int64 `gorm:"primaryKey"`
FamilyId int64
UserId int64 `gorm:"uniqueIndex"`
Role uint8 `gorm:"type:tinyint(1);default:2"`
Status uint8 `gorm:"type:tinyint(1);default:1"`
JoinSource string `gorm:"type:varchar(32)"`
JoinedAt time.Time
LeftAt *time.Time
CreatedAt time.Time
UpdatedAt time.Time
DeletedAt gorm.DeletedAt `gorm:"index"`
}
func (UserFamilyMember) TableName() string { return "user_family_member" }
// ── 主程序 ──
const (
defaultFamilyMaxSize = 3
orderStatusCompleted = 3
familyRoleOwner = 1
familyRoleMember = 2
batchSize = 100
)
func main() {
srcDSN := flag.String("src", "", "源数据库 DSN，格式: user:password@tcp(host:port)/dbname")
dstDSN := flag.String("dst", "", "目标数据库 DSN，格式: user:password@tcp(host:port)/dbname")
clean := flag.Bool("clean", false, "导入前清空目标库相关表")
dryRun := flag.Bool("dry-run", false, "仅分析不写入，打印统计信息")
flag.Parse()
if *srcDSN == "" || *dstDSN == "" {
fmt.Println("付费用户数据迁移工具")
fmt.Println()
fmt.Println("用法:")
fmt.Println(" go run scripts/migrate_paid_users.go \\")
fmt.Println(" -src 'root:rootpassword@tcp(127.0.0.1:3306)/ppanel?charset=utf8mb4&parseTime=True&loc=Local' \\")
fmt.Println(" -dst 'root:jpcV41ppanel@tcp(154.12.35.103:3306)/ppanel?charset=utf8mb4&parseTime=True&loc=Local' \\")
fmt.Println(" -clean")
fmt.Println()
fmt.Println("参数:")
fmt.Println(" -src 源数据库 DSN（旧备份库）")
fmt.Println(" -dst 目标数据库 DSN（新线上库）")
fmt.Println(" -clean 导入前清空目标库的用户/订单等表（保留表结构）")
fmt.Println(" -dry-run 仅分析不写入")
os.Exit(1)
}
gormCfg := &gorm.Config{
Logger: logger.Default.LogMode(logger.Warn),
}
// 连接源库
fmt.Println("=== 付费用户数据迁移 ===")
fmt.Println()
fmt.Print("[1/10] 连接源数据库... ")
srcDB, err := gorm.Open(mysql.Open(*srcDSN), gormCfg)
if err != nil {
log.Fatalf("源库连接失败: %v", err)
}
fmt.Println("OK")
// 连接目标库
fmt.Print("[2/10] 连接目标数据库... ")
dstDB, err := gorm.Open(mysql.Open(*dstDSN), gormCfg)
if err != nil {
log.Fatalf("目标库连接失败: %v", err)
}
fmt.Println("OK")
// ── Step 3: 查询付费用户 ID ──
fmt.Print("[3/10] 查询付费用户... ")
var paidIDs []int64
err = srcDB.Raw(`
SELECT DISTINCT t.uid FROM (
SELECT user_id AS uid FROM ` + "`order`" + ` WHERE status = ? AND user_id > 0
UNION
SELECT user_id AS uid FROM apple_iap_transactions WHERE user_id > 0
UNION
SELECT user_id AS uid FROM user_subscribe WHERE user_id > 0 AND (expire_time IS NULL OR expire_time > NOW())
) t
INNER JOIN user u ON u.id = t.uid
WHERE u.id NOT IN (
SELECT user_id FROM user_auth_methods WHERE auth_type = 'email' AND auth_identifier = 'devneeds52@gmail.com'
)
ORDER BY t.uid
`, orderStatusCompleted).Scan(&paidIDs).Error
if err != nil {
log.Fatalf("查询付费用户失败: %v", err)
}
fmt.Printf("%d 个付费用户\n", len(paidIDs))
if len(paidIDs) == 0 {
fmt.Println("没有找到付费用户，退出")
return
}
// ── Step 4: 读取源库数据 ──
fmt.Print("[4/10] 读取源库数据... ")
var (
users []User
auths []AuthMethod
devices []Device
orders []Order
subscribes []UserSubscribe
iaps []IAPTransaction
// 全量表
subPlans []Subscribe
payments []Payment
)
srcDB.Where("id IN ?", paidIDs).Find(&users)
srcDB.Where("user_id IN ?", paidIDs).Find(&auths)
srcDB.Where("user_id IN ?", paidIDs).Find(&devices)
srcDB.Where("user_id IN ? AND status = ?", paidIDs, orderStatusCompleted).Find(&orders)
srcDB.Where("user_id IN ?", paidIDs).Find(&subscribes)
srcDB.Where("user_id IN ?", paidIDs).Find(&iaps)
srcDB.Find(&subPlans)
srcDB.Find(&payments)
// ── 处理多订阅：如果用户有多个订阅，仅保留未过期的 ──
nowTime := time.Now()
subByUser := make(map[int64][]UserSubscribe)
for _, s := range subscribes {
subByUser[s.UserId] = append(subByUser[s.UserId], s)
}
var validSubscribes []UserSubscribe
for _, subs := range subByUser {
if len(subs) <= 1 {
// 单个订阅直接保留
validSubscribes = append(validSubscribes, subs...)
continue
}
var unexpired []UserSubscribe
var latest *UserSubscribe
for i := range subs {
s := subs[i]
// 如果没有过期时间，或者过期时间在当前时间之后
if s.ExpireTime == nil || s.ExpireTime.After(nowTime) {
unexpired = append(unexpired, s)
}
// 记录到期时间最晚的一个，以防全部都过期了
if latest == nil {
latest = &s
} else if latest.ExpireTime != nil && s.ExpireTime != nil && s.ExpireTime.After(*latest.ExpireTime) {
latest = &s
} else if latest.ExpireTime != nil && s.ExpireTime == nil {
latest = &s
}
}
if len(unexpired) > 0 {
// 存在未过期的订阅，仅保留所有未过期的
validSubscribes = append(validSubscribes, unexpired...)
} else if latest != nil {
// 如果全部过期，仅保留到期时间最晚的那一个
validSubscribes = append(validSubscribes, *latest)
}
}
subscribes = validSubscribes
fmt.Println("OK")
fmt.Println()
fmt.Println(" 数据统计:")
fmt.Printf(" user: %d\n", len(users))
fmt.Printf(" user_auth_methods: %d\n", len(auths))
fmt.Printf(" user_device: %d\n", len(devices))
fmt.Printf(" order: %d\n", len(orders))
fmt.Printf(" user_subscribe: %d\n", len(subscribes))
fmt.Printf(" apple_iap: %d\n", len(iaps))
fmt.Printf(" subscribe(全量): %d\n", len(subPlans))
fmt.Printf(" payment(全量): %d\n", len(payments))
// ── 识别多设备用户 ──
deviceByUser := make(map[int64][]Device)
for _, d := range devices {
deviceByUser[d.UserId] = append(deviceByUser[d.UserId], d)
}
authByUserDevice := make(map[string]*AuthMethod) // key: "userId:identifier"
for i := range auths {
a := &auths[i]
if a.AuthType == "device" {
key := fmt.Sprintf("%d:%s", a.UserId, a.AuthIdentifier)
authByUserDevice[key] = a
}
}
type splitInfo struct {
OwnerUID int64
Device Device
AuthMethod *AuthMethod
}
var splits []splitInfo
for uid, devs := range deviceByUser {
if len(devs) <= 1 {
continue
}
// 按 ID 排序：最小的保留，其余拆分
minID := devs[0].Id
for _, d := range devs[1:] {
if d.Id < minID {
minID = d.Id
}
}
for _, d := range devs {
if d.Id == minID {
continue
}
key := fmt.Sprintf("%d:%s", uid, d.Identifier)
auth := authByUserDevice[key]
splits = append(splits, splitInfo{
OwnerUID: uid,
Device: d,
AuthMethod: auth,
})
}
}
fmt.Printf("\n 多设备拆分: %d 个设备 → 独立用户\n", len(splits))
fmt.Println()
// ── ID 重建：将所有记录主键从 1 开始连续重赋值，并同步更新所有外键 ──
fmt.Println(" 重建 ID...")
// 各表 old→new 映射（仅对有外键引用的表建立映射）
userIDMap := make(map[int64]int64, len(users))
orderIDMap := make(map[int64]int64, len(orders))
subPlanIDMap := make(map[int64]int64, len(subPlans))
paymentIDMap := make(map[int64]int64, len(payments))
deviceIDMap := make(map[int64]int64, len(devices))
// 1. 重建 user ID（从 1 开始连续）
for i := range users {
newID := int64(i + 1)
userIDMap[users[i].Id] = newID
users[i].Id = newID
}
// 2. 重建 subscribe 配置表 ID
for i := range subPlans {
newID := int64(i + 1)
subPlanIDMap[subPlans[i].Id] = newID
subPlans[i].Id = newID
}
// 3. 重建 payment 配置表 ID
for i := range payments {
newID := int64(i + 1)
paymentIDMap[payments[i].Id] = newID
payments[i].Id = newID
}
// 4. 重建 auth_methods ID + 更新 user_id 外键
for i := range auths {
auths[i].Id = int64(i + 1)
if v, ok := userIDMap[auths[i].UserId]; ok {
auths[i].UserId = v
}
}
// 5. 重建 device ID + 更新 user_id 外键
for i := range devices {
newID := int64(i + 1)
deviceIDMap[devices[i].Id] = newID
devices[i].Id = newID
if v, ok := userIDMap[devices[i].UserId]; ok {
devices[i].UserId = v
}
}
// 6. 重建 order ID + 外键（user_id / subscription_user_id / payment_id / subscribe_id）
for i := range orders {
newID := int64(i + 1)
orderIDMap[orders[i].Id] = newID
orders[i].Id = newID
if v, ok := userIDMap[orders[i].UserId]; ok {
orders[i].UserId = v
}
if orders[i].SubscriptionUserId > 0 {
if v, ok := userIDMap[orders[i].SubscriptionUserId]; ok {
orders[i].SubscriptionUserId = v
}
}
if orders[i].PaymentId > 0 {
if v, ok := paymentIDMap[orders[i].PaymentId]; ok {
orders[i].PaymentId = v
}
}
if orders[i].SubscribeId > 0 {
if v, ok := subPlanIDMap[orders[i].SubscribeId]; ok {
orders[i].SubscribeId = v
}
}
}
// 二次处理 order.ParentId（父子订单指向同表，需在 orderIDMap 完整建立后再处理）
for i := range orders {
if orders[i].ParentId > 0 {
if v, ok := orderIDMap[orders[i].ParentId]; ok {
orders[i].ParentId = v
}
}
}
// 7. 重建 user_subscribe ID + 外键
for i := range subscribes {
subscribes[i].Id = int64(i + 1)
if v, ok := userIDMap[subscribes[i].UserId]; ok {
subscribes[i].UserId = v
}
if subscribes[i].OrderId > 0 {
if v, ok := orderIDMap[subscribes[i].OrderId]; ok {
subscribes[i].OrderId = v
}
}
if subscribes[i].SubscribeId > 0 {
if v, ok := subPlanIDMap[subscribes[i].SubscribeId]; ok {
subscribes[i].SubscribeId = v
}
}
}
// 8. 重建 iap ID + 更新 user_id 外键
for i := range iaps {
iaps[i].Id = int64(i + 1)
if v, ok := userIDMap[iaps[i].UserId]; ok {
iaps[i].UserId = v
}
}
// 9. 更新 paidIDs（Step 8 家庭组创建使用新 user ID）
for i, uid := range paidIDs {
if v, ok := userIDMap[uid]; ok {
paidIDs[i] = v
}
}
// 10. 更新 splits 中的 OwnerUID 和 Device 副本
// Device 是值拷贝，需通过 deviceIDMap 单独更新；AuthMethod 是指针，已随 auths[i] 同步
for i := range splits {
if v, ok := userIDMap[splits[i].OwnerUID]; ok {
splits[i].OwnerUID = v
}
if v, ok := deviceIDMap[splits[i].Device.Id]; ok {
splits[i].Device.Id = v
}
if v, ok := userIDMap[splits[i].Device.UserId]; ok {
splits[i].Device.UserId = v
}
}
fmt.Printf(" OK — user:%d auth:%d device:%d order:%d subscribe:%d iap:%d\n",
len(userIDMap), len(auths), len(deviceIDMap), len(orderIDMap), len(subscribes), len(iaps))
// ── 注入默认管理员用户（devneeds52@gmail.com） ──
// 该用户不涉及付费订单，ID 紧接在已迁移用户之后，避免冲突
{
defaultCreatedAt := time.Date(2025, 9, 30, 9, 33, 45, 780_000_000, time.UTC)
lastLogin := time.Date(2026, 3, 15, 17, 13, 45, 0, time.UTC)
defaultUID := int64(len(users) + 1)
defaultUser := User{
Id: defaultUID,
Password: "$pbkdf2-sha512$kyFSMS4eAnupW7bX$38953ce0e7ec8415c39603bdc3010050ddab2e433f0383222215bbec013450e3",
Algo: "default",
Salt: "default",
Avatar: "",
Balance: 0,
ReferCode: "uuEPXVjS",
Commission: 0,
ReferralPercentage: 0,
OnlyFirstPurchase: boolPtr(true),
GiftAmount: 0,
Enable: boolPtr(true),
IsAdmin: boolPtr(true),
EnableBalanceNotify: boolPtr(false),
EnableLoginNotify: boolPtr(false),
EnableSubscribeNotify: boolPtr(false),
EnableTradeNotify: boolPtr(false),
