Autonomous Code Review

**Category:** Development

3,891 stars

Best use case

Autonomous Code Review is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

**Category:** Development

Teams using Autonomous Code Review should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/autonomous-code-review/SKILL.md --create-dirs "https://raw.githubusercontent.com/openclaw/skills/main/skills/1477009639zw-blip/autonomous-code-review/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/autonomous-code-review/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How Autonomous Code Review Compares

Feature / AgentAutonomous Code ReviewStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

**Category:** Development

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

Related Guides

SKILL.md Source

# Autonomous Code Review

**Category:** Development  
**Author:** Beta  
**Version:** 1.0.0  
**Runtime:** OpenClaw + Claude/GPT  

## What It Does

Automatically review code for bugs, security issues, performance problems, and style violations. Acts as a tireless first-pass reviewer on any codebase.

## When to Use

- After any significant code change
- Before merging pull requests
- During code review requests
- To catch issues before human reviewers

## Review Checklist

### 🔴 Critical (Block Merge)
- [ ] Security vulnerabilities (SQL injection, XSS, auth bypass)
- [ ] Data corruption risks (race conditions, deadlocks)
- [ ] Authentication/authorization bypasses
- [ ] Secrets hardcoded in source

### 🟡 Important (Should Fix)
- [ ] Performance issues (N+1 queries, inefficient loops)
- [ ] Error handling missing or insufficient
- [ ] Missing input validation
- [ ] Resource leaks (unclosed connections, files)

### 🟢建议 (Nice to Fix)
- [ ] Code style violations
- [ ] Missing documentation
- [ ] Hardcoded values that should be config
- [ ] Overly complex logic

## Usage

```bash
# Review a file
openclaw code review --file src/auth.py

# Review a diff
openclaw code review --diff "main..feature-branch"

# Full repository audit
openclaw code review --repo ./ --exclude "node_modules,dist"
```

## Integration

### GitHub Actions
```yaml
- name: Code Review
  uses: openclaw/code-review-action@v1
  with:
    api-key: ${{ secrets.OPENCLAW_API_KEY }}
```

### Pre-commit Hook
```bash
openclaw code review --staged --fail-on critical
```

## Output Format

```json
{
  "file": "src/auth.py",
  "issues": [
    {
      "severity": "critical",
      "line": 42,
      "rule": "sql-injection",
      "message": "User input directly interpolated into SQL query",
      "fix": "Use parameterized queries instead"
    }
  ],
  "score": 72,
  "summary": "1 critical, 2 important, 3 suggestions"
}
```

## Best Practices

- Run on every commit, not just before merges
- Combine with human review for critical paths
- Track review history to catch recurring issues
- Customize rules per project type

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