code-review

name

code-review

description

Comprehensive code review guidance for quality, performance, and architecture across all programming languages. Use when (1) User explicitly requests code review, (2) After writing significant code changes, (3) Before commits/PRs, (4) Reviewing existing codebases, (5) Analyzing code quality, (6) Detecting performance issues, (7) Identifying architectural problems, (8) Finding code smells. Provides automated analysis scripts and manual review checklists for thorough code evaluation.

Code Review

Comprehensive code review for quality, performance, and architecture across all languages.

Quick Start

Automated Review

Run complete analysis:

./scripts/review_code.sh /path/to/code

This runs:

• Language-specific linters (ESLint, Pylint, Clippy)
• Complexity analysis
• Code smell detection
• Report generation

Manual Review

Use comprehensive checklist:

• See review-checklist.md for complete checklist

Individual Analyzers

Complexity analysis:

./scripts/analyze_complexity.py /path/to/code

Code smells:

./scripts/detect_code_smells.py /path/to/code

When to Use

Trigger this skill when:

• User says "review this code" or similar
• After significant code implementation
• Before committing/creating PRs
• Analyzing unfamiliar codebases
• Investigating quality issues
• Optimizing performance
• Refactoring

What This Skill Provides

Automated Analysis Scripts

review_code.sh - Complete review orchestrator

• Detects project type (JS/TS/Python/Rust)
• Runs appropriate linters
• Analyzes complexity
• Detects code smells
• Generates comprehensive report

analyze_complexity.py - Complexity metrics

• Cyclomatic complexity
• Function length
• Nesting depth
• Identifies problematic functions

detect_code_smells.py - Code smell detection

• Long parameter lists
• Magic numbers
• Duplicated code
• Deep nesting
• God classes

generate_review_report.py - Report generation

• Aggregates all findings
• Prioritizes issues
• Provides recommendations

Manual Review Guidance

review-checklist.md - Complete review checklist

• Code quality checks
• Performance considerations
• Architecture review
• Security checklist
• Testing coverage

code-smells.md - Code smell catalog

• Common smells by category
• Detection strategies
• Refactoring solutions

Review Focus Areas

1. Code Quality

• Naming and readability
• DRY (Don't Repeat Yourself)
• YAGNI (You Aren't Gonna Need It)
• Consistent style
• Proper comments

2. Performance

• Algorithmic complexity
• Unnecessary loops/nesting
• Database query optimization
• Memory management
• Caching opportunities

3. Architecture

• Modularity and organization
• SOLID principles
• Coupling and cohesion
• Dependency management
• Design patterns

4. Code Smells

• Bloaters (long methods, large classes)
• Object-orientation abusers
• Change preventers
• Dispensables (dead code, duplication)
• Couplers (tight coupling)

Common Workflows

Workflow 1: Complete Project Review

# Run automated analysis
./scripts/review_code.sh /path/to/project

# Review output
cat .code-review-output/REVIEW.md

# Manual checklist review
# Consult references/review-checklist.md

# Address findings
# Refactor based on recommendations

Workflow 2: Quick Complexity Check

# Analyze complexity
./scripts/analyze_complexity.py /path/to/code

# Identify complex functions
# Refactor functions with complexity >10

Workflow 3: Pre-Commit Review

1. Run automated review
2. Check for high-severity issues
3. Review manual checklist
4. Ensure tests pass
5. Commit if approved

Workflow 4: Legacy Code Analysis

1. Run full review to understand codebase
2. Identify problematic areas
3. Prioritize refactoring
4. Address incrementally

Understanding Results

Complexity Metrics

Cyclomatic Complexity:

• 1-5: Simple, easy to test
• 6-10: Moderate complexity
• 11-20: High complexity, consider refactoring
• 21+: Very high, definitely refactor

Function Length:

• ≤20 lines: Excellent
• 21-50 lines: Good
• 51-100 lines: Consider splitting
• 100+ lines: Refactor

Nesting Depth:

• 1-2 levels: Good
• 3-4 levels: Acceptable
• 5+ levels: Refactor

Code Smell Severity

High - Address immediately:

• God classes
• Duplicated critical logic
• Deep nesting in hot paths

Medium - Address soon:

• Long parameter lists
• Feature envy
• Duplicated code

Low - Address when convenient:

• Magic numbers
• Minor duplication
• Speculative generality

Language Support

Detected Automatically

JavaScript/TypeScript:

• ESLint integration
• TypeScript compiler checks
• Complexity analysis
• Code smell detection

Python:

• Pylint integration
• Flake8 support
• MyPy type checking
• Complexity analysis

Rust:

• Clippy integration
• Cargo check
• Basic complexity analysis

Additional Languages

For languages not automatically detected:

• Manual checklist still applies
• Use language-specific linters
• Complexity principles are universal

Best Practices

1. Review Early - Don't wait until code is "complete"
2. Focus on Impact - High-severity issues first
3. Be Constructive - Suggest improvements, not just problems
4. Consider Context - Performance vs. readability tradeoffs
5. Automate - Use tools before manual review
6. Test Changes - Ensure refactoring doesn't break functionality
7. Document Decisions - Explain WHY, not just WHAT
8. Iterate - Re-review after changes

Integration

CI/CD Integration

Add to GitHub Actions:

- name: Code Review
  run: |
    ./scripts/review_code.sh .
    # Fail if high-severity issues found

Pre-commit Hook

#!/bin/bash
./scripts/review_code.sh . --quick

Editor Integration

Most linters integrate with VS Code, IntelliJ, etc.

Limitations

• Static Analysis Only - Doesn't catch runtime issues
• Heuristic-Based - Some false positives possible
• Language Coverage - Best for JS/TS/Python/Rust
• No Business Logic - Can't evaluate correctness
• Context Unaware - Doesn't understand project-specific conventions

Getting Help

For specific issues:

• Complexity problems → See complexity thresholds above
• Code smells → See code-smells.md
• General quality → See review-checklist.md

After review:

1. Address high-severity issues
2. Refactor complex functions
3. Remove code smells
4. Re-run review to verify

Example Output

# Code Review Report

## Executive Summary
- Total Functions Analyzed: 127
- Code Smells Detected: 23
  - High Severity: 3
  - Medium Severity: 15
  - Low Severity: 5

## Complexity Analysis
- Average Complexity: 4.2
- Maximum Complexity: 18
- Functions Needing Attention: 8

## Recommendations
- Refactor 3 highly complex functions
- Address 3 high-severity code smells
- Extract duplicated code in 5 locations

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Note: This skill provides guidance and automated checks. Final decisions on code quality depend on project context, team conventions, and business requirements.