LastLoginTime: &lastLogin,
MemberStatus: "",
Remark: "",
CreatedAt: defaultCreatedAt,
UpdatedAt: time.Now(),
}
users = append(users, defaultUser)
defaultAuth := AuthMethod{
Id: int64(len(auths) + 1),
UserId: defaultUID,
AuthType: "email",
AuthIdentifier: "devneeds52@gmail.com",
Verified: boolPtr(true),
CreatedAt: defaultCreatedAt,
UpdatedAt: defaultCreatedAt,
}
auths = append(auths, defaultAuth)
fmt.Printf(" 注入管理员: uid=%d email=devneeds52@gmail.com\n", defaultUID)
}
fmt.Println()
if *dryRun {
fmt.Println("[DRY-RUN] 仅分析，不写入目标库")
return
}
// ── Step 5: 清空目标库（可选） ──
if *clean {
fmt.Print("[5/10] 清空目标库... ")
dstDB.Exec("SET FOREIGN_KEY_CHECKS = 0")
for _, tbl := range []string{
"user", "user_auth_methods", "user_device",
"`order`", "user_subscribe", "apple_iap_transactions",
"user_family", "user_family_member",
} {
dstDB.Exec(fmt.Sprintf("TRUNCATE TABLE %s", tbl))
}
dstDB.Exec("SET FOREIGN_KEY_CHECKS = 1")
fmt.Println("OK")
} else {
fmt.Println("[5/10] 跳过清空（未指定 -clean）")
}
// ── Step 6: 写入全量配置表 ──
fmt.Print("[6/10] 写入全量配置表... ")
if len(subPlans) > 0 {
dstDB.Exec("DELETE FROM subscribe") // 先清再插
for _, s := range subPlans {
dstDB.Create(&s)
}
}
if len(payments) > 0 {
dstDB.Exec("DELETE FROM payment")
for _, p := range payments {
dstDB.Create(&p)
}
}
fmt.Println("OK")
// ── Step 7: 写入付费用户数据（事务） ──
fmt.Print("[7/10] 写入付费用户数据... ")
err = dstDB.Transaction(func(tx *gorm.DB) error {
tx.Exec("SET FOREIGN_KEY_CHECKS = 0")
if err := tx.CreateInBatches(&users, batchSize).Error; err != nil {
return fmt.Errorf("写入 user 失败: %w", err)
}
if err := tx.CreateInBatches(&auths, batchSize).Error; err != nil {
return fmt.Errorf("写入 auth_methods 失败: %w", err)
}
if err := tx.CreateInBatches(&devices, batchSize).Error; err != nil {
return fmt.Errorf("写入 device 失败: %w", err)
}
if err := tx.CreateInBatches(&orders, batchSize).Error; err != nil {
return fmt.Errorf("写入 order 失败: %w", err)
}
if err := tx.CreateInBatches(&subscribes, batchSize).Error; err != nil {
return fmt.Errorf("写入 subscribe 失败: %w", err)
}
if err := tx.CreateInBatches(&iaps, batchSize).Error; err != nil {
return fmt.Errorf("写入 iap 失败: %w", err)
}
tx.Exec("SET FOREIGN_KEY_CHECKS = 1")
return nil
})
if err != nil {
log.Fatalf("写入失败: %v", err)
}
fmt.Println("OK")
// ── Step 8: 创建家庭组 ──
fmt.Print("[8/10] 创建家庭组... ")
now := time.Now()
familyCount := 0
// ── 为了配合基于新 UID 的条件检查，预先构建映射 ──
deviceCountByNewUID := make(map[int64]int)
for i := range devices {
deviceCountByNewUID[devices[i].UserId]++
}
hasEmailByNewUID := make(map[int64]bool)
for i := range auths {
if auths[i].AuthType == "email" {
hasEmailByNewUID[auths[i].UserId] = true
}
}
err = dstDB.Transaction(func(tx *gorm.DB) error {
for _, uid := range paidIDs {
// 只为多设备且有邮箱的用户创建家庭组
if deviceCountByNewUID[uid] <= 1 || !hasEmailByNewUID[uid] {
continue
}
family := UserFamily{
OwnerUserId: uid,
MaxMembers: defaultFamilyMaxSize,
Status: 1,
CreatedAt: now,
UpdatedAt: now,
}
if err := tx.Create(&family).Error; err != nil {
return fmt.Errorf("创建家庭组(uid=%d)失败: %w", uid, err)
}
member := UserFamilyMember{
FamilyId: family.Id,
UserId: uid,
Role: familyRoleOwner,
Status: 1,
JoinSource: "migration",
JoinedAt: now,
CreatedAt: now,
UpdatedAt: now,
}
if err := tx.Create(&member).Error; err != nil {
return fmt.Errorf("创建家主成员(uid=%d)失败: %w", uid, err)
}
familyCount++
}
return nil
})
if err != nil {
log.Fatalf("家庭组创建失败: %v", err)
}
fmt.Printf("%d 个家庭组\n", familyCount)
// ── Step 9: 多设备拆分 ──
fmt.Print("[9/10] 多设备拆分... ")
splitCount := 0
err = dstDB.Transaction(func(tx *gorm.DB) error {
for _, s := range splits {
// 1. 创建新用户
newUser := User{
Password: "",
Algo: "default",
Salt: "default",
Enable: boolPtr(true),
IsAdmin: boolPtr(false),
OnlyFirstPurchase: boolPtr(true),
EnableBalanceNotify: boolPtr(false),
EnableLoginNotify: boolPtr(false),
EnableSubscribeNotify: boolPtr(false),
EnableTradeNotify: boolPtr(false),
CreatedAt: s.Device.CreatedAt,
UpdatedAt: now,
}
if err := tx.Create(&newUser).Error; err != nil {
return fmt.Errorf("创建拆分用户失败(owner=%d, device=%d): %w", s.OwnerUID, s.Device.Id, err)
}
// 2. 转移设备到新用户
if err := tx.Model(&Device{}).Where("id = ?", s.Device.Id).
Update("user_id", newUser.Id).Error; err != nil {
return fmt.Errorf("转移设备失败: %w", err)
}
// 3. 转移 auth_method 到新用户
if s.AuthMethod != nil {
if err := tx.Model(&AuthMethod{}).Where("id = ?", s.AuthMethod.Id).
Update("user_id", newUser.Id).Error; err != nil {
return fmt.Errorf("转移 auth_method 失败: %w", err)
}
}
// 仅仅当原用户有邮箱时，才尝试将其加入家庭组（无邮箱的仅拆分为独立用户）
if hasEmailByNewUID[s.OwnerUID] {
// 4. 查找原用户的家庭组（如果不存在则创建，虽然理论上 Step 8 已经为多设备用户创建了）
var family UserFamily
if err := tx.Where("owner_user_id = ?", s.OwnerUID).First(&family).Error; err != nil {
if err == gorm.ErrRecordNotFound {
// 补救措施：为该用户创建一个家庭组
family = UserFamily{
OwnerUserId: s.OwnerUID,
MaxMembers: defaultFamilyMaxSize,
Status: 1,
CreatedAt: now,
UpdatedAt: now,
}
if err := tx.Create(&family).Error; err != nil {
return fmt.Errorf("创建家庭组补救失败(owner=%d): %w", s.OwnerUID, err)
}
// 创建家主成员
ownerMember := UserFamilyMember{
FamilyId: family.Id,
UserId: s.OwnerUID,
Role: familyRoleOwner,
Status: 1,
JoinSource: "migration_split_recovery",
JoinedAt: now,
CreatedAt: now,
UpdatedAt: now,
}
if err := tx.Create(&ownerMember).Error; err != nil {
return fmt.Errorf("创建家主成员补救失败(owner=%d): %w", s.OwnerUID, err)
}
familyCount++ // 更新计数器
} else {
return fmt.Errorf("查找家庭组失败(owner=%d): %w", s.OwnerUID, err)
}
}
// 5. 加入家庭组
member := UserFamilyMember{
FamilyId: family.Id,
UserId: newUser.Id,
Role: familyRoleMember,
Status: 1,
JoinSource: "migration_split",
JoinedAt: s.Device.CreatedAt,
CreatedAt: now,
UpdatedAt: now,
}
if err := tx.Create(&member).Error; err != nil {
return fmt.Errorf("添加家庭成员失败: %w", err)
}
}
splitCount++
}
return nil
})
if err != nil {
log.Fatalf("设备拆分失败: %v", err)
}
fmt.Printf("%d 个设备\n", splitCount)
// ── Step 10: 修复各表 AUTO_INCREMENT ──
// 确保迁移后新写入的记录不会触发主键冲突
fmt.Print("[10/10] 修复 AUTO_INCREMENT... ")
type autoIncTable struct {
table string // 表名（不含反引号）
quoted string // SQL 中使用的表名（含反引号）
}
autoIncTables := []autoIncTable{
{"user", "`user`"},
{"user_auth_methods", "`user_auth_methods`"},
{"user_device", "`user_device`"},
{"order", "`order`"},
{"user_subscribe", "`user_subscribe`"},
{"apple_iap_transactions", "`apple_iap_transactions`"},
{"user_family", "`user_family`"},
{"user_family_member", "`user_family_member`"},
}
for _, t := range autoIncTables {
var maxID int64
dstDB.Raw(fmt.Sprintf("SELECT COALESCE(MAX(id), 0) FROM %s", t.quoted)).Scan(&maxID)
nextID := maxID + 1
if err := dstDB.Exec(fmt.Sprintf("ALTER TABLE %s AUTO_INCREMENT = %d", t.quoted, nextID)).Error; err != nil {
log.Printf(" 警告: 修复 %s AUTO_INCREMENT 失败: %v", t.table, err)
} else {
fmt.Printf("\n %-30s MAX(id)=%-8d AUTO_INCREMENT→%d", t.table, maxID, nextID)
}
}
fmt.Println("\nOK")
// ── 结果 ──
fmt.Println()
fmt.Println("=== 迁移完成 ===")
fmt.Printf(" 用户: %d (原始) + %d (拆分) = %d\n", len(users), splitCount, len(users)+splitCount)
fmt.Printf(" 家庭组: %d\n", familyCount)
fmt.Printf(" 家庭成员: %d (家主) + %d (拆分) = %d\n", familyCount, splitCount, familyCount+splitCount)
fmt.Printf(" 订单: %d\n", len(orders))
fmt.Printf(" 订阅: %d\n", len(subscribes))
}
func boolPtr(b bool) *bool { return &b }

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scripts/output/paid_users_migration.sql
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scripts/replay_business_bugs.go
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@ -1,787 +0,0 @@
package main
import (
"context"
"encoding/json"
"flag"
"fmt"
"os"
"os/exec"
"strings"
"time"
"github.com/hibiken/asynq"
"github.com/perfect-panel/server/internal/config"
authlogic "github.com/perfect-panel/server/internal/logic/auth"
modelLog "github.com/perfect-panel/server/internal/model/log"
modelOrder "github.com/perfect-panel/server/internal/model/order"
modelSubscribe "github.com/perfect-panel/server/internal/model/subscribe"
modelUser "github.com/perfect-panel/server/internal/model/user"
"github.com/perfect-panel/server/internal/svc"
"github.com/perfect-panel/server/pkg/conf"
"github.com/perfect-panel/server/pkg/orm"
"github.com/perfect-panel/server/pkg/uuidx"
orderLogic "github.com/perfect-panel/server/queue/logic/order"
queueTypes "github.com/perfect-panel/server/queue/types"
"github.com/redis/go-redis/v9"
"gorm.io/gorm"
)
const marker = "codex-replay-business-bugs"
func main() {
var (
configPath = flag.String("config", "etc/ppanel.yaml", "ppanel config path for test server DB/Redis")
dsn = flag.String("dsn", "", "optional MySQL DSN override: user:pass@tcp(host:3306)/db?charset=utf8mb4&parseTime=true&loc=Asia%2FShanghai")
writeDB = flag.Bool("write-db", false, "create isolated test rows and execute activation replay against the configured test DB")
force = flag.Bool("force", false, "allow -write-db even when the config name does not clearly look like test/dev/staging")
keep = flag.Bool("keep", false, "keep replay rows for manual inspection")
cleanupOnly = flag.Bool("cleanup-only", false, "delete leftover replay rows by marker and exit")
skipCodeTests = flag.Bool("skip-code-tests", false, "skip go test checks")
)
flag.Parse()
ctx := context.Background()
started := time.Now()
fmt.Println("== replay business bug tests ==")
fmt.Printf("marker: %s\n", marker)
if !*skipCodeTests {
must(runCodeTests())
}
cfg := loadConfig(*configPath, *dsn)
runEmailTrialAssertions(cfg)
if *cleanupOnly {
env := mustNewReplayEnv(ctx, cfg)
env.cleanupByMarker(ctx)
return
}
if !*writeDB {
fmt.Println("\nDB replay skipped. Add -write-db to create isolated rows in the TEST database and run activation flows.")
