Code Reviewer

Comprehensive automated code review skill that systematically analyzes code for security issues, quality metrics, performance problems, and adherence to best practices.

Purpose

This skill provides structured code review workflows that combine automated analysis tools with expert guidance to identify issues across security, quality, performance, and maintainability dimensions.

When to Use This Skill

Use this skill when:

• Reviewing pull requests or code submissions
• Conducting security audits of existing codebases
• Evaluating code quality before deployment
• Identifying technical debt and refactoring opportunities
• Establishing code review standards for teams
• Learning what to look for in code reviews

Core Review Workflow

Phase 1: Initial Analysis

1.1 Understand the Context

• Read the PR description or change summary
• Identify the type of change (feature, bug fix, refactor, security patch)
• Determine the scope and affected components
• Note any related issues or tickets

1.2 Code Overview

• Review file changes and additions/deletions
• Identify changed modules and their relationships
• Look for unexpected changes or scope creep
• Check for breaking changes

Phase 2: Security Review

2.1 Common Vulnerability Patterns

Check for these critical security issues:

Input Validation

• Unvalidated user input reaching sensitive operations
• SQL injection vulnerabilities
• Command injection possibilities
• Path traversal attacks
• XML/XXE injection points

Authentication & Authorization

• Missing authentication checks
• Broken access control
• Insecure password storage
• Weak session management
• Missing CSRF protection

Data Exposure

• Hardcoded credentials or API keys
• Sensitive data in logs
• Inadequate encryption
• Information disclosure in error messages
• Exposed configuration files

Code Injection

• Unsafe deserialization
• Template injection
• Code evaluation from user input
• Unsafe reflection usage

2.2 Automated Security Scanning

Use security analysis tools:

Python:

# Run bandit for security issues
python scripts/review_helper.py --security-scan path/to/code

# Check dependencies for known vulnerabilities
safety check
pip-audit

JavaScript/Node.js:

# Check for vulnerabilities
npm audit
yarn audit

# Use ESLint security plugins
eslint --plugin security path/to/code

Go:

# Security scanning
gosec ./...

See references/security_patterns.md for detailed vulnerability patterns.

Phase 3: Code Quality Analysis

3.1 Code Structure

Modularity & Organization

• Single Responsibility Principle adherence
• Proper separation of concerns
• Appropriate abstraction levels
• Clear module boundaries
• Logical file organization

Complexity Metrics

• Cyclomatic complexity (target: < 10 per function)
• Function length (target: < 50 lines)
• Class size (target: < 300 lines)
• Nesting depth (target: < 4 levels)
• Parameter count (target: < 5 parameters)

Code Smells

• Duplicated code
• Long methods or god classes
• Feature envy (method uses more of another class)
• Data clumps (repeated parameter groups)
• Primitive obsession
• Inappropriate intimacy between classes

3.2 Naming and Readability

Naming Conventions

• Descriptive, intention-revealing names
• Consistent naming patterns
• Appropriate length (not too short, not too long)
• Avoid abbreviations unless standard
• Boolean names start with is/has/should/can

Code Clarity

• Clear control flow
• Minimal cognitive load
• Self-documenting code
• Appropriate comments (why, not what)
• Consistent formatting

3.3 Error Handling

Robustness

• Proper exception handling
• No bare except/catch blocks
• Appropriate error messages
• Resource cleanup (file handles, connections)
• Graceful degradation

Edge Cases

• Null/None checks
• Empty collection handling
• Boundary conditions
• Concurrent access issues
• Race condition prevention

Phase 4: Performance Review

4.1 Common Performance Issues

Algorithm Efficiency

• O(n²) or worse algorithms when better exists
• Unnecessary loops or iterations
• Inefficient data structure usage
• Missing memoization/caching opportunities

Resource Management

• Memory leaks
• Unclosed file handles or connections
• Excessive memory allocation
• Thread/process pool exhaustion

Database Operations

• N+1 query problems
• Missing indexes
• SELECT * usage
• Inefficient JOIN operations
• Missing query optimization

Network Calls

• Synchronous blocking calls
• Missing timeout configurations
• No retry logic
• Excessive API calls
• Missing connection pooling

See references/performance_guide.md for optimization strategies.

