Skills Agentes

Agent Code Review Swarm

Despliega agentes de IA especializados para realizar revisiones de código exhaustivas e inteligentes que van más allá del análisis estático tradicional.

Reemplaza a: análisis estático tradicional

Estrellas
69.4k

en todo el repo

Actividad
27

0–100, la ruta de este skill

Actualizado
hace 6 meses

último commit aquí

Commits
0

últimos 90 días

Contexto
3k tok

18 tok en reposo

Paquete
1 archivo

12 KB

Instalar

Funciona con cualquier agente que lea SKILL.md

npx -y skills add ruvnet/ruflo --skill agent-code-review-swarm --agent claude-code

Se instala solo en este repositorio.

Este skill reads environment config.

Qué hace

  • Coordina agentes de revisión especializados (seguridad, rendimiento, estilo, arquitectura, accesibilidad) sobre un PR
  • Bloquea el PR ante hallazgos críticos de seguridad y comenta o solicita cambios vía gh CLI
  • Ejecuta benchmarks de rendimiento comparando con la rama principal
  • Aplica quality gates configurables (seguridad, cobertura, complejidad) antes de aprobar

Úsalo cuando

  • Al revisar un pull request que toca código sensible (auth, pagos) y requiere revisión de seguridad profunda
  • Al necesitar revisión automatizada multi-agente en cada push a un PR
  • Al querer aplicar quality gates antes de fusionar un PR

No lo uses cuando

    Qué lo activa

    Di cualquiera de estas frases y el agente debería cargar este skill.

    • Inicia una revisión multi-agente del PR 123
    • Ejecuta una revisión de seguridad sobre los archivos cambiados en este PR
    • Configura quality gates para este repositorio

    SKILL.md

    En inglés

    name: code-review-swarm description: Deploy specialized AI agents to perform comprehensive, intelligent code reviews that go beyond traditional static analysis tools: mcp__claude-flow__swarm_init, mcp__claude-flow__agent_spawn, mcp__claude-flow__task_orchestrate, Bash, Read, Write, TodoWrite color: blue type: development capabilities:

    • Automated multi-agent code review
    • Security vulnerability analysis
    • Performance bottleneck detection
    • Architecture pattern validation
    • Style and convention enforcement priority: high hooks: pre: | echo "Starting code-review-swarm..." echo "Initializing multi-agent review system" gh auth status || (echo "GitHub CLI not authenticated" && exit 1) post: | echo "Completed code-review-swarm" echo "Review results posted to GitHub" echo "Quality gates evaluated"

    Code Review Swarm - Automated Code Review with AI Agents

    Overview

    Deploy specialized AI agents to perform comprehensive, intelligent code reviews that go beyond traditional static analysis.

    Core Features

    1. Multi-Agent Review System

    # Initialize code review swarm with gh CLI
    # Get PR details
    PR_DATA=$(gh pr view 123 --json files,additions,deletions,title,body)
    PR_DIFF=$(gh pr diff 123)
    
    # Initialize swarm with PR context
    npx ruv-swarm github review-init \
      --pr 123 \
      --pr-data "$PR_DATA" \
      --diff "$PR_DIFF" \
      --agents "security,performance,style,architecture,accessibility" \
      --depth comprehensive
    
    # Post initial review status
    gh pr comment 123 --body "🔍 Multi-agent code review initiated"
    

    2. Specialized Review Agents

    Security Agent

    # Security-focused review with gh CLI
    # Get changed files
    CHANGED_FILES=$(gh pr view 123 --json files --jq '.files[].path')
    
    # Run security review
    SECURITY_RESULTS=$(npx ruv-swarm github review-security \
      --pr 123 \
      --files "$CHANGED_FILES" \
      --check "owasp,cve,secrets,permissions" \
      --suggest-fixes)
    
    # Post security findings
    if echo "$SECURITY_RESULTS" | grep -q "critical"; then
      # Request changes for critical issues
      gh pr review 123 --request-changes --body "$SECURITY_RESULTS"
      # Add security label
      gh pr edit 123 --add-label "security-review-required"
    else
      # Post as comment for non-critical issues
      gh pr comment 123 --body "$SECURITY_RESULTS"
    fi
    

    Performance Agent

    # Performance analysis
    npx ruv-swarm github review-performance \
      --pr 123 \
      --profile "cpu,memory,io" \
      --benchmark-against main \
      --suggest-optimizations
    

    Architecture Agent

    # Architecture review
    npx ruv-swarm github review-architecture \
      --pr 123 \
      --check "patterns,coupling,cohesion,solid" \
      --visualize-impact \
      --suggest-refactoring
    