fmt.Println("Example:")
fmt.Printf(" go run scripts/replay_business_bugs.go -config %s -write-db\n", *configPath)
return
}
if looksLikeProduction(cfg) && !*force {
fatalf("refusing to write DB because config does not look like a test environment: db=%s host=%s; add -force only on the test server", cfg.MySQL.Dbname, cfg.Site.Host)
}
env := mustNewReplayEnv(ctx, cfg)
if !*keep {
defer env.cleanup(ctx)
}
must(env.replaySingleSubscription(ctx))
must(env.replayInviteRulesMatrix(ctx))
must(env.replayFamilyInviteGiftToOwner(ctx))
fmt.Printf("\nPASS all replay checks in %s\n", time.Since(started).Round(time.Millisecond))
if *keep {
fmt.Println("Replay rows kept for inspection. Delete rows with remark/name/order_no containing:", marker)
}
}
func runCodeTests() error {
fmt.Println("\n-- code-level tests --")
args := []string{"test",
"./internal/logic/auth",
"./internal/logic/common",
"./internal/logic/public/order",
"./queue/logic/order",
}
cmd := exec.Command("go", args...)
cmd.Stdout = os.Stdout
cmd.Stderr = os.Stderr
if err := cmd.Run(); err != nil {
return fmt.Errorf("go test failed: %w", err)
}
fmt.Println("PASS code-level tests")
return nil
}
func loadConfig(path, dsn string) config.Config {
var cfg config.Config
conf.MustLoad(path, &cfg)
if dsn != "" {
cfg.MySQL = parseDSN(dsn)
}
return cfg
}
func parseDSN(dsn string) orm.Config {
cfg := orm.ParseDSN(dsn)
if cfg == nil {
fatalf("invalid dsn")
}
return *cfg
}
func runEmailTrialAssertions(cfg config.Config) {
fmt.Println("\n-- bug1 email trial whitelist assertions --")
cfg.Register.EnableTrial = true
cfg.Register.EnableTrialEmailWhitelist = true
if cfg.Register.TrialEmailDomainWhitelist == "" {
cfg.Register.TrialEmailDomainWhitelist = "gmail.com,163.com"
}
cases := []struct {
email string
want bool
}{
{"1.2.3.4xxx@gmaial.com", false},
{"a.b.c@gmail.com", false},
{"user+tag@gmail.com", false},
{"user@fake.gmail.com", false},
{"normaluser@gmail.com", true},
}
for _, tc := range cases {
got := authlogic.ShouldGrantTrialForEmail(cfg.Register, tc.email)
if got != tc.want {
fatalf("email trial assertion failed: email=%s got=%v want=%v", tc.email, got, tc.want)
}
fmt.Printf("PASS %-32s grant=%v\n", tc.email, got)
}
}
type replayEnv struct {
db *gorm.DB
rds *redis.Client
cfg config.Config
svcCtx *svc.ServiceContext
ids struct {
users []int64
subscribes []int64
plans []int64
orders []int64
logs []int64
}
}
func mustNewReplayEnv(ctx context.Context, cfg config.Config) *replayEnv {
fmt.Println("\n-- connecting test DB/Redis --")
db, err := orm.ConnectMysql(orm.Mysql{Config: cfg.MySQL})
must(err)
rds := redis.NewClient(&redis.Options{
Addr: cfg.Redis.Host,
Password: cfg.Redis.Pass,
DB: cfg.Redis.DB,
PoolSize: cfg.Redis.PoolSize,
MinIdleConns: cfg.Redis.MinIdleConns,
})
must(rds.Ping(ctx).Err())
svcCtx := &svc.ServiceContext{
DB: db,
Redis: rds,
Config: cfg,
UserModel: modelUser.NewModel(db, rds),
OrderModel: modelOrder.NewModel(db, rds),
SubscribeModel: modelSubscribe.NewModel(db, rds),
LogModel: modelLog.NewModel(db),
}
fmt.Printf("connected: mysql=%s/%s redis=%s\n", cfg.MySQL.Addr, cfg.MySQL.Dbname, cfg.Redis.Host)
return &replayEnv{db: db, rds: rds, cfg: cfg, svcCtx: svcCtx}
}
func (e *replayEnv) replaySingleSubscription(ctx context.Context) error {
fmt.Println("\n-- bug2 replay: paid purchase must reuse existing subscription --")
planA, planB, err := e.createPlans(ctx, "bug2")
if err != nil {
return err
}
owner, err := e.createUser(ctx, "bug2-owner", 0, 0)
if err != nil {
return err
}
existing, err := e.createUserSubscribe(ctx, owner.Id, 0, planA.Id, time.Now().Add(7*24*time.Hour))
if err != nil {
return err
}
order, err := e.createPaidOrder(ctx, owner.Id, owner.Id, planB.Id, true, "bug2")
if err != nil {
return err
}
payload, _ := json.Marshal(queueTypes.ForthwithActivateOrderPayload{OrderNo: order.OrderNo})
worker := orderLogic.NewActivateOrderLogic(e.svcCtx)
if err = worker.ProcessTask(ctx, asynq.NewTask(queueTypes.ForthwithActivateOrder, payload)); err != nil {
return err
}
if err = worker.ProcessTask(ctx, asynq.NewTask(queueTypes.ForthwithActivateOrder, payload)); err != nil {
return err
}
var rows []modelUser.Subscribe
if err = e.db.WithContext(ctx).
Where("user_id = ? AND token <> '' AND status IN ?", owner.Id, []int{0, 1, 2, 3, 4}).
Order("id ASC").
Find(&rows).Error; err != nil {
return err
}
if len(rows) != 1 {
return fmt.Errorf("bug2 failed: expected one visible subscription, got %d", len(rows))
}
if rows[0].Id != existing.Id {
return fmt.Errorf("bug2 failed: expected original subscription id=%d to be reused, got id=%d", existing.Id, rows[0].Id)
}
if rows[0].SubscribeId != planB.Id || rows[0].OrderId != order.Id {
return fmt.Errorf("bug2 failed: reused subscription not updated, subscribe_id=%d order_id=%d", rows[0].SubscribeId, rows[0].OrderId)
}
fmt.Printf("PASS user=%d user_subscribe=%d plan %d -> %d order=%s\n", owner.Id, rows[0].Id, planA.Id, planB.Id, order.OrderNo)
return nil
}
func (e *replayEnv) replayInviteGiftDays(ctx context.Context) error {
fmt.Println("\n-- bug3 replay: commission=0 invite should grant gift days to both users --")
giftDays := e.cfg.Invite.GiftDays
if giftDays <= 0 {
giftDays = 2
e.svcCtx.Config.Invite.GiftDays = giftDays
}
e.svcCtx.Config.Invite.ReferralPercentage = 0
e.svcCtx.Config.Invite.OnlyFirstPurchase = true
planA, _, err := e.createPlans(ctx, "bug3")
if err != nil {
return err
}
referer, err := e.createUser(ctx, "bug3-referer", 0, 0)
if err != nil {
return err
}
referee, err := e.createUser(ctx, "bug3-referee", referer.Id, 0)
if err != nil {
return err
}
baseExpire := time.Now().Add(10 * 24 * time.Hour).Truncate(time.Millisecond)
refererSub, err := e.createUserSubscribe(ctx, referer.Id, 0, planA.Id, baseExpire)
if err != nil {
return err
}
refereeSub, err := e.createUserSubscribe(ctx, referee.Id, 0, planA.Id, baseExpire)
if err != nil {
return err
}
order, err := e.createPaidOrder(ctx, referee.Id, referee.Id, planA.Id, true, "bug3")
if err != nil {
return err
}
payload, _ := json.Marshal(queueTypes.ForthwithActivateOrderPayload{OrderNo: order.OrderNo})
worker := orderLogic.NewActivateOrderLogic(e.svcCtx)
if err = worker.ProcessTask(ctx, asynq.NewTask(queueTypes.ForthwithActivateOrder, payload)); err != nil {
return err
}
if err = e.waitForGiftLogs(ctx, order.OrderNo, referer.Id, referee.Id); err != nil {
return err
}
var refererAfter, refereeAfter modelUser.Subscribe
if err = e.db.WithContext(ctx).First(&refererAfter, refererSub.Id).Error; err != nil {
return err
}
if err = e.db.WithContext(ctx).First(&refereeAfter, refereeSub.Id).Error; err != nil {
return err
}
minRefererExpire := baseExpire.Add(time.Duration(giftDays) * 24 * time.Hour)
if refererAfter.ExpireTime.Before(minRefererExpire.Add(-time.Second)) {
return fmt.Errorf("bug3 failed: referer expire not increased by gift days, got=%s want>=%s", refererAfter.ExpireTime, minRefererExpire)
}
if !refereeAfter.ExpireTime.After(baseExpire) {
return fmt.Errorf("bug3 failed: referee expire did not increase, got=%s base=%s", refereeAfter.ExpireTime, baseExpire)
}
// Idempotency: repeat the same order task and make sure gift logs are still one per user.
if err = worker.ProcessTask(ctx, asynq.NewTask(queueTypes.ForthwithActivateOrder, payload)); err != nil {
return err
}
var giftCount int64
if err = e.db.WithContext(ctx).Model(&modelLog.SystemLog{}).
Where("type = ? AND object_id IN ? AND content LIKE ?", modelLog.TypeGift.Uint8(), []int64{referer.Id, referee.Id}, "%"+order.OrderNo+"%").
Count(&giftCount).Error; err != nil {
return err
}
if giftCount != 2 {
return fmt.Errorf("bug3 failed: expected 2 gift logs after duplicate task, got %d", giftCount)
}
fmt.Printf("PASS referer=%d referee=%d order=%s gift_days=%d logs=%d\n", referer.Id, referee.Id, order.OrderNo, giftDays, giftCount)
return nil
}
func (e *replayEnv) replayInviteRulesMatrix(ctx context.Context) error {
fmt.Println("\n-- bug3 replay matrix: invite gift/commission rules --")
giftDays := e.cfg.Invite.GiftDays
if giftDays <= 0 {
giftDays = 2
}
e.svcCtx.Config.Invite.GiftDays = giftDays
e.svcCtx.Config.Invite.OnlyFirstPurchase = false
planA, _, err := e.createPlans(ctx, "bug3-matrix")
if err != nil {
return err
}
cases := []struct {
name string
hasReferer bool
globalReferralPct int64
isNewOrder bool
wantGiftLogs int64
wantCommissionLogs int64
wantCommission int64
}{
{
name: "no invite relation first order no gift",
hasReferer: false,
isNewOrder: true,
wantGiftLogs: 0,
},
{
name: "ordinary invite commission 0 first order gifts both",
hasReferer: true,
isNewOrder: true,
wantGiftLogs: 2,
},
{
name: "ordinary invite commission 0 non-first order no gift",
hasReferer: true,
isNewOrder: false,
wantGiftLogs: 0,
},
{
name: "channel commission positive first order gifts referee only",
hasReferer: true,
globalReferralPct: 10,
isNewOrder: true,
wantGiftLogs: 1,
wantCommissionLogs: 1,
wantCommission: 59,
},
{
name: "channel commission positive non-first order commission only",
hasReferer: true,
globalReferralPct: 10,
isNewOrder: false,
wantGiftLogs: 0,
wantCommissionLogs: 1,
wantCommission: 59,
},
}
for idx, tc := range cases {
e.svcCtx.Config.Invite.ReferralPercentage = tc.globalReferralPct
scope := fmt.Sprintf("bug3-rule-%d", idx+1)
var referer *modelUser.User
if tc.hasReferer {
referer, err = e.createUser(ctx, scope+"-referer", 0, 0)
if err != nil {
return err
}
if _, err = e.createUserSubscribe(ctx, referer.Id, 0, planA.Id, time.Now().Add(10*24*time.Hour)); err != nil {
return err
}
}
var refererID int64
if referer != nil {
refererID = referer.Id
}
referee, err := e.createUser(ctx, scope+"-referee", refererID, 0)
if err != nil {
return err
}
if _, err = e.createUserSubscribe(ctx, referee.Id, 0, planA.Id, time.Now().Add(10*24*time.Hour)); err != nil {
return err
}
order, err := e.createPaidOrder(ctx, referee.Id, referee.Id, planA.Id, tc.isNewOrder, scope)
if err != nil {
return err
}
if err = e.activateOrderTwice(ctx, order.OrderNo); err != nil {
return fmt.Errorf("%s: %w", tc.name, err)
}
if err = e.waitForLogCounts(ctx, order.OrderNo, tc.wantGiftLogs, tc.wantCommissionLogs); err != nil {
return fmt.Errorf("%s: %w", tc.name, err)
}
giftLogs, err := e.countLogs(ctx, modelLog.TypeGift.Uint8(), order.OrderNo)
if err != nil {
return err
}
commissionLogs, err := e.countLogs(ctx, modelLog.TypeCommission.Uint8(), order.OrderNo)
if err != nil {
return err
}
if giftLogs != tc.wantGiftLogs {
return fmt.Errorf("%s: expected gift logs=%d got=%d", tc.name, tc.wantGiftLogs, giftLogs)
}
if commissionLogs != tc.wantCommissionLogs {
return fmt.Errorf("%s: expected commission logs=%d got=%d", tc.name, tc.wantCommissionLogs, commissionLogs)
}
if referer != nil && tc.wantCommission > 0 {
var after modelUser.User
if err = e.db.WithContext(ctx).First(&after, referer.Id).Error; err != nil {
return err
}
if after.Commission != tc.wantCommission {
return fmt.Errorf("%s: expected referer commission=%d got=%d", tc.name, tc.wantCommission, after.Commission)
}
}
fmt.Printf("PASS %-58s gifts=%d commission_logs=%d\n", tc.name, giftLogs, commissionLogs)
}
return nil
}
func (e *replayEnv) replayFamilyInviteGiftToOwner(ctx context.Context) error {
fmt.Println("\n-- bug3 family replay: member purchase gift days go to owner --")
giftDays := e.cfg.Invite.GiftDays
if giftDays <= 0 {
giftDays = 2
}
e.svcCtx.Config.Invite.GiftDays = giftDays
e.svcCtx.Config.Invite.ReferralPercentage = 0
e.svcCtx.Config.Invite.OnlyFirstPurchase = false
planA, _, err := e.createPlans(ctx, "bug3-family")
if err != nil {
return err
}
referer, err := e.createUser(ctx, "bug3-family-referer", 0, 0)
if err != nil {
return err
}
owner, err := e.createUser(ctx, "bug3-family-owner", 0, 0)
if err != nil {
return err
}
member, err := e.createUser(ctx, "bug3-family-member", referer.Id, 0)
if err != nil {
return err
}
if err = e.createFamily(ctx, owner.Id, member.Id); err != nil {
return err
}
baseExpire := time.Now().Add(10 * 24 * time.Hour).Truncate(time.Millisecond)
ownerSub, err := e.createUserSubscribe(ctx, owner.Id, 0, planA.Id, baseExpire)
if err != nil {
return err
}
memberSub, err := e.createUserSubscribe(ctx, member.Id, 0, planA.Id, baseExpire)
if err != nil {
return err
}
refererSub, err := e.createUserSubscribe(ctx, referer.Id, 0, planA.Id, baseExpire)
if err != nil {
return err
}
order, err := e.createPaidOrder(ctx, member.Id, owner.Id, planA.Id, true, "bug3-family")
if err != nil {
return err
}
if err = e.activateOrderTwice(ctx, order.OrderNo); err != nil {
return err
}
if err = e.waitForLogCounts(ctx, order.OrderNo, 2, 0); err != nil {
return err
}
var ownerAfter, memberAfter, refererAfter modelUser.Subscribe
if err = e.db.WithContext(ctx).First(&ownerAfter, ownerSub.Id).Error; err != nil {
return err
}
if err = e.db.WithContext(ctx).First(&memberAfter, memberSub.Id).Error; err != nil {
return err
}
if err = e.db.WithContext(ctx).First(&refererAfter, refererSub.Id).Error; err != nil {
return err
}
if !ownerAfter.ExpireTime.After(baseExpire) {
return fmt.Errorf("family gift failed: owner expire not increased")
}
if !refererAfter.ExpireTime.After(baseExpire) {
return fmt.Errorf("family gift failed: referer expire not increased")
}
if memberAfter.ExpireTime.After(baseExpire.Add(time.Second)) {
return fmt.Errorf("family gift failed: member subscription should not receive gift days")
}
var memberGiftLogs int64
if err = e.db.WithContext(ctx).Model(&modelLog.SystemLog{}).
