home / skills / ruvnet / ruflo / agent-code-review-swarm

agent-code-review-swarm skill

/.agents/skills/agent-code-review-swarm

This skill orchestrates a multi-agent code review swarm to identify security, performance, and architectural issues across PRs.

npx playbooks add skill ruvnet/ruflo --skill agent-code-review-swarm

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SKILL.md
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---
name: agent-code-review-swarm
description: Agent skill for code-review-swarm - invoke with $agent-code-review-swarm
---

---
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
```bash
# 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 "šŸ” Multi-agent code review initiated"
```

### 2. Specialized Review Agents

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

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

### 3. Review Configuration
```yaml
# .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
```javascript
// 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
```javascript
// 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
```javascript
// 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
```javascript
// 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
```bash
# Review with full context
npx ruv-swarm github review-context \
  --pr 123 \
  --load-related-prs \
  --analyze-impact \
  --check-breaking-changes
```

### 2. Learning from History
```bash
# Learn from past reviews
npx ruv-swarm github review-learn \
  --analyze-past-reviews \
  --identify-patterns \
  --improve-suggestions \
  --reduce-false-positives
```

### 3. Cross-PR Analysis
```bash
# 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
```yaml
# .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
```javascript
// 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
```bash
# 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
```markdown
<!-- Security Issue Template -->
šŸ”’ **Security Issue: [Type]**

**Severity**: šŸ”“ 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**:
- [OWASP Guide](link)
- [Security Best Practices](link)
```

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

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

### 2. Custom Review Agents
```javascript
// 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
```bash
# Orchestrate complex reviews
npx ruv-swarm github review-orchestrate \
  --strategy "risk-based" \
  --allocate-time-budget \
  --prioritize-critical
```

## Examples

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

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

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

## Monitoring & Analytics

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

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

See also: [swarm-pr.md](.$swarm-pr.md), [workflow-automation.md](.$workflow-automation.md)

Overview

This skill deploys a coordinated swarm of specialized AI agents to perform automated, context-aware code reviews that go beyond static analysis. It runs multiple reviewers (security, performance, style, architecture, accessibility) in parallel, aggregates findings, and posts actionable comments and quality-gate results back to your CI or pull request. The goal is faster, more consistent reviews and fewer regressions in production.

How this skill works

The skill initializes a multi-agent review session with PR context (diff, changed files, history) and spawns dedicated agents for security, performance, style, architecture, etc. Each agent runs targeted checks, suggests fixes, and assigns severity. An orchestrator aggregates results, applies configurable thresholds and quality gates, and posts inline comments, review statuses, or requests changes through the repository CLI or CI integration.

When to use it

  • On pull requests where multiple risk domains must be evaluated (security, perf, style, architecture)
  • For large or high-risk PRs that need comprehensive, parallelized review coverage
  • When you want automated, consistent enforcement of team coding standards and security rules
  • As part of CI pipelines to gate merges with measurable quality thresholds
  • To batch-analyze related PRs for integration and cross-change consistency

Best practices

  • Define clear review rules and thresholds (block vs warn vs suggest) in repo config
  • Limit agents per run to balance depth and latency for routine PRs; increase depth for critical paths
  • Provide examples and documentation for suggested fixes to reduce back-and-forth
  • Use incremental reviews and caching for large codebases to speed repeat runs
  • Treat agent findings as actionable suggestions; triage and tune models to reduce false positives

Example use cases

  • Security-critical change to authentication or payment code: run comprehensive security + architecture agents and require approval before merge
  • Performance optimization PR: spawn performance, database and benchmarking agents to compare against baseline and suggest optimizations
  • UI component library changes: run accessibility, style, i18n and visual-regression agents and post contextual inline comments
  • Batch analysis for a release: analyze multiple PRs together to detect cross-PR conflicts and integration risks
  • Automated CI gate: fail merges when security agent flags critical issues or when performance regressions exceed thresholds

FAQ

Can I customize which agents run per path or PR type?

Yes. Configure path-based triggers to select agents and depth per file patterns so only relevant specialists run.

How are critical findings enforced?

The orchestrator applies configured quality gates and can block merges, request changes, or add labels based on severity rules.