Imported from David-Li0406/meta-skill-evloving (skill-flow/data/skills-refined-autoskill-36k/skillsmp/code-review-helper-1/SKILL.md). Install upstream with npx skills add David-Li0406/meta-skill-evloving --skill code-review-helper-1. Copyright stays with the author.

Code Review Helper Skill

Systematic code review workflows with utilities for thorough, consistent reviews.

Overview

This skill provides structured workflows and utilities for conducting high-quality code reviews. It emphasizes:

• Systematic analysis across multiple dimensions
• Automated pattern detection
• Structured feedback format
• Constructive, actionable suggestions

Review Process

1. Initial Context Gathering

Before reviewing, understand the change:

# View PR metadata
gh pr view <pr-number> --repo <owner/repo>

# Check file changes
gh pr diff <pr-number> --repo <owner/repo>

# Review CI status
gh pr checks <pr-number> --repo <owner/repo>

2. Systematic Analysis

Review across these dimensions:

Correctness

• Does the code do what it claims?
• Are edge cases handled?
• Are error conditions addressed?

Clarity

• Is the code easy to understand?
• Are names descriptive?
• Is complexity justified?

Testing

• Are there adequate tests?
• Do tests cover edge cases?
• Are tests clear and maintainable?

Documentation

• Is public API documented?
• Are complex algorithms explained?
• Are assumptions stated?

Performance

• Are there obvious inefficiencies?
• Are large operations appropriate?
• Is caching used where beneficial?

Security

• Are inputs validated?
• Are sensitive data handled properly?
• Are dependencies trustworthy?

3. Pattern Detection

Use bundled utilities to detect common issues:

# Import review helpers
from review_helpers import (
    check_naming_conventions,
    detect_code_smells,
    analyze_complexity,
    find_duplicate_code,
    check_test_coverage
)

# Analyze code
naming_issues = check_naming_conventions("path/to/file.py")
smells = detect_code_smells("path/to/file.py")
complexity = analyze_complexity("path/to/file.py")
duplicates = find_duplicate_code("src/")
coverage = check_test_coverage("tests/", "src/")

# Report findings
for issue in naming_issues + smells:
    print(f"⚠️ {issue}")

4. Structured Feedback Format

Provide feedback in this format:

## Summary
[Brief overview of the PR and general assessment]

## Strengths
- [Positive aspect 1]
- [Positive aspect 2]

## Issues Found

### Critical
- [ ] **[File:Line]**: [Description]
  - Why: [Explanation]
  - Fix: [Suggestion]

### Important
- [ ] **[File:Line]**: [Description]
  - Why: [Explanation]
  - Fix: [Suggestion]

### Minor
- [ ] **[File:Line]**: [Description]
  - Why: [Explanation]
  - Fix: [Suggestion]

## Suggestions
- [Non-blocking improvement 1]
- [Non-blocking improvement 2]

## Questions
- [Clarification needed 1]
- [Clarification needed 2]

## Overall Assessment
[Approve / Request Changes / Comment]

Bundled Utilities

review_helpers.py

Provides automated analysis functions:

check_naming_conventions(filepath)

• Validates Python naming conventions
• Checks for PEP 8 compliance
• Returns list of naming issues

detect_code_smells(filepath)

• Identifies common anti-patterns
• Detects magic numbers
• Finds long functions/classes
• Reports deeply nested code

analyze_complexity(filepath)

• Calculates cyclomatic complexity
• Identifies complex functions
• Suggests refactoring candidates

find_duplicate_code(directory)

• Detects code duplication
• Uses AST-based analysis
• Reports similar code blocks

check_test_coverage(test_dir, source_dir)

• Analyzes test-to-code ratio
• Identifies untested code paths
• Suggests missing test cases

Common Review Patterns

Pattern: Security-Sensitive Code

When reviewing authentication, authorization, or data handling:

**Security Checklist**:
- [ ] Input validation present
- [ ] SQL injection prevention
- [ ] XSS prevention (if web)
- [ ] Secrets not hardcoded
- [ ] Sensitive data encrypted
- [ ] Access control enforced
- [ ] Audit logging included