Where("type = ? AND object_id = ? AND content LIKE ?", modelLog.TypeGift.Uint8(), member.Id, "%"+order.OrderNo+"%").
Count(&memberGiftLogs).Error; err != nil {
return err
}
if memberGiftLogs != 0 {
return fmt.Errorf("family gift failed: expected no member gift logs, got %d", memberGiftLogs)
}
fmt.Printf("PASS family member purchase gift target owner owner=%d member=%d referer=%d gift_days=%d\n", owner.Id, member.Id, referer.Id, giftDays)
return nil
}
func (e *replayEnv) activateOrderTwice(ctx context.Context, orderNo string) error {
payload, _ := json.Marshal(queueTypes.ForthwithActivateOrderPayload{OrderNo: orderNo})
worker := orderLogic.NewActivateOrderLogic(e.svcCtx)
if err := worker.ProcessTask(ctx, asynq.NewTask(queueTypes.ForthwithActivateOrder, payload)); err != nil {
return err
}
return worker.ProcessTask(ctx, asynq.NewTask(queueTypes.ForthwithActivateOrder, payload))
}
func (e *replayEnv) waitForLogCounts(ctx context.Context, orderNo string, wantGiftLogs, wantCommissionLogs int64) error {
deadline := time.Now().Add(8 * time.Second)
for {
giftLogs, err := e.countLogs(ctx, modelLog.TypeGift.Uint8(), orderNo)
if err != nil {
return err
}
commissionLogs, err := e.countLogs(ctx, modelLog.TypeCommission.Uint8(), orderNo)
if err != nil {
return err
}
if giftLogs >= wantGiftLogs && commissionLogs >= wantCommissionLogs {
if wantGiftLogs == 0 && wantCommissionLogs == 0 {
time.Sleep(500 * time.Millisecond)
}
return nil
}
if time.Now().After(deadline) {
return fmt.Errorf("timed out waiting for logs: order=%s gift=%d/%d commission=%d/%d", orderNo, giftLogs, wantGiftLogs, commissionLogs, wantCommissionLogs)
}
time.Sleep(100 * time.Millisecond)
}
}
func (e *replayEnv) countLogs(ctx context.Context, logType uint8, orderNo string) (int64, error) {
var count int64
err := e.db.WithContext(ctx).Model(&modelLog.SystemLog{}).
Where("type = ? AND content LIKE ?", logType, "%"+orderNo+"%").
Count(&count).Error
return count, err
}
func (e *replayEnv) waitForGiftLogs(ctx context.Context, orderNo string, userIDs ...int64) error {
deadline := time.Now().Add(5 * time.Second)
for time.Now().Before(deadline) {
var count int64
if err := e.db.WithContext(ctx).Model(&modelLog.SystemLog{}).
Where("type = ? AND object_id IN ? AND content LIKE ?", modelLog.TypeGift.Uint8(), userIDs, "%"+orderNo+"%").
Count(&count).Error; err != nil {
return err
}
if count == int64(len(userIDs)) {
return nil
}
time.Sleep(100 * time.Millisecond)
}
return fmt.Errorf("timed out waiting for gift logs for order=%s", orderNo)
}
func (e *replayEnv) createPlans(ctx context.Context, scope string) (*modelSubscribe.Subscribe, *modelSubscribe.Subscribe, error) {
a := &modelSubscribe.Subscribe{
Name: marker + "-" + scope + "-A",
Language: "en",
UnitPrice: 599,
UnitTime: "Month",
Traffic: 1024 * 1024 * 1024,
Inventory: -1,
Quota: 0,
NodeGroupIds: modelSubscribe.JSONInt64Slice{},
}
b := &modelSubscribe.Subscribe{
Name: marker + "-" + scope + "-B",
Language: "en",
UnitPrice: 699,
UnitTime: "Month",
Traffic: 2 * 1024 * 1024 * 1024,
Inventory: -1,
Quota: 0,
NodeGroupIds: modelSubscribe.JSONInt64Slice{},
}
if err := e.db.WithContext(ctx).Create(a).Error; err != nil {
return nil, nil, err
}
if err := e.db.WithContext(ctx).Create(b).Error; err != nil {
return nil, nil, err
}
e.ids.plans = append(e.ids.plans, a.Id, b.Id)
return a, b, nil
}
func (e *replayEnv) createUser(ctx context.Context, scope string, refererID int64, referralPercentage uint8) (*modelUser.User, error) {
onlyFirst := true
enable := true
isAdmin := false
u := &modelUser.User{
Password: marker,
Algo: "default",
Salt: "default",
RefererId: refererID,
ReferralPercentage: referralPercentage,
OnlyFirstPurchase: &onlyFirst,
Enable: &enable,
IsAdmin: &isAdmin,
EnableBalanceNotify: &enable,
EnableLoginNotify: &enable,
EnableSubscribeNotify: &enable,
EnableTradeNotify: &enable,
Remark: marker + "-" + scope,
}
if err := e.db.WithContext(ctx).Create(u).Error; err != nil {
return nil, err
}
u.ReferCode = uuidx.UserInviteCode(u.Id)
if err := e.db.WithContext(ctx).Model(&modelUser.User{}).Where("id = ?", u.Id).Update("refer_code", u.ReferCode).Error; err != nil {
return nil, err
}
e.ids.users = append(e.ids.users, u.Id)
return u, nil
}
func (e *replayEnv) createFamily(ctx context.Context, ownerID, memberID int64) error {
now := time.Now()
family := &modelUser.UserFamily{
OwnerUserId: ownerID,
MaxMembers: modelUser.DefaultFamilyMaxSize,
Status: modelUser.FamilyStatusActive,
}
if err := e.db.WithContext(ctx).Create(family).Error; err != nil {
return err
}
members := []modelUser.UserFamilyMember{
{
FamilyId: family.Id,
UserId: ownerID,
Role: modelUser.FamilyRoleOwner,
Status: modelUser.FamilyMemberActive,
JoinSource: marker,
JoinedAt: now,
},
{
FamilyId: family.Id,
UserId: memberID,
Role: modelUser.FamilyRoleMember,
Status: modelUser.FamilyMemberActive,
JoinSource: marker,
JoinedAt: now,
},
}
return e.db.WithContext(ctx).Create(&members).Error
}
func (e *replayEnv) createUserSubscribe(ctx context.Context, userID, orderID, planID int64, expire time.Time) (*modelUser.Subscribe, error) {
groupLocked := false
sub := &modelUser.Subscribe{
UserId: userID,
OrderId: orderID,
SubscribeId: planID,
GroupLocked: &groupLocked,
StartTime: time.Now().Add(-time.Hour),
ExpireTime: expire,
Traffic: 1024 * 1024 * 1024,
Token: marker + "-" + uuidx.NewUUID().String(),
UUID: uuidx.NewUUID().String(),
Status: 1,
Note: marker,
}
if err := e.db.WithContext(ctx).Create(sub).Error; err != nil {
return nil, err
}
e.ids.subscribes = append(e.ids.subscribes, sub.Id)
return sub, nil
}
func (e *replayEnv) createPaidOrder(ctx context.Context, userID, subscriptionUserID, planID int64, isNew bool, scope string) (*modelOrder.Order, error) {
orderNo := fmt.Sprintf("%s-%s-%d", marker, scope, time.Now().UnixNano())
order := &modelOrder.Order{
UserId: userID,
SubscriptionUserId: subscriptionUserID,
OrderNo: orderNo,
Type: 1,
Quantity: 1,
Price: 599,
Amount: 599,
Status: 2,
SubscribeId: planID,
Method: "replay",
IsNew: isNew,
}
if err := e.db.WithContext(ctx).Create(order).Error; err != nil {
return nil, err
}
e.ids.orders = append(e.ids.orders, order.Id)
return order, nil
}
func (e *replayEnv) cleanup(ctx context.Context) {
fmt.Println("\n-- cleanup replay rows --")
e.cleanupByMarker(ctx)
if len(e.ids.subscribes) > 0 {
_ = e.db.WithContext(ctx).Where("id IN ?", e.ids.subscribes).Delete(&modelUser.Subscribe{}).Error
}
if len(e.ids.orders) > 0 {
_ = e.db.WithContext(ctx).Where("id IN ?", e.ids.orders).Delete(&modelOrder.Order{}).Error
}
if len(e.ids.plans) > 0 {
_ = e.db.WithContext(ctx).Where("id IN ?", e.ids.plans).Delete(&modelSubscribe.Subscribe{}).Error
}
if len(e.ids.users) > 0 {
_ = e.db.WithContext(ctx).Unscoped().Where("id IN ?", e.ids.users).Delete(&modelUser.User{}).Error
}
fmt.Println("cleanup done")
}
func (e *replayEnv) cleanupByMarker(ctx context.Context) {
_ = e.db.WithContext(ctx).
Where("join_source = ?", marker).
Delete(&modelUser.UserFamilyMember{}).Error
_ = e.db.WithContext(ctx).
Where("owner_user_id IN (SELECT id FROM `user` WHERE remark LIKE ?)", marker+"%").
Delete(&modelUser.UserFamily{}).Error
_ = e.db.WithContext(ctx).
Where("type IN (33, 34) AND content LIKE ?", "%"+marker+"%").
Delete(&modelLog.SystemLog{}).Error
_ = e.db.WithContext(ctx).
Where("order_no LIKE ?", marker+"%").
Delete(&modelOrder.Order{}).Error
_ = e.db.WithContext(ctx).
Where("note = ? OR token LIKE ?", marker, marker+"%").
Delete(&modelUser.Subscribe{}).Error
_ = e.db.WithContext(ctx).
Where("name LIKE ?", marker+"%").
Delete(&modelSubscribe.Subscribe{}).Error
_ = e.db.WithContext(ctx).Unscoped().
Where("remark LIKE ?", marker+"%").
