name: code-review description: Performs comprehensive code reviews focusing on correctness, maintainability, performance, and best practices. Trigger keywords: review, code review, PR review, pull request, check code, audit code. allowed-tools: Read, Grep, Glob, Bash

Code Review

Overview

This skill provides thorough code review capabilities, analyzing code for bugs, design issues, performance problems, and adherence to best practices. It helps identify potential issues before they reach production.

Instructions

1. Gather Context

• Identify the files to review using Glob patterns
• Understand the project structure and conventions
• Check for existing linting/formatting rules

2. Analyze Code Structure

• Review file organization and module structure
• Check for proper separation of concerns
• Verify naming conventions are consistent

3. Check for Common Issues

• Logic errors and edge cases
• Error handling completeness
• Resource management (memory leaks, unclosed handles)
• Thread safety issues in concurrent code
• Input validation gaps

4. Evaluate Code Quality

• Readability and clarity
• DRY principle adherence
• SOLID principles compliance
• Appropriate abstraction levels
• Test coverage adequacy

5. Performance Review

• Algorithm complexity analysis
• Database query efficiency
• Memory usage patterns
• Caching opportunities

Best Practices

1. Be Specific: Point to exact lines and provide concrete suggestions
2. Prioritize Issues: Distinguish between critical bugs and style preferences
3. Explain Why: Don't just say what's wrong, explain the reasoning
4. Suggest Solutions: Provide alternative implementations when possible
5. Acknowledge Good Code: Recognize well-written sections
6. Consider Context: Understand the constraints and trade-offs
7. Be Constructive: Frame feedback positively and professionally

Examples

Example 1: Reviewing a Python Function

# Before Review
def process(data):
    result = []
    for item in data:
        if item['status'] == 'active':
            result.append(item['value'] * 2)
    return result

# Review Comments:
# 1. Function name is too generic - consider 'double_active_values'
# 2. No type hints - add typing for better maintainability
# 3. No docstring explaining purpose and parameters
# 4. No null/empty check on input data
# 5. Could use list comprehension for cleaner code

# After Review
def double_active_values(data: list[dict]) -> list[int]:
    """
    Doubles the values of all active items in the dataset.

    Args:
        data: List of dictionaries with 'status' and 'value' keys

    Returns:
        List of doubled values for active items
    """
    if not data:
        return []
    return [item['value'] * 2 for item in data if item.get('status') == 'active']

Example 2: Security Review Flag

// CRITICAL: SQL Injection vulnerability
const query = `SELECT * FROM users WHERE id = ${userId}`;

// Recommendation: Use parameterized queries
const query = "SELECT * FROM users WHERE id = ?";
db.query(query, [userId]);

Example 3: Performance Review

# Issue: O(n*m) complexity due to nested loops with list membership check
for user in users:
    if user.id in active_ids:  # O(n) lookup each time
        process(user)

# Recommendation: Convert to set for O(1) lookups
active_ids_set = set(active_ids)
for user in users:
    if user.id in active_ids_set:
        process(user)