Notre avis
Cette compétence orchestre des revues de code multi-dimensionnelles en parallèle, générant des retours unifiés et priorisés pour les pull requests, audits de sécurité, revues de performance et d'architecture.
Points forts
- Analyse parallèle sur plusieurs dimensions (qualité, sécurité, performance, architecture)
- Synthèse unifiée avec priorisation des problèmes
- Prise en charge de plusieurs types de revues (PR, sécurité, performance, architecture)
- Scalabilité via des sous-agents spécialisés
Limites
- Nécessite une coordination minutieuse des sous-agents
- Possible chevauchement des résultats entre dimensions
- Dépend de la qualité des modèles pour chaque dimension
Utilisez cette compétence pour des revues de code complètes nécessitant une analyse multi-facette et des retours structurés.
Évitez pour des modifications triviales ou des changements minimes ne justifiant pas une orchestration poussée.
Analyse de sécurité
SûrThe skill describes code review orchestration patterns using task management and subagents. It contains no instructions for destructive, exfiltrating, or obfuscated actions. No commands like curl, sh, rm, or secret handling are present. The bash examples use 'npx cc-mirror tasks create' for task management, which is not inherently dangerous.
Aucun point d'attention détecté
Exemples
Review PR #123Security audit the authentication moduleFind performance bottlenecksCode Review Orchestration Patterns
┌─────────────────────────────────────────────────────────────┐
│ │
│ Reviews should be thorough, fast, and actionable. │
│ Parallel analysis. Unified feedback. Clear priorities. │
│ │
└─────────────────────────────────────────────────────────────┘
Load when: PR review, security audit, performance review, architecture review, pre-merge validation Common patterns: Multi-Dimensional Analysis, OWASP-Parallel, Layer-by-Layer
Table of Contents
Pull Request Review
Pattern: Multi-Dimensional Analysis
User Request: "Review PR #123"
Phase 1: FAN-OUT (Parallel analysis dimensions)
├─ Explore agent: Understand PR context, related issues
├─ Agent A: Code quality analysis (style, patterns, DRY)
├─ Agent B: Logic correctness (edge cases, error handling)
├─ Agent C: Security implications
└─ Agent D: Performance implications
Phase 2: REDUCE (Synthesize)
└─ General-purpose agent: Aggregate findings, prioritize, format review
Implementation:
# All in single message for parallelism (opus for reviews - critical thinking)
Task(subagent_type="Explore", prompt="Fetch PR #123 details, understand context and related issues",
model="haiku", run_in_background=True)
Task(subagent_type="general-purpose", prompt="Review code quality: patterns, readability, maintainability",
model="opus", run_in_background=True)
Task(subagent_type="general-purpose", prompt="Review logic: correctness, edge cases, error handling",
model="opus", run_in_background=True)
Task(subagent_type="general-purpose", prompt="Review security: injection, auth, data exposure",
model="opus", run_in_background=True)
Task(subagent_type="general-purpose", prompt="Review performance: complexity, queries, memory",
model="opus", run_in_background=True)
Security Audit
Pattern: OWASP-Parallel
User Request: "Security audit the authentication module"
Phase 1: FAN-OUT (OWASP categories in parallel)
├─ Agent A: Injection vulnerabilities (SQL, command, XSS)
├─ Agent B: Authentication/session issues
├─ Agent C: Access control problems
├─ Agent D: Cryptographic weaknesses
├─ Agent E: Data exposure risks
└─ Agent F: Security misconfiguration
Phase 2: REDUCE
└─ General-purpose agent: Risk-ranked findings with remediation
Pattern: Attack Surface Mapping
Phase 1: EXPLORE
└─ Explore agent: Map all entry points (APIs, forms, file uploads)
Phase 2: FAN-OUT (Per entry point)