Delete(&modelUser.User{}).Error
}
func looksLikeProduction(cfg config.Config) bool {
joined := strings.ToLower(strings.Join([]string{cfg.MySQL.Dbname, cfg.Site.Host, cfg.Host}, " "))
if strings.Contains(joined, "prod") || strings.Contains(joined, "production") {
return true
}
if cfg.Debug {
return false
}
if strings.Contains(joined, "test") || strings.Contains(joined, "dev") || strings.Contains(joined, "staging") {
return false
}
return true
}
func must(err error) {
if err != nil {
fatalf("%v", err)
}
}
func fatalf(format string, args ...interface{}) {
fmt.Fprintf(os.Stderr, "FAIL: "+format+"\n", args...)
os.Exit(1)
}

345
scripts/test_apple_lookup.go
View File

@ -1,345 +0,0 @@
package main
// Apple App Store Server API 测试脚本
// 用法: go run scripts/test_apple_lookup.go
// 功能: 通过 Apple Server API 获取交易历史，并按 appAccountToken (UUID) 过滤匹配交易
import (
"bytes"
"crypto/ecdsa"
"crypto/rand"
"crypto/sha256"
"crypto/x509"
"encoding/base64"
"encoding/json"
"encoding/pem"
"fmt"
"io"
"net/http"
"os"
"strings"
"time"
)
// ==================== 配置区域 ====================
// 请填入你的 Apple App Store Connect API 凭证
const (
keyID = "" // App Store Connect Key ID
issuerID = "" // App Store Connect Issuer ID
bundleID = "com.hifastvpn.vip" // 你的 App Bundle ID
sandbox = true // true=沙盒环境, false=生产环境
)
// 私钥内容 (PEM 格式)
// 从 App Store Connect 下载的 .p8 文件内容
var privateKeyPEM = ``
// ==================== 主逻辑 ====================
func main() {
if len(os.Args) < 2 {
fmt.Println("用法:")
fmt.Println(" go run scripts/test_apple_lookup.go <originalTransactionId> [appAccountToken]")
fmt.Println("")
fmt.Println("参数:")
fmt.Println(" originalTransactionId Apple 交易原始 ID")
fmt.Println(" appAccountToken 可选, 用于过滤的 UUID (服务端下单时生成)")
fmt.Println("")
fmt.Println("示例:")
fmt.Println(" go run scripts/test_apple_lookup.go 2000001132940893")
fmt.Println(" go run scripts/test_apple_lookup.go 2000001132940893 f0eb8c62-4be9-4be7-9266-58d1a0a4e7bf")
os.Exit(1)
}
originalTransactionId := os.Args[1]
filterToken := ""
if len(os.Args) >= 3 {
filterToken = os.Args[2]
}
if keyID == "" || issuerID == "" || privateKeyPEM == "" {
fmt.Println("❌ 请先在脚本中填入 Apple API 凭证 (keyID, issuerID, privateKeyPEM)")
os.Exit(1)
}
fmt.Println("═══════════════════════════════════════════════")
fmt.Println(" Apple App Store Server API - 交易历史查询")
fmt.Println("═══════════════════════════════════════════════")
fmt.Printf(" 环境: %s\n", envName())
fmt.Printf(" TransactionID: %s\n", originalTransactionId)
if filterToken != "" {
fmt.Printf(" 过滤 Token: %s\n", filterToken)
}
fmt.Println("═══════════════════════════════════════════════")
// 1. 生成 JWT
token, err := buildJWT()
if err != nil {
fmt.Printf("❌ 生成 JWT 失败: %v\n", err)
os.Exit(1)
}
fmt.Println("✅ JWT 生成成功")
// 2. 查询交易历史
fmt.Printf("\n📡 正在查询交易历史...\n")
transactions, err := getTransactionHistory(token, originalTransactionId)
if err != nil {
fmt.Printf("❌ 查询失败: %v\n", err)
os.Exit(1)
}
fmt.Printf("✅ 共获取 %d 条交易记录\n\n", len(transactions))
// 3. 解析并展示交易
for i, jws := range transactions {
info, err := parseJWS(jws)
if err != nil {
fmt.Printf(" [%d] ❌ 解析失败: %v\n", i+1, err)
continue
}
txToken := info["appAccountToken"]
matched := ""
if filterToken != "" && fmt.Sprintf("%v", txToken) == filterToken {
matched = " ✅ 匹配!"
}
fmt.Printf(" [%d]%s\n", i+1, matched)
fmt.Printf(" TransactionID: %v\n", info["transactionId"])
fmt.Printf(" OriginalTxID: %v\n", info["originalTransactionId"])
fmt.Printf(" ProductID: %v\n", info["productId"])
fmt.Printf(" AppAccountToken: %v\n", txToken)
fmt.Printf(" PurchaseDate: %v\n", formatTimestamp(info["purchaseDate"]))
fmt.Printf(" Type: %v\n", info["type"])
fmt.Println()
}
// 4. 如果指定了过滤 token，显示匹配结果
if filterToken != "" {
found := false
for _, jws := range transactions {
info, err := parseJWS(jws)
if err != nil {
continue
}
if fmt.Sprintf("%v", info["appAccountToken"]) == filterToken {
found = true
fmt.Println("═══════════════════════════════════════════════")
fmt.Printf("🎯 找到匹配的交易！AppAccountToken: %s\n", filterToken)
fmt.Printf(" TransactionID: %v\n", info["transactionId"])
fmt.Printf(" ProductID: %v\n", info["productId"])
fmt.Println("═══════════════════════════════════════════════")
break
}
}
if !found {
fmt.Println("═══════════════════════════════════════════════")
fmt.Printf("⚠️ 未找到 AppAccountToken=%s 的交易\n", filterToken)
fmt.Println("═══════════════════════════════════════════════")
}
}
}
// ==================== Apple API 调用 ====================
// getTransactionHistory 获取指定交易 ID 的完整交易历史
// Apple API: GET /inApps/v2/history/{transactionId}
func getTransactionHistory(jwt, transactionId string) ([]string, error) {
var allTransactions []string
revision := ""
for {
host := apiHost()
url := fmt.Sprintf("%s/inApps/v2/history/%s?sort=DESCENDING", host, transactionId)
if revision != "" {
url += "&revision=" + revision
}
req, _ := http.NewRequest("GET", url, nil)
req.Header.Set("Authorization", "Bearer "+jwt)
resp, err := http.DefaultClient.Do(req)
if err != nil {
return nil, fmt.Errorf("HTTP 请求失败: %v", err)
}
defer resp.Body.Close()
body, _ := io.ReadAll(resp.Body)
if resp.StatusCode != 200 {
// 尝试另一个环境
host2 := apiHostSecondary()
url2 := fmt.Sprintf("%s/inApps/v2/history/%s?sort=DESCENDING", host2, transactionId)
if revision != "" {
url2 += "&revision=" + revision
}
req2, _ := http.NewRequest("GET", url2, nil)
req2.Header.Set("Authorization", "Bearer "+jwt)
resp2, err2 := http.DefaultClient.Do(req2)
if err2 != nil {
return nil, fmt.Errorf("两个环境都失败: primary[%d:%s]", resp.StatusCode, string(body))
}
defer resp2.Body.Close()
body2, _ := io.ReadAll(resp2.Body)
if resp2.StatusCode != 200 {
return nil, fmt.Errorf("两个环境都失败: primary[%d:%s], secondary[%d:%s]",
resp.StatusCode, string(body), resp2.StatusCode, string(body2))
}
body = body2
}
var result struct {
SignedTransactions []string `json:"signedTransactions"`
Revision string `json:"revision"`
HasMore bool `json:"hasMore"`
}
if err := json.Unmarshal(body, &result); err != nil {
return nil, fmt.Errorf("解析响应失败: %v, body: %s", err, string(body))
}
allTransactions = append(allTransactions, result.SignedTransactions...)
if !result.HasMore || result.Revision == "" {
break
}
revision = result.Revision
}
return allTransactions, nil
}
// ==================== JWT 构建 ====================
// buildJWT 构建 Apple Server API 的 ES256 JWT Token
func buildJWT() (string, error) {
header := map[string]interface{}{
"alg": "ES256",
"kid": keyID,
"typ": "JWT",
}
now := time.Now().Unix()
payload := map[string]interface{}{
"iss": issuerID,
"iat": now,
"exp": now + 1800,
"aud": "appstoreconnect-v1",
}
if bundleID != "" {
payload["bid"] = bundleID
}
hb, _ := json.Marshal(header)
pb, _ := json.Marshal(payload)
enc := func(b []byte) string {
return base64.RawURLEncoding.EncodeToString(b)
}
unsigned := fmt.Sprintf("%s.%s", enc(hb), enc(pb))
key := fixPEM(privateKeyPEM)
block, _ := pem.Decode([]byte(key))
if block == nil {
return "", fmt.Errorf("invalid private key PEM")
}
keyAny, err := x509.ParsePKCS8PrivateKey(block.Bytes)
if err != nil {
return "", fmt.Errorf("parse private key failed: %v", err)
}
priv, ok := keyAny.(*ecdsa.PrivateKey)
if !ok {
return "", fmt.Errorf("private key is not ECDSA")
}
h := sha256.New()
h.Write([]byte(unsigned))
digest := h.Sum(nil)
r, s, err := ecdsa.Sign(rand.Reader, priv, digest)
if err != nil {
return "", err
}
curveBits := priv.Curve.Params().BitSize
keyBytes := curveBits / 8
if curveBits%8 > 0 {
keyBytes++
}
rBytes := r.Bytes()
rPadded := make([]byte, keyBytes)
copy(rPadded[keyBytes-len(rBytes):], rBytes)
sBytes := s.Bytes()
sPadded := make([]byte, keyBytes)
copy(sPadded[keyBytes-len(sBytes):], sBytes)
sig := append(rPadded, sPadded...)
return unsigned + "." + base64.RawURLEncoding.EncodeToString(sig), nil
}
// ==================== 工具函数 ====================
// parseJWS 解析 Apple 返回的 JWS (只取 payload 部分)
func parseJWS(jws string) (map[string]interface{}, error) {
parts := strings.Split(jws, ".")
if len(parts) != 3 {
return nil, fmt.Errorf("invalid JWS format")
}
payload, err := base64.RawURLEncoding.DecodeString(parts[1])
if err != nil {
// 尝试标准 base64
payload, err = base64.RawStdEncoding.DecodeString(parts[1])
if err != nil {
return nil, fmt.Errorf("decode payload failed: %v", err)
}
}
var result map[string]interface{}
if err := json.Unmarshal(payload, &result); err != nil {
return nil, fmt.Errorf("unmarshal payload failed: %v", err)
}
return result, nil
}
// formatTimestamp 格式化 Apple 返回的毫秒时间戳
func formatTimestamp(v interface{}) string {
if v == nil {
return "N/A"
}
switch t := v.(type) {
case float64:
ts := time.UnixMilli(int64(t))
return ts.Format("2006-01-02 15:04:05")
default:
return fmt.Sprintf("%v", v)
}
}
func fixPEM(key string) string {
if !strings.Contains(key, "\n") && strings.Contains(key, "BEGIN PRIVATE KEY") {
key = strings.ReplaceAll(key, " ", "\n")
key = strings.ReplaceAll(key, "-----BEGIN\nPRIVATE\nKEY-----", "-----BEGIN PRIVATE KEY-----")
key = strings.ReplaceAll(key, "-----END\nPRIVATE\nKEY-----", "-----END PRIVATE KEY-----")
}
return key
}
func apiHost() string {
if sandbox {
return "https://api.storekit-sandbox.itunes.apple.com"
}
return "https://api.storekit.itunes.apple.com"
}
func apiHostSecondary() string {
if sandbox {
return "https://api.storekit.itunes.apple.com"
}
return "https://api.storekit-sandbox.itunes.apple.com"
}
func envName() string {
if sandbox {
return "🏖️ Sandbox"
}
return "🏭 Production"
}
// 忽略未使用导入
var _ = bytes.NewBuffer

295
scripts/test_device_login.go
View File

@ -1,295 +0,0 @@
package main
// 设备登录测试脚本
// 用法: go run scripts/test_device_login.go
// 功能: 模拟客户端设备登录，自动加密请求体，解密响应，打印 token
import (
"bytes"
"crypto/hmac"
"crypto/md5"
"crypto/sha256"
"encoding/base64"
"encoding/hex"
"encoding/json"
"fmt"
"io"
"net/http"
"sort"
"strconv"
"strings"
"time"
"github.com/forgoer/openssl"
)
// ==================== 配置区域 ====================
const (
serverURL = "https://tapi.hifast.biz" // 服务地址
securitySecret = "c0qhq99a-nq8h-ropg-wrlc-ezj4dlkxqpzx" // device.security_secret
identifier = "test-device-script-001" // 设备唯一标识
userAgent = "TestScript/1.0" // UserAgent
appId = "android-client" // AppSignature.AppSecrets 中的 key
appSecret = "uB4G,XxL2{7b" // AppSignature.AppSecrets 中的 value
)
// ==================== AES 工具（与服务端 pkg/aes/aes.go 一致）====================
func generateKey(key string) []byte {
hash := sha256.Sum256([]byte(key))
return hash[:32]
}
func generateIv(iv, key string) []byte {
h := md5.New()
h.Write([]byte(iv))
return generateKey(hex.EncodeToString(h.Sum(nil)) + key)
}
func aesEncrypt(plainText []byte, keyStr string) (data string, nonce string, err error) {
nonce = fmt.Sprintf("%x", time.Now().UnixNano())
key := generateKey(keyStr)
iv := generateIv(nonce, keyStr)
dst, err := openssl.AesCBCEncrypt(plainText, key, iv, openssl.PKCS7_PADDING)
if err != nil {
return "", "", err
}
return base64.StdEncoding.EncodeToString(dst), nonce, nil
}
func aesDecrypt(cipherText string, keyStr string, ivStr string) (string, error) {
decode, err := base64.StdEncoding.DecodeString(cipherText)
if err != nil {
return "", err
}
key := generateKey(keyStr)
iv := generateIv(ivStr, keyStr)
dst, err := openssl.AesCBCDecrypt(decode, key, iv, openssl.PKCS7_PADDING)
return string(dst), err
}
// ==================== 签名工具（与服务端 pkg/signature 一致）====================
func buildStringToSign(method, path, rawQuery string, body []byte, xAppId, timestamp, nonce string) string {
canonical := canonicalQuery(rawQuery)
bodyHash := sha256Hex(body)
