Development

code-review - Claude MCP Skill

Perform thorough code reviews with security, performance, and maintainability analysis. Use when user asks to review code, check for bugs, or audit a codebase.

SEO Guide: Enhance your AI agent with the code-review tool. This Model Context Protocol (MCP) server allows Claude Desktop and other LLMs to perform thorough code reviews with security, performance, and maintainability analysis. use when use... Download and configure this skill to unlock new capabilities for your AI workflow.

🌟6323 stars • 4558 forks
📥0 downloads

Documentation

SKILL.md
# Code Review Skill

You now have expertise in conducting comprehensive code reviews. Follow this structured approach:

## Review Checklist

### 1. Security (Critical)

Check for:
- [ ] **Injection vulnerabilities**: SQL, command, XSS, template injection
- [ ] **Authentication issues**: Hardcoded credentials, weak auth
- [ ] **Authorization flaws**: Missing access controls, IDOR
- [ ] **Data exposure**: Sensitive data in logs, error messages
- [ ] **Cryptography**: Weak algorithms, improper key management
- [ ] **Dependencies**: Known vulnerabilities (check with `npm audit`, `pip-audit`)

```bash
# Quick security scans
npm audit                    # Node.js
pip-audit                    # Python
cargo audit                  # Rust
grep -r "password\|secret\|api_key" --include="*.py" --include="*.js"
```

### 2. Correctness

Check for:
- [ ] **Logic errors**: Off-by-one, null handling, edge cases
- [ ] **Race conditions**: Concurrent access without synchronization
- [ ] **Resource leaks**: Unclosed files, connections, memory
- [ ] **Error handling**: Swallowed exceptions, missing error paths
- [ ] **Type safety**: Implicit conversions, any types

### 3. Performance

Check for:
- [ ] **N+1 queries**: Database calls in loops
- [ ] **Memory issues**: Large allocations, retained references
- [ ] **Blocking operations**: Sync I/O in async code
- [ ] **Inefficient algorithms**: O(n^2) when O(n) possible
- [ ] **Missing caching**: Repeated expensive computations

### 4. Maintainability

Check for:
- [ ] **Naming**: Clear, consistent, descriptive
- [ ] **Complexity**: Functions > 50 lines, deep nesting > 3 levels
- [ ] **Duplication**: Copy-pasted code blocks
- [ ] **Dead code**: Unused imports, unreachable branches
- [ ] **Comments**: Outdated, redundant, or missing where needed

### 5. Testing

Check for:
- [ ] **Coverage**: Critical paths tested
- [ ] **Edge cases**: Null, empty, boundary values
- [ ] **Mocking**: External dependencies isolated
- [ ] **Assertions**: Meaningful, specific checks

## Review Output Format

```markdown
## Code Review: [file/component name]

### Summary
[1-2 sentence overview]

### Critical Issues
1. **[Issue]** (line X): [Description]
   - Impact: [What could go wrong]
   - Fix: [Suggested solution]

### Improvements
1. **[Suggestion]** (line X): [Description]

### Positive Notes
- [What was done well]

### Verdict
[ ] Ready to merge
[ ] Needs minor changes
[ ] Needs major revision
```

## Common Patterns to Flag

### Python
```python
# Bad: SQL injection
cursor.execute(f"SELECT * FROM users WHERE id = {user_id}")
# Good:
cursor.execute("SELECT * FROM users WHERE id = ?", (user_id,))

# Bad: Command injection
os.system(f"ls {user_input}")
# Good:
subprocess.run(["ls", user_input], check=True)

# Bad: Mutable default argument
def append(item, lst=[]):  # Bug: shared mutable default
# Good:
def append(item, lst=None):
    lst = lst or []
```

### JavaScript/TypeScript
```javascript
// Bad: Prototype pollution
Object.assign(target, userInput)
// Good:
Object.assign(target, sanitize(userInput))

// Bad: eval usage
eval(userCode)
// Good: Never use eval with user input

// Bad: Callback hell
getData(x => process(x, y => save(y, z => done(z))))
// Good:
const data = await getData();
const processed = await process(data);
await save(processed);
```

## Review Commands

```bash
# Show recent changes
git diff HEAD~5 --stat
git log --oneline -10

# Find potential issues
grep -rn "TODO\|FIXME\|HACK\|XXX" .
grep -rn "password\|secret\|token" . --include="*.py"

# Check complexity (Python)
pip install radon && radon cc . -a

# Check dependencies
npm outdated  # Node
pip list --outdated  # Python
```

## Review Workflow

1. **Understand context**: Read PR description, linked issues
2. **Run the code**: Build, test, run locally if possible
3. **Read top-down**: Start with main entry points
4. **Check tests**: Are changes tested? Do tests pass?
5. **Security scan**: Run automated tools
6. **Manual review**: Use checklist above
7. **Write feedback**: Be specific, suggest fixes, be kind

Signals

Avg rating0.0
Reviews0
Favorites0

Information

Repository
shareAI-lab/learn-claude-code
Author
shareAI-lab
Last Sync
3/12/2026
Repo Updated
3/12/2026
Created
1/12/2026

Reviews (0)

No reviews yet. Be the first to review this skill!