Phase 5: Testing Assessment

5.1 Test Coverage

Coverage Metrics

• Line coverage (target: > 80%)
• Branch coverage (target: > 75%)
• Function coverage (target: > 90%)
• Critical path coverage (target: 100%)

Test Quality

• Tests actually assert meaningful behavior
• Tests are independent and isolated
• Test names clearly describe what they test
• Proper use of mocks and stubs
• No test interdependencies

5.2 Test Completeness

Required Test Types

• Unit tests for business logic
• Integration tests for component interaction
• Edge case and boundary tests
• Error condition tests
• Security-related tests

Missing Tests

• Untested error paths
• Missing negative test cases
• Uncovered edge conditions
• No regression tests for bug fixes

Phase 6: Documentation Review

6.1 Code Documentation

Function/Method Documentation

• Purpose and behavior description
• Parameter descriptions with types
• Return value documentation
• Exception documentation
• Usage examples for complex APIs

Module/Class Documentation

• High-level purpose
• Architecture overview
• Design decisions
• Dependencies
• Public API contracts

6.2 External Documentation

README Updates

• Installation instructions
• Configuration changes
• New feature documentation
• Breaking change notices
• Migration guides

API Documentation

• Endpoint descriptions
• Request/response formats
• Authentication requirements
• Error responses
• Rate limiting

Review Checklist

Use this checklist to ensure comprehensive review:

Security

• No hardcoded credentials or secrets
• Input validation on all user inputs
• Proper authentication and authorization
• No SQL/command injection vulnerabilities
• Secure password handling
• HTTPS/TLS for sensitive data
• Security scanning tools executed
• Dependencies checked for vulnerabilities

Code Quality

• Functions follow Single Responsibility Principle
• Cyclomatic complexity under 10
• No code duplication
• Consistent naming conventions
• Proper error handling
• No TODO/FIXME without tickets
• Code is self-documenting

Performance

• No obvious performance bottlenecks
• Efficient algorithms and data structures
• Proper resource cleanup
• Database queries optimized
• No N+1 query problems
• Appropriate caching strategies

Testing

• Tests included for new functionality
• Edge cases covered
• Test coverage meets standards
• Tests are independent and repeatable
• No flaky tests introduced

Documentation

• Public APIs documented
• Complex logic explained
• README updated if needed
• Breaking changes documented
• Migration guide provided if needed

Using the Review Helper Script

The scripts/review_helper.py provides automated analysis:

# Full code review analysis
python scripts/review_helper.py --file path/to/file.py --report full

# Security-focused scan
python scripts/review_helper.py --security-scan path/to/directory

# Complexity analysis
python scripts/review_helper.py --complexity path/to/file.py

# Generate review report
python scripts/review_helper.py --file path/to/file.py --output report.md

Best Practices

For Reviewers

Be Constructive

• Focus on improvement, not criticism
• Explain the "why" behind suggestions
• Offer alternatives or solutions
• Recognize good code and patterns

Be Thorough but Efficient

• Use automated tools for mechanical checks
• Focus human review on logic and design
• Don't bikeshed on style issues (use linters)
• Prioritize security and correctness over style

Be Consistent

• Apply the same standards to all code
• Reference team coding standards
• Create reusable review templates
• Document common feedback patterns

For Code Authors

Prepare for Review

• Self-review before requesting review
• Run linters and formatters
• Execute test suite
• Add context in PR description
• Keep changes focused and small

Respond to Feedback

• Address all comments
• Ask questions if unclear
• Don't take feedback personally
• Mark conversations as resolved

Common Review Feedback Patterns

Security Issues

❌ Security: Hardcoded API key found
→ Move to environment variable or secrets management
→ See: references/security_patterns.md#secrets-management

❌ Security: SQL injection vulnerability
→ Use parameterized queries instead of string concatenation
→ Example: cursor.execute("SELECT * FROM users WHERE id = %s", (user_id,))

Quality Issues

❌ Quality: Function complexity too high (complexity: 15)
→ Break down into smaller, focused functions
→ Target: < 10 cyclomatic complexity

❌ Quality: Duplicated code across 3 locations
→ Extract common logic into shared function
→ DRY principle violation

Performance Issues

❌ Performance: N+1 query problem detected
→ Use JOIN or eager loading instead
→ See: references/performance_guide.md#database-optimization

❌ Performance: Inefficient O(n²) algorithm
→ Consider using set/hash for O(1) lookup
→ Current: nested loops, Suggested: set intersection

Additional Resources

• Security Patterns: references/security_patterns.md - Common vulnerabilities and fixes
• Performance Guide: references/performance_guide.md - Optimization strategies
• Review Checklist: examples/review_checklist.md - Comprehensive review template
• Helper Scripts: scripts/review_helper.py - Automated analysis tools

Language-Specific Considerations

Python

• Check for proper use of context managers (with statements)
• Verify list comprehensions aren't overly complex
• Look for opportunities to use generators
• Check for mutable default arguments

JavaScript/TypeScript

• Verify proper async/await usage
• Check for callback hell
• Look for memory leaks in event listeners
• Verify proper typing in TypeScript

Java

• Check for proper exception handling
• Verify resource cleanup (try-with-resources)
• Look for proper use of immutability
• Check for thread safety issues

Go

• Check for proper error handling (not ignoring errors)
• Verify goroutine leak prevention
• Look for race conditions
• Check for proper context usage

Conclusion

Effective code review combines automated tooling with human expertise. Use automated tools for mechanical checks (security, style, complexity) and focus human review on logic, design, and maintainability. Always be constructive, thorough, and consistent in your reviews.