parts := []string{
strings.ToUpper(method),
path,
canonical,
bodyHash,
xAppId,
timestamp,
nonce,
}
return strings.Join(parts, "\n")
}
func canonicalQuery(rawQuery string) string {
if rawQuery == "" {
return ""
}
pairs := strings.Split(rawQuery, "&")
sort.Strings(pairs)
return strings.Join(pairs, "&")
}
func sha256Hex(data []byte) string {
h := sha256.Sum256(data)
return fmt.Sprintf("%x", h)
}
func buildSignature(stringToSign, secret string) string {
mac := hmac.New(sha256.New, []byte(secret))
mac.Write([]byte(stringToSign))
return hex.EncodeToString(mac.Sum(nil))
}
func signedRequest(method, url, rawQuery string, body []byte, token string) (*http.Request, error) {
var bodyReader io.Reader
if body != nil {
bodyReader = bytes.NewReader(body)
}
req, err := http.NewRequest(method, url, bodyReader)
if err != nil {
return nil, err
}
req.Header.Set("Content-Type", "application/json")
if token != "" {
req.Header.Set("Authorization", token) // 不带 Bearer 前缀，服务端直接 Parse token
}
timestamp := strconv.FormatInt(time.Now().Unix(), 10)
nonce := fmt.Sprintf("%x", time.Now().UnixNano())
// 提取 path
path := req.URL.Path
sts := buildStringToSign(method, path, rawQuery, body, appId, timestamp, nonce)
sig := buildSignature(sts, appSecret)
req.Header.Set("X-App-Id", appId)
req.Header.Set("X-Timestamp", timestamp)
req.Header.Set("X-Nonce", nonce)
req.Header.Set("X-Signature", sig)
return req, nil
}
// ==================== 主逻辑 ====================
func main() {
fmt.Println("=== 设备登录测试 ===")
fmt.Printf("Server: %s\n", serverURL)
fmt.Printf("Identifier: %s\n", identifier)
fmt.Println()
// 1. 构造原始请求体
payload := map[string]string{
"identifier": identifier,
"user_agent": userAgent,
}
plainBytes, err := json.Marshal(payload)
if err != nil {
fmt.Printf("[ERROR] marshal payload: %v\n", err)
return
}
fmt.Printf("原始请求体: %s\n", string(plainBytes))
// 2. AES 加密请求体
encData, nonce, err := aesEncrypt(plainBytes, securitySecret)
if err != nil {
fmt.Printf("[ERROR] encrypt: %v\n", err)
return
}
encBody := map[string]string{
"data": encData,
"time": nonce,
}
encBytes, _ := json.Marshal(encBody)
fmt.Printf("加密请求体: %s\n\n", string(encBytes))
// 3. 发送请求
req, err := signedRequest("POST", serverURL+"/v1/auth/login/device", "", encBytes, "")
if err != nil {
fmt.Printf("[ERROR] new request: %v\n", err)
return
}
req.Header.Set("Login-Type", "device")
client := &http.Client{Timeout: 10 * time.Second}
resp, err := client.Do(req)
if err != nil {
fmt.Printf("[ERROR] request failed: %v\n", err)
return
}
defer resp.Body.Close()
respBody, _ := io.ReadAll(resp.Body)
fmt.Printf("HTTP Status: %d\n", resp.StatusCode)
fmt.Printf("原始响应: %s\n\n", string(respBody))
// 4. 解密响应
// 响应格式: {"code":200,"data":{"data":"<encrypted>","time":"<nonce>"},"message":""}
var outer struct {
Code int `json:"code"`
Message string `json:"message"`
Data json.RawMessage `json:"data"`
}
if err := json.Unmarshal(respBody, &outer); err != nil {
fmt.Printf("[ERROR] parse response: %v\n", err)
return
}
if outer.Code != 200 {
fmt.Printf("[FAIL] 登录失败: code=%d message=%s\n", outer.Code, outer.Message)
return
}
// data 字段是加密对象
var encResp struct {
Data string `json:"data"`
Time string `json:"time"`
}
if err := json.Unmarshal(outer.Data, &encResp); err != nil {
// 如果 Device.Enable=false，data 直接就是明文对象
fmt.Printf("响应 data 非加密格式，直接解析: %s\n", string(outer.Data))
var loginResp struct {
Token string `json:"token"`
}
if err2 := json.Unmarshal(outer.Data, &loginResp); err2 == nil && loginResp.Token != "" {
fmt.Printf("[OK] Token: %s\n", loginResp.Token)
}
return
}
decrypted, err := aesDecrypt(encResp.Data, securitySecret, encResp.Time)
if err != nil {
fmt.Printf("[ERROR] decrypt response: %v\n", err)
return
}
fmt.Printf("解密后响应: %s\n\n", decrypted)
var loginResp struct {
Token string `json:"token"`
}
if err := json.Unmarshal([]byte(decrypted), &loginResp); err != nil {
fmt.Printf("[ERROR] parse decrypted: %v\n", err)
return
}
fmt.Printf("[OK] Token: %s\n", loginResp.Token)
// 5. 用 token 请求订阅列表
fmt.Println("\n=== 请求订阅列表 ===")
subReq, err := signedRequest("GET", serverURL+"/v1/public/subscribe/list", "", nil, loginResp.Token)
if err != nil {
fmt.Printf("[ERROR] build subscribe request: %v\n", err)
return
}
subReq.Header.Set("Login-Type", "device")
subResp, err := client.Do(subReq)
if err != nil {
fmt.Printf("[ERROR] subscribe list request: %v\n", err)
return
}
defer subResp.Body.Close()
subBody, _ := io.ReadAll(subResp.Body)
fmt.Printf("HTTP Status: %d\n", subResp.StatusCode)
fmt.Printf("原始响应: %s\n", string(subBody))
// 解密订阅列表响应
var subOuter struct {
Code int `json:"code"`
Message string `json:"message"`
Data json.RawMessage `json:"data"`
}
if err := json.Unmarshal(subBody, &subOuter); err != nil {
fmt.Printf("[ERROR] parse subscribe response: %v\n", err)
return
}
if subOuter.Code != 200 {
fmt.Printf("[FAIL] 订阅列表失败: code=%d message=%s\n", subOuter.Code, subOuter.Message)
return
}
var subEnc struct {
Data string `json:"data"`
Time string `json:"time"`
}
if err := json.Unmarshal(subOuter.Data, &subEnc); err != nil || subEnc.Data == "" {
// 无加密，直接打印
fmt.Printf("\n[OK] 订阅列表(明文): %s\n", string(subOuter.Data))
return
}
subDecrypted, err := aesDecrypt(subEnc.Data, securitySecret, subEnc.Time)
if err != nil {
fmt.Printf("[ERROR] decrypt subscribe list: %v\n", err)
return
}
fmt.Printf("\n[OK] 订阅列表(解密): %s\n", subDecrypted)
}

238
scripts/test_device_trial_registration.go
View File

@ -1,238 +0,0 @@
//go:build ignore
package main
import (
"context"
"flag"
"fmt"
"os"
"strings"
"time"
"github.com/perfect-panel/server/initialize"
"github.com/perfect-panel/server/internal/config"
authlogic "github.com/perfect-panel/server/internal/logic/auth"
modelAuth "github.com/perfect-panel/server/internal/model/auth"
modelLog "github.com/perfect-panel/server/internal/model/log"
modelNode "github.com/perfect-panel/server/internal/model/node"
modelSubscribe "github.com/perfect-panel/server/internal/model/subscribe"
modelSystem "github.com/perfect-panel/server/internal/model/system"
modelUser "github.com/perfect-panel/server/internal/model/user"
"github.com/perfect-panel/server/internal/svc"
"github.com/perfect-panel/server/internal/types"
"github.com/perfect-panel/server/pkg/conf"
"github.com/perfect-panel/server/pkg/orm"
"github.com/perfect-panel/server/pkg/tool"
"github.com/redis/go-redis/v9"
"gorm.io/gorm"
)
func main() {
var (
configPath = flag.String("config", "etc/ppanel.yaml", "config file path on the test server")
dsn = flag.String("dsn", "", "optional MySQL DSN override")
identifier = flag.String("identifier", "", "optional device identifier; defaults to a unique test identifier")
ip = flag.String("ip", "", "optional request IP; defaults to a reserved test IP")
userAgent = flag.String("user-agent", "CodexDeviceTrialTest/1.0", "device user agent")
write = flag.Bool("write", false, "actually create a test device user by running DeviceLogin")
cleanup = flag.Bool("cleanup", false, "delete the test user/device/subscription/log rows after verification")
)
flag.Parse()
if !*write {
fmt.Println("Refusing to write DB without -write.")
fmt.Println("Example:")
fmt.Printf(" go run scripts/test_device_trial_registration.go -config %s -write\n", *configPath)
os.Exit(2)
}
ctx := context.Background()
cfg := loadConfig(*configPath, *dsn)
env := mustNewDeviceTrialEnv(ctx, cfg)
defer env.close()
initialize.Device(env.svcCtx)
initialize.Register(env.svcCtx)
if *identifier == "" {
*identifier = fmt.Sprintf("codex-device-trial-%d", time.Now().UnixNano())
}
if *ip == "" {
now := time.Now().UnixNano()
*ip = fmt.Sprintf("198.18.%d.%d", now%200+1, now/200%200+1)
}
fmt.Println("== device registration no-trial test ==")
fmt.Printf("mysql: %s/%s\n", env.cfg.MySQL.Addr, env.cfg.MySQL.Dbname)
fmt.Printf("redis: %s db=%d\n", env.cfg.Redis.Host, env.cfg.Redis.DB)
fmt.Printf("device.enable=%v\n", env.svcCtx.Config.Device.Enable)
fmt.Printf("register.enable_trial=%v trial_subscribe=%d trial_time=%d trial_time_unit=%s\n",
env.svcCtx.Config.Register.EnableTrial,
env.svcCtx.Config.Register.TrialSubscribe,
env.svcCtx.Config.Register.TrialTime,
env.svcCtx.Config.Register.TrialTimeUnit,
)
fmt.Printf("identifier=%s ip=%s user_agent=%s\n", *identifier, *ip, *userAgent)
if err := ensureIdentifierUnused(ctx, env.db, *identifier); err != nil {
fail(err)
}
logic := authlogic.NewDeviceLoginLogic(ctx, env.svcCtx)
resp, err := logic.DeviceLogin(&types.DeviceLoginRequest{
Identifier: *identifier,
IP: *ip,
UserAgent: *userAgent,
})
if err != nil {
fail(fmt.Errorf("DeviceLogin failed: %w", err))
}
if resp == nil || strings.TrimSpace(resp.Token) == "" {
fail(fmt.Errorf("DeviceLogin returned empty token"))
}
fmt.Printf("login token: ok len=%d\n", len(resp.Token))
device, err := env.svcCtx.UserModel.FindOneDeviceByIdentifier(ctx, *identifier)
if err != nil {
fail(fmt.Errorf("query created device failed: %w", err))
}
fmt.Printf("device: id=%d sn=%s user_id=%d created_at=%s\n",
device.Id,
tool.DeviceIdToHash(device.Id),
device.UserId,
device.CreatedAt.Format(time.RFC3339),
)
var subs []modelUser.Subscribe
if err = env.db.WithContext(ctx).
Where("user_id = ?", device.UserId).
Order("id ASC").
Find(&subs).Error; err != nil {
fail(fmt.Errorf("query user_subscribe failed: %w", err))
}
for i := range subs {
sub := &subs[i]
fmt.Printf("subscribe: id=%d order_id=%d subscribe_id=%d status=%d start=%s expire=%s token_empty=%v\n",
sub.Id,
sub.OrderId,
sub.SubscribeId,
sub.Status,
sub.StartTime.Format(time.RFC3339),
sub.ExpireTime.Format(time.RFC3339),
sub.Token == "",
)
if sub.OrderId == 0 &&
sub.SubscribeId == env.svcCtx.Config.Register.TrialSubscribe &&
(sub.Status == 0 || sub.Status == 1) &&
sub.ExpireTime.After(time.Now()) {
fail(fmt.Errorf("FAIL: device registration unexpectedly granted trial user_subscribe_id=%d user_id=%d", sub.Id, device.UserId))
}
}
fmt.Printf("PASS: device registration created no active trial subscription for user_id=%d\n", device.UserId)
if *cleanup {
if err = cleanupTestRows(ctx, env.db, device.UserId); err != nil {
fail(fmt.Errorf("cleanup failed: %w", err))
}
fmt.Printf("cleanup: deleted test rows for user_id=%d\n", device.UserId)
}
}
type deviceTrialEnv struct {
db *gorm.DB
rds *redis.Client
cfg config.Config
svcCtx *svc.ServiceContext
}
func mustNewDeviceTrialEnv(ctx context.Context, cfg config.Config) *deviceTrialEnv {
db, err := orm.ConnectMysql(orm.Mysql{Config: cfg.MySQL})
must(err)
rds := redis.NewClient(&redis.Options{
Addr: cfg.Redis.Host,
Password: cfg.Redis.Pass,
DB: cfg.Redis.DB,
PoolSize: cfg.Redis.PoolSize,
MinIdleConns: cfg.Redis.MinIdleConns,
})
must(rds.Ping(ctx).Err())
svcCtx := &svc.ServiceContext{
DB: db,
Redis: rds,
Config: cfg,
AuthModel: modelAuth.NewModel(db, rds),
LogModel: modelLog.NewModel(db),
NodeModel: modelNode.NewModel(db, rds),
SystemModel: modelSystem.NewModel(db, rds),
UserModel: modelUser.NewModel(db, rds),
SubscribeModel: modelSubscribe.NewModel(db, rds),
}
return &deviceTrialEnv{db: db, rds: rds, cfg: cfg, svcCtx: svcCtx}
}
func (e *deviceTrialEnv) close() {
if e == nil || e.rds == nil {
return
}
_ = e.rds.Close()
}
func loadConfig(path, dsn string) config.Config {
var cfg config.Config
conf.MustLoad(path, &cfg)
if dsn != "" {
parsed := orm.ParseDSN(dsn)
if parsed == nil {
fail(fmt.Errorf("invalid dsn"))
}
cfg.MySQL = *parsed
}
return cfg
}
func ensureIdentifierUnused(ctx context.Context, db *gorm.DB, identifier string) error {
var count int64
if err := db.WithContext(ctx).