    3. Review Configuration

    # .github$review-swarm.yml
    version: 1
    review:
      auto-trigger: true
      required-agents:
        - security
        - performance
        - style
      optional-agents:
        - architecture
        - accessibility
        - i18n
      
      thresholds:
        security: block
        performance: warn
        style: suggest
        
      rules:
        security:
          - no-eval
          - no-hardcoded-secrets
          - proper-auth-checks
        performance:
          - no-n-plus-one
          - efficient-queries
          - proper-caching
        architecture:
          - max-coupling: 5
          - min-cohesion: 0.7
          - follow-patterns
    

    Review Agents

    Security Review Agent

    // Security checks performed
    {
      "checks": [
        "SQL injection vulnerabilities",
        "XSS attack vectors",
        "Authentication bypasses",
        "Authorization flaws",
        "Cryptographic weaknesses",
        "Dependency vulnerabilities",
        "Secret exposure",
        "CORS misconfigurations"
      ],
      "actions": [
        "Block PR on critical issues",
        "Suggest secure alternatives",
        "Add security test cases",
        "Update security documentation"
      ]
    }
    

    Performance Review Agent

    // Performance analysis
    {
      "metrics": [
        "Algorithm complexity",
        "Database query efficiency",
        "Memory allocation patterns",
        "Cache utilization",
        "Network request optimization",
        "Bundle size impact",
        "Render performance"
      ],
      "benchmarks": [
        "Compare with baseline",
        "Load test simulations",
        "Memory leak detection",
        "Bottleneck identification"
      ]
    }
    

    Style & Convention Agent

    // Style enforcement
    {
      "checks": [
        "Code formatting",
        "Naming conventions",
        "Documentation standards",
        "Comment quality",
        "Test coverage",
        "Error handling patterns",
        "Logging standards"
      ],
      "auto-fix": [
        "Formatting issues",
        "Import organization",
        "Trailing whitespace",
        "Simple naming issues"
      ]
    }
    

    Architecture Review Agent

    // Architecture analysis
    {
      "patterns": [
        "Design pattern adherence",
        "SOLID principles",
        "DRY violations",
        "Separation of concerns",
        "Dependency injection",
        "Layer violations",
        "Circular dependencies"
      ],
      "metrics": [
        "Coupling metrics",
        "Cohesion scores",
        "Complexity measures",
        "Maintainability index"
      ]
    }
    

    Advanced Review Features

    1. Context-Aware Reviews

    # Review with full context
    npx ruv-swarm github review-context \
      --pr 123 \
      --load-related-prs \
      --analyze-impact \
      --check-breaking-changes
    

    2. Learning from History

    # Learn from past reviews
    npx ruv-swarm github review-learn \
      --analyze-past-reviews \
      --identify-patterns \
      --improve-suggestions \
      --reduce-false-positives
    

    3. Cross-PR Analysis

    # Analyze related PRs together
    npx ruv-swarm github review-batch \
      --prs "123,124,125" \
      --check-consistency \
      --verify-integration \
      --combined-impact
    

    Review Automation

    Auto-Review on Push

    # .github$workflows$auto-review.yml
    name: Automated Code Review
    on:
      pull_request:
        types: [opened, synchronize]
    
    jobs:
      swarm-review:
        runs-on: ubuntu-latest
        steps:
          - uses: actions$checkout@v3
            with:
              fetch-depth: 0
              
          - name: Setup GitHub CLI
            run: echo "${{ secrets.GITHUB_TOKEN }}" | gh auth login --with-token
              
          - name: Run Review Swarm
            run: |
              # Get PR context with gh CLI
              PR_NUM=${{ github.event.pull_request.number }}
              PR_DATA=$(gh pr view $PR_NUM --json files,title,body,labels)
              
              # Run swarm review
              REVIEW_OUTPUT=$(npx ruv-swarm github review-all \
                --pr $PR_NUM \
                --pr-data "$PR_DATA" \
                --agents "security,performance,style,architecture")
              
              # Post review results
              echo "$REVIEW_OUTPUT" | gh pr review $PR_NUM --comment -F -
              
              # Update PR status
              if echo "$REVIEW_OUTPUT" | grep -q "approved"; then
                gh pr review $PR_NUM --approve
              elif echo "$REVIEW_OUTPUT" | grep -q "changes-requested"; then
                gh pr review $PR_NUM --request-changes -b "See review comments above"
              fi
    

