github-code-review
Deploy specialized AI agents to perform comprehensive, intelligent code reviews that go beyond traditional static analysis. Use for automated multi-agent review, security vulnerability analysis, performance bottleneck detection, and architecture pattern validation.
Installation
Suivez les instructions du dépôt pour votre agent. Consultez les fichiers et dépendances avant l’installation.
Voir la source ↗GuidesInstructions
GitHub Code Review Skill
Overview
Deploy specialized AI agents for comprehensive code reviews. This skill provides multi-agent review capabilities covering security vulnerabilities, performance bottlenecks, architecture patterns, and code style enforcement.
Quick Start
# Get PR details for review
gh pr view 123 --json files,additions,deletions,title,body
# Get PR diff
gh pr diff 123
# Post review comment
gh pr review 123 --comment --body "Review findings..."
# Approve PR
gh pr review 123 --approve --body "LGTM!"
# Request changes
gh pr review 123 --request-changes --body "Please fix..."
When to Use
- Automated code review for pull requests
- Security vulnerability analysis
- Performance bottleneck detection
- Architecture pattern validation
- Style and convention enforcement
- Multi-agent collaborative review
Review Agents
Security Agent
| Check | Description |
|---|---|
| SQL injection | Detect SQL injection vulnerabilities |
| XSS | Cross-site scripting attack vectors |
| Authentication | Auth bypasses and flaws |
| Cryptographic | Weak crypto implementations |
| Secrets | Exposed credentials or API keys |
| CORS | Misconfiguration issues |
Performance Agent
| Metric | Description |
|---|---|
| Algorithm complexity | Big-O analysis |
| Query efficiency | N+1 queries, slow queries |
| Memory patterns | Allocation and leaks |
| Cache utilization | Caching opportunities |
| Bundle size | Impact on bundle size |
Style Agent
| Check | Description |
|---|---|
| Code formatting | Consistent formatting |
| Naming conventions | Variable/function naming |
| Documentation | Comment quality |
| Test coverage | Missing tests |
| Error handling | Proper error patterns |
Architecture Agent
| Pattern | Description |
|---|---|
| SOLID principles | Single responsibility, etc. |
| DRY violations | Code duplication |
| Coupling metrics | Component dependencies |
| Layer violations | Architecture boundaries |
| Circular dependencies | Dependency cycles |
Usage Examples
1. Multi-Agent Review System
# 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" \
--depth comprehensive
# Post initial review status
gh pr comment 123 --body "Multi-agent code review initiated"
2. Security-Focused Review
# 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 "$CHANGED_FILES" \
--check "owasp,cve,secrets,permissions" \
--suggest-fixes)
# Post findings based on severity
if echo "$SECURITY_RESULTS" | grep -q "critical"; then
gh pr review 123 --request-changes --body "$SECURITY_RESULTS"
gh pr edit 123 --add-label "security-review-required"
else
gh pr comment 123 --body "$SECURITY_RESULTS"
fi
3. Initialize Review Swarm
// Initialize code review swarm
mcp__claude-flow__swarm_init({ topology: "hierarchical", maxAgents: 5 })
mcp__claude-flow__agent_spawn({ type: "reviewer", name: "Security Reviewer" })
mcp__claude-flow__agent_spawn({ type: "reviewer", name: "Performance Reviewer" })
mcp__claude-flow__agent_spawn({ type: "reviewer", name: "Style Reviewer" })
mcp__claude-flow__agent_spawn({ type: "architect", name: "Architecture Reviewer" })
// Orchestrate parallel review
mcp__claude-flow__task_orchestrate({
task: "Comprehensive code review covering security, performance, style, and architecture",
strategy: "parallel",
priority: "high"
})
4. Post Inline Comments
# Get 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 "$PR_FILES" \
--style "constructive" \
--include-examples \
--suggest-fixes)
# Post inline comments
echo "$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')
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
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
MCP Tool Integration
Swarm Coordination
// Initialize review swarm
mcp__claude-flow__swarm_init({
topology: "hierarchical",
maxAgents: 5,
strategy: "specialized"
})
// Spawn specialized reviewers
mcp__claude-flow__agents_spawn_parallel({
agents: [
{ type: "reviewer", name: "security-agent", capabilities: ["security-audit"] },
{ type: "reviewer", name: "perf-agent", capabilities: ["performance-analysis"] },
{ type: "reviewer", name: "style-agent", capabilities: ["code-style"] }
]
})
Memory for Review State
// Store review findings
mcp__claude-flow__memory_usage({
action: "store",
key: "review/pr-123/findings",
value: JSON.stringify({
security: { issues: 2, severity: "medium" },
performance: { issues: 1, severity: "low" },
style: { issues: 5, severity: "info" }
})
})
GitHub Actions Integration
# .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 "${{ secrets.GITHUB_TOKEN }}" | gh auth login --with-token
- name: Run Review Swarm
run: |
PR_NUM=${{ github.event.pull_request.number }}
PR_DATA=$(gh pr view $PR_NUM --json files,title,body,labels)
REVIEW_OUTPUT=$(npx ruv-swarm github review-all \
--pr $PR_NUM \
--pr-data "$PR_DATA" \
--agents "security,performance,style,architecture")
echo "$REVIEW_OUTPUT" | gh pr review $PR_NUM --comment -F -
if echo "$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
Comment Templates
Security Issue
**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:
### Performance Issue
```markdown
**Performance Issue: [Type]**
**Impact**: [Expected performance degradation]
**Current**:
```language
[Current code]
Suggested:
[Optimized code]
## 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
---
## Version History
- **1.0.0** (2025-01-02): Initial release - converted from code-review-swarm agent
Exemples
Consultez les exemples du dépôt source.
Voir la source ↗Historique source
- Date d’indexation
- 2026-01-20
- Mise à jour de l’index
- 2026-01-26
- Branch
- main
Les dates concernent ce répertoire. Consultez le dépôt pour l’historique du code.
GitHub ↗chat Comments (0)
Sign in to join the discussion and leave a comment.
No comments yet. Be the first to share your thoughts!