├─ Agent per entry point: Analyze input validation, sanitization
Phase 3: PIPELINE
└─ General-purpose agent: Threat model, prioritized vulnerabilities
Performance Review
Pattern: Layer-by-Layer
User Request: "Find performance bottlenecks"
Phase 1: FAN-OUT (Analyze each layer)
├─ Agent A: Database queries (N+1, missing indexes, slow queries)
├─ Agent B: API layer (response times, payload sizes)
├─ Agent C: Frontend (bundle size, render performance)
└─ Agent D: Infrastructure (caching, connection pooling)
Phase 2: REDUCE
└─ General-purpose agent: Prioritized optimization roadmap
Pattern: Complexity Audit
Phase 1: MAP
└─ Explore agent: Find all functions, measure complexity
Phase 2: FAN-OUT (High complexity functions)
├─ Agent A: Analyze function X, suggest optimizations
├─ Agent B: Analyze function Y, suggest optimizations
└─ Agent C: Analyze function Z, suggest optimizations
Architecture Review
Pattern: Multi-Stakeholder
User Request: "Review system architecture"
Phase 1: FAN-OUT (Perspectives)
├─ Agent A: Scalability assessment
├─ Agent B: Maintainability assessment
├─ Agent C: Security architecture
├─ Agent D: Cost efficiency
└─ Agent E: Developer experience
Phase 2: REDUCE
└─ Plan agent: Synthesize into architecture decision record
Pattern: Dependency Analysis
Phase 1: EXPLORE
└─ Explore agent: Map all module dependencies
Phase 2: FAN-OUT
├─ Agent A: Identify circular dependencies
├─ Agent B: Find coupling hotspots
└─ Agent C: Assess abstraction boundaries
Phase 3: PIPELINE
└─ Plan agent: Recommend architectural improvements
Pre-merge Validation
Pattern: Comprehensive Gate
User Request: "Validate PR ready for merge"
Phase 1: FAN-OUT (All checks in parallel)
├─ Background agent: Run full test suite
├─ Agent A: Verify all review comments addressed
├─ Agent B: Check for merge conflicts
├─ Agent C: Validate documentation updated
└─ Agent D: Confirm CI checks passing
Phase 2: REDUCE
└─ General-purpose agent: Go/no-go recommendation with blockers
Review Output Formats
Structured Review Template
## Summary
[1-2 sentence overview]
## Risk Assessment
- **Security**: Low/Medium/High
- **Performance**: Low/Medium/High
- **Breaking Changes**: Yes/No
## Must Fix (Blocking)
1. [Critical issue with line reference]
## Should Fix (Non-blocking)
1. [Important improvement]
## Consider (Optional)
1. [Nice-to-have suggestion]
## Positive Notes
- [What was done well]
Task Management for Reviews
For comprehensive reviews, create parallel analysis tasks:
# Create review tasks (can run in parallel)
npx cc-mirror tasks create --subject "Analyze PR context" --description "Understand changes, related issues..."
npx cc-mirror tasks create --subject "Review code quality" --description "Patterns, readability, maintainability..."
npx cc-mirror tasks create --subject "Check security" --description "Injection, auth, data exposure..."
npx cc-mirror tasks create --subject "Assess performance" --description "Complexity, queries, memory..."
npx cc-mirror tasks create --subject "Synthesize review" --description "Aggregate findings into review..."
# Synthesis blocked by analysis tasks
npx cc-mirror tasks update 5 --add-blocked-by 1,2,3,4
# Spawn parallel agents for analysis (opus for reviews - critical thinking)
Task(subagent_type="Explore", prompt="Task 1: Analyze PR context...",
model="haiku", run_in_background=True)
Task(subagent_type="general-purpose", prompt="Task 2: Review code quality...",
model="opus", run_in_background=True)
Task(subagent_type="general-purpose", prompt="Task 3: Check security...",
model="opus", run_in_background=True)
Task(subagent_type="general-purpose", prompt="Task 4: Assess performance...",
model="opus", run_in_background=True)
─── ◈ Code Review ───────────────────────
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