Model(&modelUser.Device{}).
Where("identifier = ?", identifier).
Count(&count).Error; err != nil {
return err
}
if count > 0 {
return fmt.Errorf("identifier already exists: %s", identifier)
}
return nil
}
func cleanupTestRows(ctx context.Context, db *gorm.DB, userID int64) error {
return db.WithContext(ctx).Transaction(func(tx *gorm.DB) error {
if err := tx.Where("object_id = ?", userID).Delete(&modelLog.SystemLog{}).Error; err != nil {
return err
}
if err := tx.Where("user_id = ?", userID).Delete(&modelUser.Subscribe{}).Error; err != nil {
return err
}
if err := tx.Where("user_id = ?", userID).Delete(&modelUser.AuthMethods{}).Error; err != nil {
return err
}
if err := tx.Where("user_id = ?", userID).Delete(&modelUser.Device{}).Error; err != nil {
return err
}
return tx.Where("id = ?", userID).Delete(&modelUser.User{}).Error
})
}
func must(err error) {
if err != nil {
fail(err)
}
}
func fail(err error) {
fmt.Fprintln(os.Stderr, err)
os.Exit(1)
}

485
scripts/test_invite_gift_days.go
View File

@ -1,485 +0,0 @@
package main
import (
"context"
"database/sql"
"encoding/json"
"flag"
"fmt"
"os"
"strings"
"time"
_ "github.com/go-sql-driver/mysql"
)
const inviteGiftMarker = "codex-test-invite-gift-days"
type giftLog struct {
Type uint16 `json:"type"`
OrderNo string `json:"order_no"`
SubscribeId int64 `json:"subscribe_id"`
Amount int64 `json:"amount"`
Balance int64 `json:"balance"`
Remark string `json:"remark,omitempty"`
Timestamp int64 `json:"timestamp"`
}
type commissionLog struct {
Type uint16 `json:"type"`
Amount int64 `json:"amount"`
OrderNo string `json:"order_no"`
Timestamp int64 `json:"timestamp"`
}
type userSubscribe struct {
ID int64
UserID int64
ExpireTime time.Time
}
func main() {
var (
dsn = flag.String("dsn", "", "MySQL DSN, for example root:pass@tcp(host:3306)/ppanel?charset=utf8mb4&parseTime=true&loc=Asia%2FShanghai")
writeDB = flag.Bool("write-db", false, "create isolated rows, simulate invite gifts, and clean them up")
keep = flag.Bool("keep", false, "keep rows for manual inspection")
cleanupOnly = flag.Bool("cleanup-only", false, "delete leftover rows created by this script and exit")
giftDays = flag.Int("gift-days", 3, "days to add to both invite users")
commission = flag.Int64("commission-percent", 10, "commission percent for commission-path simulation")
)
flag.Parse()
if *dsn == "" {
exitf("-dsn is required")
}
ctx := context.Background()
db, err := sql.Open("mysql", *dsn)
mustNoErr(err)
defer db.Close()
db.SetMaxIdleConns(1)
db.SetMaxOpenConns(1)
mustNoErr(db.PingContext(ctx))
if *cleanupOnly {
mustNoErr(cleanup(ctx, db))
fmt.Println("cleanup done")
return
}
if !*writeDB {
fmt.Println("dry run only. Add -write-db to create isolated invite rows in the TEST database.")
return
}
if *giftDays <= 0 {
exitf("-gift-days must be positive")
}
mustNoErr(cleanup(ctx, db))
if !*keep {
defer func() {
if err := cleanup(context.Background(), db); err != nil {
fmt.Fprintf(os.Stderr, "cleanup failed: %v\n", err)
}
}()
}
planID := mustCreatePlan(ctx, db)
runSelfInviteScenario(ctx, db, planID, *giftDays)
runFamilyInviteScenario(ctx, db, planID, *giftDays)
runCommissionScenario(ctx, db, planID, *giftDays, *commission)
if *keep {
fmt.Println("rows kept; cleanup with -cleanup-only. inviteGiftMarker:", inviteGiftMarker)
}
}
func runSelfInviteScenario(ctx context.Context, db *sql.DB, planID int64, giftDays int) {
refererID := mustCreateUser(ctx, db, "self-referer", 0)
refereeID := mustCreateUser(ctx, db, "self-referee", refererID)
baseExpire := time.Now().Add(10 * 24 * time.Hour).Truncate(time.Second)
refererSubID := mustCreateUserSubscribe(ctx, db, refererID, planID, baseExpire)
refereeSubID := mustCreateUserSubscribe(ctx, db, refereeID, planID, baseExpire)
orderNo := fmt.Sprintf("%s-self-order-%d", inviteGiftMarker, time.Now().UnixNano())
mustNoErr(simulateInviteGiftBoth(ctx, db, orderNo, refererID, refereeID, 0, giftDays))
mustNoErr(simulateInviteGiftBoth(ctx, db, orderNo, refererID, refereeID, 0, giftDays))
assertExpire(ctx, db, "referer", refererSubID, baseExpire, giftDays)
assertExpire(ctx, db, "referee", refereeSubID, baseExpire, giftDays)
logs := mustGiftLogCount(ctx, db, orderNo)
if logs != 2 {
exitf("gift log count mismatch after duplicate simulation: got=%d want=2", logs)
}
fmt.Printf("PASS self invite: referer=%d referee=%d order=%s gift_days=%d logs=%d\n", refererID, refereeID, orderNo, giftDays, logs)
}
func runFamilyInviteScenario(ctx context.Context, db *sql.DB, planID int64, giftDays int) {
refererOwnerID := mustCreateUser(ctx, db, "family-referer-owner", 0)
refererMemberID := mustCreateUser(ctx, db, "family-referer-member", 0)
refereeOwnerID := mustCreateUser(ctx, db, "family-referee-owner", 0)
refereeMemberID := mustCreateUser(ctx, db, "family-referee-member", refererMemberID)
mustCreateFamily(ctx, db, refererOwnerID, refererMemberID)
mustCreateFamily(ctx, db, refereeOwnerID, refereeMemberID)
baseExpire := time.Now().Add(10 * 24 * time.Hour).Truncate(time.Second)
refererOwnerSubID := mustCreateUserSubscribe(ctx, db, refererOwnerID, planID, baseExpire)
refereeOwnerSubID := mustCreateUserSubscribe(ctx, db, refereeOwnerID, planID, baseExpire)
refererMemberSubID := mustCreateUserSubscribe(ctx, db, refererMemberID, planID, baseExpire)
refereeMemberSubID := mustCreateUserSubscribe(ctx, db, refereeMemberID, planID, baseExpire)
orderNo := fmt.Sprintf("%s-family-order-%d", inviteGiftMarker, time.Now().UnixNano())
mustNoErr(simulateInviteGiftBoth(ctx, db, orderNo, refererMemberID, refereeMemberID, refereeOwnerID, giftDays))
mustNoErr(simulateInviteGiftBoth(ctx, db, orderNo, refererMemberID, refereeMemberID, refereeOwnerID, giftDays))
assertExpire(ctx, db, "referer owner", refererOwnerSubID, baseExpire, giftDays)
assertExpire(ctx, db, "referee owner", refereeOwnerSubID, baseExpire, giftDays)
assertExpire(ctx, db, "referer member", refererMemberSubID, baseExpire, 0)
assertExpire(ctx, db, "referee member", refereeMemberSubID, baseExpire, 0)
logs := mustGiftLogCount(ctx, db, orderNo)
if logs != 2 {
exitf("family gift log count mismatch after duplicate simulation: got=%d want=2", logs)
}
fmt.Printf("PASS family invite: referer_member=%d->owner=%d referee_member=%d->owner=%d order=%s gift_days=%d logs=%d\n",
refererMemberID, refererOwnerID, refereeMemberID, refereeOwnerID, orderNo, giftDays, logs)
}
func runCommissionScenario(ctx context.Context, db *sql.DB, planID int64, giftDays int, commissionPercent int64) {
if commissionPercent <= 0 {
fmt.Println("SKIP commission invite: commission-percent <= 0")
return
}
const amount int64 = 599
refererID := mustCreateUser(ctx, db, "commission-referer", 0)
refereeID := mustCreateUser(ctx, db, "commission-referee", refererID)
baseExpire := time.Now().Add(10 * 24 * time.Hour).Truncate(time.Second)
refererSubID := mustCreateUserSubscribe(ctx, db, refererID, planID, baseExpire)
refereeSubID := mustCreateUserSubscribe(ctx, db, refereeID, planID, baseExpire)
orderNo := fmt.Sprintf("%s-commission-first-order-%d", inviteGiftMarker, time.Now().UnixNano())
mustNoErr(simulateInviteCommission(ctx, db, orderNo, refererID, refereeID, 0, giftDays, amount, commissionPercent, true))
mustNoErr(simulateInviteCommission(ctx, db, orderNo, refererID, refereeID, 0, giftDays, amount, commissionPercent, true))
wantCommission := amount * commissionPercent / 100
assertExpire(ctx, db, "commission referer", refererSubID, baseExpire, 0)
assertExpire(ctx, db, "commission referee", refereeSubID, baseExpire, giftDays)
assertCommission(ctx, db, refererID, wantCommission)
assertLogCount(ctx, db, "commission first gift", 34, orderNo, 1)
assertLogCount(ctx, db, "commission first commission", 33, orderNo, 1)
nonFirstRefererID := mustCreateUser(ctx, db, "commission-nonfirst-referer", 0)
nonFirstRefereeID := mustCreateUser(ctx, db, "commission-nonfirst-referee", nonFirstRefererID)
nonFirstRefererSubID := mustCreateUserSubscribe(ctx, db, nonFirstRefererID, planID, baseExpire)
nonFirstRefereeSubID := mustCreateUserSubscribe(ctx, db, nonFirstRefereeID, planID, baseExpire)
nonFirstOrderNo := fmt.Sprintf("%s-commission-nonfirst-order-%d", inviteGiftMarker, time.Now().UnixNano())
mustNoErr(simulateInviteCommission(ctx, db, nonFirstOrderNo, nonFirstRefererID, nonFirstRefereeID, 0, giftDays, amount, commissionPercent, false))
mustNoErr(simulateInviteCommission(ctx, db, nonFirstOrderNo, nonFirstRefererID, nonFirstRefereeID, 0, giftDays, amount, commissionPercent, false))
assertExpire(ctx, db, "commission non-first referer", nonFirstRefererSubID, baseExpire, 0)
assertExpire(ctx, db, "commission non-first referee", nonFirstRefereeSubID, baseExpire, 0)
assertCommission(ctx, db, nonFirstRefererID, wantCommission)
assertLogCount(ctx, db, "commission non-first gift", 34, nonFirstOrderNo, 0)
assertLogCount(ctx, db, "commission non-first commission", 33, nonFirstOrderNo, 1)
fmt.Printf("PASS commission invite: percent=%d first_order_commission=%d non_first_commission=%d\n",
commissionPercent, wantCommission, wantCommission)
}
func assertExpire(ctx context.Context, db *sql.DB, label string, subID int64, before time.Time, addedDays int) {
got := mustExpire(ctx, db, subID)
want := before.Add(time.Duration(addedDays) * 24 * time.Hour)
if !got.Equal(want) {
exitf("%s expire mismatch: got=%s want=%s", label, got, want)
}
fmt.Printf("PASS %s subscribe=%d expire %s -> %s\n", label, subID, before.Format(time.RFC3339), got.Format(time.RFC3339))
}
func simulateInviteGiftBoth(ctx context.Context, db *sql.DB, orderNo string, refererID, refereeID, forcedRefereeOwnerID int64, days int) error {
refereeTargetID, err := resolveGiftTargetUser(ctx, db, refereeID, forcedRefereeOwnerID)
if err != nil {
return fmt.Errorf("resolve referee gift target: %w", err)
}
refererTargetID, err := resolveGiftTargetUser(ctx, db, refererID, 0)
if err != nil {
return fmt.Errorf("resolve referer gift target: %w", err)
}
if err := grantGiftDays(ctx, db, refereeTargetID, orderNo, days); err != nil {
return fmt.Errorf("grant referee gift: %w", err)
}
if err := grantGiftDays(ctx, db, refererTargetID, orderNo, days); err != nil {
return fmt.Errorf("grant referer gift: %w", err)
}
return nil
}
func simulateInviteCommission(ctx context.Context, db *sql.DB, orderNo string, refererID, refereeID, forcedRefereeOwnerID int64, days int, amount int64, commissionPercent int64, isFirstOrder bool) error {
if err := grantCommission(ctx, db, refererID, orderNo, amount, commissionPercent); err != nil {
return fmt.Errorf("grant commission: %w", err)
}
if isFirstOrder {
refereeTargetID, err := resolveGiftTargetUser(ctx, db, refereeID, forcedRefereeOwnerID)
if err != nil {
return fmt.Errorf("resolve referee gift target: %w", err)
}
if err := grantGiftDays(ctx, db, refereeTargetID, orderNo, days); err != nil {
return fmt.Errorf("grant commission-path referee gift: %w", err)
}
}
return nil
}
func resolveGiftTargetUser(ctx context.Context, db *sql.DB, userID int64, forcedOwnerID int64) (int64, error) {
if forcedOwnerID > 0 {
return forcedOwnerID, nil
}
var ownerID int64
err := db.QueryRowContext(ctx, `
SELECT 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
AND ufm.status = 1
AND ufm.role = 2
AND uf.status = 1
ORDER BY ufm.role
LIMIT 1`, userID).Scan(&ownerID)
if err == sql.ErrNoRows {
return userID, nil
}
if err != nil {
return 0, err
}
if ownerID > 0 && ownerID != userID {
return ownerID, nil
}
return userID, nil
}
func grantCommission(ctx context.Context, db *sql.DB, refererID int64, orderNo string, amount int64, commissionPercent int64) error {
var existing int64
err := db.QueryRowContext(ctx,
"SELECT COUNT(*) FROM system_logs WHERE type = 33 AND object_id = ? AND content LIKE ?",
refererID, "%\""+orderNo+"\"%",
).Scan(&existing)
if err != nil {
return err
}
if existing > 0 {
return nil
}
commissionAmount := amount * commissionPercent / 100
if _, err = db.ExecContext(ctx,
"UPDATE `user` SET commission = commission + ?, updated_at = ? WHERE id = ?",
commissionAmount, time.Now(), refererID,
); err != nil {
return err
}
content, err := json.Marshal(commissionLog{
Type: 331,
Amount: commissionAmount,
OrderNo: orderNo,
Timestamp: time.Now().UnixMilli(),
})
if err != nil {
return err
}
_, err = db.ExecContext(ctx,
"INSERT INTO system_logs (`type`, object_id, content, created_at, `date`) VALUES (33, ?, ?, ?, ?)",
refererID, string(content), time.Now(), time.Now().Format("2006-01-02"),
)
return err
}
func grantGiftDays(ctx context.Context, db *sql.DB, userID int64, orderNo string, days int) error {
var existing int64
err := db.QueryRowContext(ctx,
"SELECT COUNT(*) FROM system_logs WHERE type = 34 AND object_id = ? AND content LIKE ?",
userID, "%\""+orderNo+"\"%",
).Scan(&existing)
if err != nil {
return err
}
if existing > 0 {
return nil
}
sub, err := findActiveSubscribe(ctx, db, userID)
if err != nil {
return err
}
nextExpire := sub.ExpireTime
if !sub.ExpireTime.Equal(time.UnixMilli(0)) {
nextExpire = sub.ExpireTime.Add(time.Duration(days) * 24 * time.Hour)
if _, err = db.ExecContext(ctx,
"UPDATE user_subscribe SET expire_time = ?, updated_at = ? WHERE id = ?",
nextExpire, time.Now(), sub.ID,
); err != nil {
return err
}
}
content, err := json.Marshal(giftLog{
Type: 341,
OrderNo: orderNo,
SubscribeId: sub.ID,
Amount: int64(days),
Balance: 0,
Remark: "邀请赠送",
Timestamp: time.Now().UnixMilli(),
})
if err != nil {
return err
}
_, err = db.ExecContext(ctx,
"INSERT INTO system_logs (`type`, object_id, content, created_at, `date`) VALUES (34, ?, ?, ?, ?)",
userID, string(content), time.Now(), time.Now().Format("2006-01-02"),
)
return err
}
func findActiveSubscribe(ctx context.Context, db *sql.DB, userID int64) (*userSubscribe, error) {
var row userSubscribe
err := db.QueryRowContext(ctx, `
SELECT id, user_id, expire_time
FROM user_subscribe
WHERE user_id = ?