    Review Triggers

    // Custom review triggers
    {
      "triggers": {
        "high-risk-files": {
          "paths": ["**$auth/**", "**$payment/**"],
          "agents": ["security", "architecture"],
          "depth": "comprehensive"
        },
        "performance-critical": {
          "paths": ["**$api/**", "**$database/**"],
          "agents": ["performance", "database"],
          "benchmarks": true
        },
        "ui-changes": {
          "paths": ["**$components/**", "**$styles/**"],
          "agents": ["accessibility", "style", "i18n"],
          "visual-tests": true
        }
      }
    }
    

    Review Comments

    Intelligent Comment Generation

    # Generate contextual review comments with gh CLI
    # Get PR diff with context
    PR_DIFF=$(gh pr diff 123 --color never)
    PR_FILES=$(gh pr view 123 --json files)
    
    # Generate review comments
    COMMENTS=$(npx ruv-swarm github review-comment \
      --pr 123 \
      --diff "$PR_DIFF" \
      --files "$PR_FILES" \
      --style "constructive" \
      --include-examples \
      --suggest-fixes)
    
    # Post comments using gh CLI
    echo "$COMMENTS" | jq -c '.[]' | while read -r comment; do
      FILE=$(echo "$comment" | jq -r '.path')
      LINE=$(echo "$comment" | jq -r '.line')
      BODY=$(echo "$comment" | jq -r '.body')
      
      # Create review with inline comments
      gh api \
        --method POST \
        $repos/:owner/:repo$pulls/123$comments \
        -f path="$FILE" \
        -f line="$LINE" \
        -f body="$BODY" \
        -f commit_id="$(gh pr view 123 --json headRefOid -q .headRefOid)"
    done
    

    Comment Templates

    <!-- Security Issue Template -->
    🔒 **Security Issue: [Type]**
    
    **Severity**: 🔴 Critical / 🟡 High / 🟢 Low
    
    **Description**: 
    [Clear explanation of the security issue]
    
    **Impact**:
    [Potential consequences if not addressed]
    
    **Suggested Fix**:
    ```language
    [Code example of the fix]
    

    References:

    
    ### Batch Comment Management
    ```bash
    # Manage review comments efficiently
    npx ruv-swarm github review-comments \
      --pr 123 \
      --group-by "agent,severity" \
      --summarize \
      --resolve-outdated
    

    Integration with CI/CD

    Status Checks

    # Required status checks
    protection_rules:
      required_status_checks:
        contexts:
          - "review-swarm$security"
          - "review-swarm$performance"
          - "review-swarm$architecture"
    

    Quality Gates

    # Define quality gates
    npx ruv-swarm github quality-gates \
      --define '{
        "security": {"threshold": "no-critical"},
        "performance": {"regression": "<5%"},
        "coverage": {"minimum": "80%"},
        "architecture": {"complexity": "<10"}
      }'
    

    Review Metrics

    # Track review effectiveness
    npx ruv-swarm github review-metrics \
      --period 30d \
      --metrics "issues-found,false-positives,fix-rate" \
      --export-dashboard
    

    Best Practices

    1. Review Configuration

    • Define clear review criteria
    • Set appropriate thresholds
    • Configure agent specializations
    • Establish override procedures

    2. Comment Quality

    • Provide actionable feedback
    • Include code examples
    • Reference documentation
    • Maintain respectful tone

    3. Performance

    • Cache analysis results
    • Incremental reviews for large PRs
    • Parallel agent execution
    • Smart comment batching

    Advanced Features

    1. AI Learning

    # Train on your codebase
    npx ruv-swarm github review-train \
      --learn-patterns \
      --adapt-to-style \
      --improve-accuracy
    

    2. Custom Review Agents

    // Create custom review agent
    class CustomReviewAgent {
      async review(pr) {
        const issues = [];
        
        // Custom logic here
        if (await this.checkCustomRule(pr)) {
          issues.push({
            severity: 'warning',
            message: 'Custom rule violation',
            suggestion: 'Fix suggestion'
          });
        }
        
        return issues;
      }
    }
    

    3. Review Orchestration

    # Orchestrate complex reviews
    npx ruv-swarm github review-orchestrate \
      --strategy "risk-based" \
      --allocate-time-budget \
      --prioritize-critical
    

    Examples

    Security-Critical PR

    # Auth system changes
    npx ruv-swarm github review-init \
      --pr 456 \
      --agents "security,authentication,audit" \
      --depth "maximum" \
      --require-security-approval
    

    Performance-Sensitive PR

    # Database optimization
    npx ruv-swarm github review-init \
      --pr 789 \
      --agents "performance,database,caching" \
      --benchmark \
      --profile
    

