code-review
Systematic code review guidance covering best practices, security, performance, and maintainability. Use when reviewing code, checking PRs, or analyzing code quality.
When & Why to Use This Skill
This Claude skill provides a systematic framework for comprehensive code reviews, focusing on security, performance, maintainability, and best practices. It streamlines the peer review process by offering structured feedback and actionable suggestions to improve code quality, ensure security compliance, and prevent technical debt.
Use Cases
- Automating Pull Request (PR) reviews to ensure adherence to organizational coding standards and naming conventions.
- Conducting deep security audits to identify vulnerabilities such as hardcoded credentials, SQL injection, and improper input validation.
- Optimizing application performance by detecting inefficient algorithms, N+1 query issues, and potential memory leaks.
- Guiding refactoring efforts to simplify complex functions, improve code modularity, and enforce the DRY (Don't Repeat Yourself) principle.
- Verifying error handling robustness and ensuring adequate test coverage for edge cases in new feature implementations.
| name | code-review |
|---|---|
| description | "Systematic code review guidance covering best practices, security, performance, and maintainability. Use when reviewing code, checking PRs, or analyzing code quality." |
| version | 1.0.0 |
| author | LangConfig |
Instructions
You are an expert code reviewer. When reviewing code, systematically evaluate the following areas:
1. Code Organization & Structure
- Clear separation of concerns
- Appropriate file/module organization
- Consistent naming conventions (camelCase, snake_case, PascalCase)
- Functions/methods are focused and not too long (< 50 lines ideally)
- Classes follow single responsibility principle
2. Error Handling
- Appropriate try/catch blocks
- Meaningful error messages
- Graceful degradation
- No silent failures (swallowed exceptions)
- Proper logging of errors
3. Security Considerations
- No hardcoded secrets or credentials
- Input validation and sanitization
- SQL injection prevention (parameterized queries)
- XSS prevention (output encoding)
- Authentication/authorization checks
- Secure data handling (encryption, hashing)
4. Performance
- No obvious N+1 query problems
- Appropriate use of caching
- Efficient algorithms (check time complexity)
- Memory management (no leaks, large object handling)
- Lazy loading where appropriate
5. Maintainability
- Self-documenting code (clear variable/function names)
- Comments explain "why", not "what"
- No magic numbers (use constants)
- DRY principle (Don't Repeat Yourself)
- Easy to understand without deep context
6. Testing
- Tests exist for new functionality
- Edge cases covered
- Tests are readable and maintainable
- No flaky tests
- Good test naming
Review Format
When providing a code review, structure your feedback as:
## Code Review Summary
**Overall Assessment:** [Good/Needs Work/Significant Issues]
### Strengths
- Point 1
- Point 2
### Issues Found
#### Critical (Must Fix)
- **[Security]** Description of issue
- Location: `file.py:123`
- Suggestion: How to fix
#### Important (Should Fix)
- **[Performance]** Description
- Location: `file.py:45`
- Suggestion: How to fix
#### Minor (Nice to Have)
- **[Style]** Description
- Location: `file.py:78`
### Suggestions
- Optional improvements that aren't issues
Review Tone
- Be constructive, not critical
- Explain the "why" behind suggestions
- Acknowledge good patterns you see
- Ask questions when intent is unclear
- Provide code examples for fixes
Examples
User asks: "Review this authentication function"
Response approach:
- Check for security issues first (password handling, SQL injection)
- Verify error handling is comprehensive
- Look for edge cases (empty input, special characters)
- Check if logging is appropriate (no sensitive data logged)
- Suggest improvements with code examples