
Agent Code Review Swarm
- 1k installs
- 67k repo stars
- Updated August 4, 2026
- ruvnet/ruflo
agent-code-review-swarm is a ruflo Claude Code skill that orchestrates multiple specialized AI agents to perform deep code reviews on pull requests for developers who need automated multi-perspective review beyond static
About
agent-code-review-swarm is a ruflo skill that deploys specialized AI agents through claude-flow MCP tools including swarm_init, agent_spawn, and task_orchestrate to perform comprehensive pull request reviews. Capabilities include automated multi-agent code review, security vulnerability analysis, performance bottleneck detection, architecture pattern validation, and style and convention enforcement. The skill uses Bash, Read, Write, and TodoWrite alongside MCP orchestration to go beyond traditional static analyzers. Developers invoke it with $agent-code-review-swarm when a PR needs parallel expert passes across security, performance, and architecture concerns before merge.
- Automated multi-agent code review
- Security vulnerability analysis
- Performance bottleneck detection
- Architecture pattern validation
- Style and convention enforcement
Agent Code Review Swarm by the numbers
- 1,014 all-time installs (skills.sh)
- +3 installs in the week ending Aug 5, 2026 (Skillselion tracking)
- Ranked #126 of 1,352 Code Review & Quality skills by installs in the Skillselion catalog
- Security screen: MEDIUM risk (skills.sh audit)
- Data as of Aug 5, 2026 (Skillselion catalog sync)
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| Installs | 1k |
|---|---|
| repo stars | ★ 67k |
| Security audit | 1 / 3 scanners passed |
| Last updated | August 4, 2026 |
| Repository | ruvnet/ruflo ↗ |
How do you run multi-agent code review on PRs?
Automatically orchestrate multiple specialized AI agents that perform deep, intelligent code reviews on pull requests.
Who is it for?
Teams using ruflo and claude-flow who want orchestrated multi-agent PR reviews beyond a single static analyzer pass.
Skip if: Simple lint-only checks or projects without claude-flow MCP orchestration should skip agent-code-review-swarm.
When should I use this skill?
A pull request needs comprehensive multi-agent review covering security vulnerabilities, performance bottlenecks, and architecture patterns.
What you get
Multi-agent PR review findings covering security, performance, architecture, and style
- Structured multi-agent review findings
- Security and performance issue list
By the numbers
- Lists five core review capabilities including security, performance, architecture, and style analysis
Files
--- 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 "$SECURITY_RESULTS"
fiPerformance Agent
# Performance analysis
npx ruv-swarm github review-performance \
--pr 123 \
--profile "cpu,memory,io" \
--benchmark-against main \
--suggest-optimizationsArchitecture Agent
# Architecture review
npx ruv-swarm github review-architecture \
--pr 123 \
--check "patterns,coupling,cohesion,solid" \
--visualize-impact \
--suggest-refactoring3. Review Configuration
# .github$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-patternsReview 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-changes2. Learning from History
# Learn from past reviews
npx ruv-swarm github review-learn \
--analyze-past-reviews \
--identify-patterns \
--improve-suggestions \
--reduce-false-positives3. Cross-PR Analysis
# Analyze related PRs together
npx ruv-swarm github review-batch \
--prs "123,124,125" \
--check-consistency \
--verify-integration \
--combined-impactReview 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 $PR_NUM --request-changes -b "See review comments above"
fiReview Triggers
// Custom review triggers
{
"triggers": {
"high-risk-files": {
"paths": ["**$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="$(gh pr view 123 --json headRefOid -q .headRefOid)"
doneComment Templates
<!-- Security Issue Template -->
🔒 **Security Issue: [Type]**
**Severity**: 🔴 Critical / 🟡 High / 🟢 Low
**Description**:
[Clear explanation of the security issue]
**Impact**:
[Potential consequences if not addressed]
**Suggested Fix**:[Code example of the fix]
**References**:
- [OWASP Guide](link)
- [Security Best Practices](link)Batch Comment Management
# Manage review comments efficiently
npx ruv-swarm github review-comments \
--pr 123 \
--group-by "agent,severity" \
--summarize \
--resolve-outdatedIntegration with CI/CD
Status Checks
# Required status checks
protection_rules:
required_status_checks:
contexts:
- "review-swarm$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-dashboardBest 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-accuracy2. 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-criticalExamples
Security-Critical PR
# Auth system changes
npx ruv-swarm github review-init \
--pr 456 \
--agents "security,authentication,audit" \
--depth "maximum" \
--require-security-approvalPerformance-Sensitive PR
# Database optimization
npx ruv-swarm github review-init \
--pr 789 \
--agents "performance,database,caching" \
--benchmark \
--profileUI Component PR
# New component library
npx ruv-swarm github review-init \
--pr 321 \
--agents "accessibility,style,i18n,docs" \
--visual-regression \
--component-testsMonitoring & 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-stakeholdersSee also: swarm-pr.md, workflow-automation.md
Related skills
How it compares
Pick agent-code-review-swarm when a PR needs parallel specialized agent passes rather than a single linter or one-shot review prompt.
FAQ
What MCP tools does agent-code-review-swarm use?
agent-code-review-swarm orchestrates reviews through claude-flow MCP tools including swarm_init, agent_spawn, and task_orchestrate, combined with Bash, Read, Write, and TodoWrite for repository analysis.
What review dimensions does agent-code-review-swarm cover?
agent-code-review-swarm deploys agents for security vulnerability analysis, performance bottleneck detection, architecture pattern validation, and style and convention enforcement on pull request changes.
Is Agent Code Review Swarm safe to install?
skills.sh reports 1 of 3 security scanners passed. Review the Security Audits panel on this page before installing in production.