    UI Component PR

    # New component library
    npx ruv-swarm github review-init \
      --pr 321 \
      --agents "accessibility,style,i18n,docs" \
      --visual-regression \
      --component-tests
    

    Monitoring & Analytics

    Review Dashboard

    # Launch review dashboard
    npx ruv-swarm github review-dashboard \
      --real-time \
      --show "agent-activity,issue-trends,fix-rates"
    

    Review Reports

    # Generate review reports
    npx ruv-swarm github review-report \
      --format "markdown" \
      --include "summary,details,trends" \
      --email-stakeholders
    

    See also: swarm-pr.md, workflow-automation.md

    Reproducido de ruvnet/ruflo bajo licencia MIT. Leer esta página en markdown.

    Archivos

    1 archivo en el paquete. Solo se lee SKILL.md al activarse — las referencias se cargan si el skill decide que las necesita.

    Antes de instalar

    Requiere GitHub CLI autenticado (gh auth status) y npx ruv-swarm/claude-flow instalados.

    Necesita en el PATH:ghjqnpx

    Variables de entorno:BODYCHANGED_FILESCOMMENTSFILELINEPR_DATAPR_DIFFPR_FILESPR_NUMREVIEW_OUTPUTSECURITY_RESULTS

    Detalles

    Creador
    ruvnet
    Categoría
    Testing y QA
    Licencia
    MIT
    Recursos incluidos
    Solo SKILL.md
    Repositorio
    ruvnet/ruflo
    Código fuente
    Ver SKILL.md

    Etiquetas

    Más de ruvnet/ruflo

    Este repo incluye 275 skills. Si instalas uno, normalmente ya tienes los demás.

    Inspect and audit GEPA genomes via the `@metaharness/darwin/gepa` library entry (darwin 0.8.0) — load/validate a genome (default is the shipped cand-6 promotion), render the system prompt a genome compiles to, or classify failure modes in a run transcript. The `gepaOptimize` loop itself is library-only (bring your own evaluator) and not surfaced here — use `harness-evolve` for sandbox-scored evolution. Degrades gracefully when @metaharness/darwin is absent.

    Costo de contexto al activarse
    833 tok
    Tamaño del paquete
    1 archivo
    Última actualización
    hace 4 días
    Permisos
    herramientas desarrollo

    One-shot chat completion against DeepSeek's `deepseek-chat` model via the OpenAI-compatible /v1/chat/completions endpoint. Reads DEEPSEEK_API_KEY from the environment; degrades gracefully (exit 0 with a JSON status:degraded envelope) when the key is missing or the API is unreachable. Use for non-reasoning tasks — summarization, extraction, quick classification — where deepseek-reasoner would be overkill.

    Costo de contexto al activarse
    566 tok
    Tamaño del paquete
    1 archivo
    Última actualización
    hace 4 días
    Permisos
    herramientas desarrollo

    Reasoning-mode completion against DeepSeek's `deepseek-reasoner` model (R1) via /v1/chat/completions. Surfaces the model's chain-of-thought (`reasoning_content`) separately from the final answer (`content`), so callers can display or discard the CoT without re-parsing. Reads DEEPSEEK_API_KEY; degrades gracefully (exit 0 with status:degraded envelope) when unset or the API is unreachable. Ignores temperature/top_p per DeepSeek's spec for reasoner models.

    Costo de contexto al activarse
    627 tok
    Tamaño del paquete
    1 archivo
    Última actualización
    hace 4 días
    Permisos
    herramientas desarrollo

    Build or rebuild the ADR index + dependency graph by running scripts/import.mjs (handles v3-style and plugin-style ADR formats; one Bash call vs hundreds of MCP round-trips)

    Costo de contexto al activarse
    866 tok
    Tamaño del paquete
    1 archivo
    Última actualización
    hace 27 días
    herramientas desarrollo

    Create a new Architecture Decision Record with sequential numbering and AgentDB registration

    Costo de contexto al activarse
    680 tok
    Tamaño del paquete
    1 archivo
    Última actualización
    hace 27 días
    herramientas desarrollo

    Show AGNTCY/SLIM/CASA integration status — whether upstream AGNTCY packages are installed, which transport (local vs SLIM) is active, and whether CASA enforcement is enabled. Use when the user asks "is AGNTCY configured?", "show SLIM/CASA status", or "is AGNTCY/IOC integration active?".

    Costo de contexto al activarse
    443 tok
    Tamaño del paquete
    1 archivo
    Última actualización
    hace 26 días
    herramientas desarrollo