AG

agent-code-review-swarm

Automates multi-agent code reviews to detect security vulnerabilities, performance issues, and architecture patterns.

Install

mkdir -p .claude/skills/agent-code-review-swarm && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/1664" && unzip -o skill.zip -d .claude/skills/agent-code-review-swarm && rm skill.zip

Installs to .claude/skills/agent-code-review-swarm

Activation

This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.

Agent skill for code-review-swarm - invoke with $agent-code-review-swarm
72 charsno explicit “when” trigger
Intermediate

Key capabilities

  • Automated multi-agent code review
  • Security vulnerability analysis
  • Performance bottleneck detection
  • Architecture pattern validation
  • Style and convention enforcement

How it works

The tool initializes a swarm of specialized agents that analyze code changes based on defined configurations and thresholds. It uses the GitHub CLI to fetch PR context and post findings back to the repository.

Inputs & outputs

You give it
GitHub pull request number and associated diff data
You get back
Review comments, status updates, and quality gate evaluations posted to GitHub

When to use agent-code-review-swarm

  • Performing automated PR reviews
  • Analyzing security vulnerabilities
  • Detecting performance bottlenecks
  • Validating architecture patterns

About this skill


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

Content truncated.

When not to use it

  • Projects lacking GitHub CLI authentication
  • Environments without access to npx

Prerequisites

GitHub CLIGitHub authentication

Limitations

  • Requires manual configuration of review thresholds and agent rules
  • Performance depends on the complexity of the codebase and PR size

How it compares

Unlike standard static analysis tools, this system orchestrates multiple specialized AI agents to provide context-aware feedback and automated suggestions across security, performance, and architecture domains.

Compared to similar skills

agent-code-review-swarm side by side with the closest alternatives in the catalog.

SkillInstallsUpdatedSafetyDifficulty
agent-code-review-swarm (this skill)36moReviewIntermediate
audit-project04moReviewAdvanced
agent-reviewer02moReviewIntermediate
claude-automation-recommender472moReviewBeginner

Try saying

Example prompts that trigger this skill in your AI assistant.

You might also like

audit-project

agent-sh

Use when user asks to 'review my code', 'audit the codebase', 'run code review', 'check for issues', 'find bugs', 'security review', 'performance review', or wants multi-agent iterative review. Spawns role-based reviewers (code-quality-reviewer, security-expert, performance-engineer, test-quality-gu

00

agent-reviewer

ynitto

入力された成果物を分析し、適切な perspectives を自律選択してサブエージェントを並列起動し、集約レビューを返す。「レビューして」「コードを確認して」「設計をレビューして」「ドキュメントをチェックして」「品質確認して」などの依頼で発動。sprint-reviewer は含まない。

00

claude-automation-recommender

anthropics

Analyze a codebase and recommend Claude Code automations (hooks, subagents, skills, plugins, MCP servers). Use when user asks for automation recommendations, wants to optimize their Claude Code setup, mentions improving Claude Code workflows, asks how to first set up Claude Code for a project, or wants to know what Claude Code features they should use.

47140

github-code-review

ruvnet

Comprehensive GitHub code review with AI-powered swarm coordination

13135

reviewing-code

CaptainCrouton89

Systematically evaluate code changes for security, correctness, performance, and spec alignment. Use when reviewing PRs, assessing code quality, or verifying implementation against requirements.

21105

reviewing-nextjs-16-patterns

djankies

Review code for Next.js 16 compliance - security patterns, caching, breaking changes. Use when reviewing Next.js code, preparing for migration, or auditing for violations.

11106

Search skills

Search the agent skills registry