AND status IN (0, 1)
AND (expire_time > ? OR expire_time = '1970-01-01 08:00:00')
ORDER BY expire_time DESC, id DESC
LIMIT 1`, userID, time.Now()).Scan(&row.ID, &row.UserID, &row.ExpireTime)
if err != nil {
return nil, err
}
return &row, nil
}
func mustCreatePlan(ctx context.Context, db *sql.DB) int64 {
var sort int64
mustNoErr(db.QueryRowContext(ctx, "SELECT COALESCE(MAX(sort), 0) + 1 FROM subscribe").Scan(&sort))
res, err := db.ExecContext(ctx, `
INSERT INTO subscribe
(name, language, description, unit_price, unit_time, discount, replacement, inventory, traffic, speed_limit, device_limit, quota, new_user_only, nodes, node_tags, node_group_ids, node_group_id, traffic_limit, `+"`show`"+`, sell, sort, deduction_ratio, allow_deduction, reset_cycle, renewal_reset, show_original_price, created_at, updated_at)
VALUES (?, 'en', '', 599, 'Month', '', 0, -1, 1073741824, 0, 0, 0, false, '', '', '[]', 0, '', false, false, ?, 0, true, 0, false, true, ?, ?)`,
inviteGiftMarker+"-plan", sort, time.Now(), time.Now())
mustNoErr(err)
id, err := res.LastInsertId()
mustNoErr(err)
return id
}
func mustCreateUser(ctx context.Context, db *sql.DB, role string, refererID int64) int64 {
res, err := db.ExecContext(ctx, `
INSERT INTO `+"`user`"+`
(password, algo, avatar, balance, refer_code, referer_id, commission, referral_percentage, only_first_purchase, gift_amount, enable, is_admin, enable_balance_notify, enable_login_notify, enable_subscribe_notify, enable_trade_notify, rules, member_status, remark, created_at, updated_at, salt)
VALUES (?, 'default', '', 0, '', ?, 0, 0, true, 0, true, false, true, true, true, true, '', '', ?, ?, ?, 'default')`,
inviteGiftMarker, refererID, inviteGiftMarker+"-"+role, time.Now(), time.Now())
mustNoErr(err)
id, err := res.LastInsertId()
mustNoErr(err)
_, err = db.ExecContext(ctx, "UPDATE `user` SET refer_code = ?, updated_at = ? WHERE id = ?", fmt.Sprintf("codex%d", id), time.Now(), id)
mustNoErr(err)
return id
}
func mustCreateFamily(ctx context.Context, db *sql.DB, ownerID, memberID int64) int64 {
res, err := db.ExecContext(ctx, `
INSERT INTO user_family
(owner_user_id, max_members, status, created_at, updated_at)
VALUES (?, 3, 1, ?, ?)`, ownerID, time.Now(), time.Now())
mustNoErr(err)
familyID, err := res.LastInsertId()
mustNoErr(err)
now := time.Now()
_, err = db.ExecContext(ctx, `
INSERT INTO user_family_member
(family_id, user_id, role, status, join_source, joined_at, created_at, updated_at)
VALUES
(?, ?, 1, 1, ?, ?, ?, ?),
(?, ?, 2, 1, ?, ?, ?, ?)`,
familyID, ownerID, inviteGiftMarker, now, now, now,
familyID, memberID, inviteGiftMarker, now, now, now)
mustNoErr(err)
return familyID
}
func mustCreateUserSubscribe(ctx context.Context, db *sql.DB, userID, planID int64, expire time.Time) int64 {
token := fmt.Sprintf("%s-token-%d-%d", inviteGiftMarker, userID, time.Now().UnixNano())
uuid := fmt.Sprintf("%08d-0000-4000-8000-%012d", userID, time.Now().UnixNano()%1_000_000_000_000)
res, err := db.ExecContext(ctx, `
INSERT INTO user_subscribe
(user_id, order_id, subscribe_id, node_group_id, group_locked, traffic, download, upload, expired_download, expired_upload, token, uuid, status, note, created_at, updated_at, start_time, expire_time)
VALUES (?, 0, ?, 0, false, 1073741824, 0, 0, 0, 0, ?, ?, 1, ?, ?, ?, ?, ?)`,
userID, planID, token, uuid, inviteGiftMarker, time.Now(), time.Now(), time.Now().Add(-time.Hour), expire)
mustNoErr(err)
id, err := res.LastInsertId()
mustNoErr(err)
return id
}
func mustExpire(ctx context.Context, db *sql.DB, subID int64) time.Time {
var expire time.Time
mustNoErr(db.QueryRowContext(ctx, "SELECT expire_time FROM user_subscribe WHERE id = ?", subID).Scan(&expire))
return expire
}
func mustGiftLogCount(ctx context.Context, db *sql.DB, orderNo string) int64 {
var count int64
mustNoErr(db.QueryRowContext(ctx, "SELECT COUNT(*) FROM system_logs WHERE type = 34 AND content LIKE ?", "%"+orderNo+"%").Scan(&count))
return count
}
func assertCommission(ctx context.Context, db *sql.DB, userID int64, want int64) {
var got int64
mustNoErr(db.QueryRowContext(ctx, "SELECT commission FROM `user` WHERE id = ?", userID).Scan(&got))
if got != want {
exitf("commission mismatch: user=%d got=%d want=%d", userID, got, want)
}
fmt.Printf("PASS commission user=%d amount=%d\n", userID, got)
}
func assertLogCount(ctx context.Context, db *sql.DB, label string, logType uint8, orderNo string, want int64) {
var got int64
mustNoErr(db.QueryRowContext(ctx, "SELECT COUNT(*) FROM system_logs WHERE type = ? AND content LIKE ?", logType, "%"+orderNo+"%").Scan(&got))
if got != want {
exitf("%s log count mismatch: got=%d want=%d", label, got, want)
}
fmt.Printf("PASS %s logs=%d\n", label, got)
}
func cleanup(ctx context.Context, db *sql.DB) error {
stmts := []string{
"DELETE FROM user_family_member WHERE join_source = '" + inviteGiftMarker + "'",
"DELETE FROM user_family WHERE owner_user_id IN (SELECT id FROM `user` WHERE remark LIKE '" + inviteGiftMarker + "%')",
"DELETE FROM system_logs WHERE type IN (33, 34) AND content LIKE '%" + inviteGiftMarker + "%'",
"DELETE FROM user_subscribe WHERE note = '" + inviteGiftMarker + "' OR token LIKE '" + inviteGiftMarker + "%'",
"DELETE FROM subscribe WHERE name LIKE '" + inviteGiftMarker + "%'",
"DELETE FROM `user` WHERE remark LIKE '" + inviteGiftMarker + "%'",
}
for _, stmt := range stmts {
if _, err := db.ExecContext(ctx, stmt); err != nil {
return fmt.Errorf("%s: %w", stmt, err)
}
}
return nil
}
func mustNoErr(err error) {
if err != nil {
exitf("%v", err)
}
}
func exitf(format string, args ...interface{}) {
msg := fmt.Sprintf(format, args...)
fmt.Fprintln(os.Stderr, "FAIL:", strings.TrimSpace(msg))
os.Exit(1)
}

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@ -1,35 +0,0 @@
# 说明文档.md
## 项目规划
本任务旨在帮助用户在云端服务器上替换 SSL 证书。用户已解压 `airoport.co_ecc.zip`，需将得到的 `.cer` 和 `.key` 文件替换到 `/etc/letsencrypt/archive/airoport.co` 目录下对应的 `.pem` 文件中。
## 实施方案
1. **文件对应关系确认**：映射解压后的文件与 `archive` 目录下的 PEM 文件。
2. **备份与替换**：备份旧证书，执行 `cp` 命令覆盖现有文件。
3. **服务重启建议**：提示用户替换后需重启 Nginx/Gateway 服务以使证书生效。
## 进度记录
| 时间节点 | 任务说明 | 进度 | 结果说明 |
| :--- | :--- | :--- | :--- |
| 2026-03-11 | 初始化文档并提供指令 | [x] 已完成 | 已提供查找日志的命令并记录在文档中 |
| 2026-03-11 | 提供今天所有 ERROR 报错指令 | [x] 已完成 | 已提供根据日期过滤 ERROR 的命令 |
| 2026-03-12 | 分析并确认 Unknown column 错误 | [x] 已完成 | 确认为 `user_device` 缺少 `short_code` 字段，已提供 SQL |
| 2026-03-12 | 提供 SSL 证书替换指令 | [x] 已完成 | 已提供备份与替换证书的组合指令 |
| 2026-03-17 | 合并 internal 到 internal/main | [x] 已完成 | 已查验均为fast-forward，受限网络/权限，需手动push完成合并 |
| 2026-04-14 | 排查支付成功但订阅未下发问题 | [x] 已完成 | 已提供 Docker 相关的日志排查与数据库核对命令 |
certbot certonly --manual --preferred-challenges dns -d airoport.win -d "*.airoport.win" -d hifastapp.com
gunzip -c mysql_dump_20260318_052811.sql.gz \
| docker exec -i ppanel-mysql mysql -uroot -pjpcV41ppanel
go run scripts/migrate_paid_users.go -src 'root:rootpassword@tcp(127.0.0.1:3306)/ppanel?charset=utf8mb4&parseTime=True&loc=Local' -dst 'root:jpcV41ppanel@tcp(103.150.215.44:3306)/hifast?charset=utf8mb4&parseTime=True&loc=Local' -clean
docker exec ppanel-redis redis-cli --scan --pattern "*" \
grep -vE "^auth:session_id:|^auth:user_sessions:" \
xargs -r -n 500 docker exec -i ppanel-redis redis-cli DEL