Pattern: Performance-Critical Code

When reviewing loops, database queries, or large data operations:

**Performance Checklist**:
- [ ] N+1 queries avoided
- [ ] Appropriate indexes used
- [ ] Caching considered
- [ ] Bulk operations used where possible
- [ ] Memory usage reasonable
- [ ] Algorithmic complexity acceptable

Pattern: API Changes

When reviewing public API modifications:

**API Checklist**:
- [ ] Backward compatibility maintained or migration path provided
- [ ] Documentation updated
- [ ] Examples provided
- [ ] Error handling clear
- [ ] Type hints complete
- [ ] Deprecation warnings added if needed

Best Practices

Be Constructive

Focus on improvement, not criticism:

# Unhelpful
This code is terrible.

# Helpful
This function is hard to test due to tight coupling. Consider using dependency injection to improve testability.

Provide Context

Explain why something matters:

# Incomplete
This variable name is bad.

# Complete
The variable name 'x' is unclear. Consider 'customer_count' which better expresses the domain concept and improves readability.

Suggest Alternatives

When requesting changes, show how:

# Vague
This needs to be refactored.

# Specific
Consider extracting this logic into a separate function for better testability and reuse.

Prioritize Issues

Distinguish between blocking and non-blocking feedback:

**Blocking**: Security vulnerability in password handling (line 42)
**Important**: Missing input validation (line 67)
**Nice-to-have**: Consider extracting helper function (line 123)
**Question**: Why use X instead of Y here? (line 89)

Integration with Other Tools

With Shell Tool

# Check for common issues
rg "TODO|FIXME|XXX" --type py src/
grep -r "import \*" src/
find src/ -name "*.py" -exec wc -l {} + | sort -rn | head -10

With Patch Tool

<<<<<<< ORIGINAL
def process_data(data):
    result = []
    for item in data:
        result.append(transform(item))
    return result
=======
def process_data(data):
    """Process data items with transformation.

    Args:
        data: Iterable of items to process

    Returns:
        List of transformed items
    """
    return [transform(item) for item in data]
>>>>>>> UPDATED

With ast-grep

# Find patterns
sg --pattern 'except: $$$' --lang python src/
sg --pattern 'print($MSG)' --lang python src/
sg --pattern 'def $FUNC($ARGS): pass' --lang python src/

Workflow Example

Complete review workflow:

#!/usr/bin/env python3
"""Automated code review workflow."""

from review_helpers import (
    check_naming_conventions,
    detect_code_smells,
    analyze_complexity,
    find_duplicate_code
)

def review_pr(pr_number: int, repo: str):
    """Conduct automated review of PR."""

    # Get changed files
    files = get_pr_files(pr_number, repo)

    # Analyze each file
    all_issues = []
    for filepath in files:
        if filepath.endswith('.py'):
            issues = []
            issues.extend(check_naming_conventions(filepath))
            issues.extend(detect_code_smells(filepath))

            complexity = analyze_complexity(filepath)
            if complexity > 10:
                issues.append(f"High complexity: {complexity}")

            all_issues.extend([(filepath, issue) for issue in issues])

    # Check for duplicates across files
    duplicates = find_duplicate_code("src/")

    # Generate review comment
    comment = format_review_comment(all_issues, duplicates)

    # Post review
    post_pr_review(pr_number, repo, comment)

if __name__ == "__main__":
    review_pr(123, "owner/repo")

Tips for Reviewers

1. Review small chunks: Don't try to review 1000+ line PRs in one sitting
2. Test the code: Pull the branch and run it locally when possible
3. Consider context: Understand the business requirements and constraints
4. Be timely: Review promptly to unblock authors
5. Distinguish preferences from problems: Not all feedback is blocking
6. Learn from reviews: Use reviews as learning opportunities

Tips for Authors

1. Keep PRs small: Aim for <300 lines when possible
2. Write descriptive PR descriptions: Explain what, why, and how
3. Self-review first: Review your own diff before requesting review
4. Respond to feedback: Address comments or explain why not
5. Ask questions: Clarify unclear feedback

Related

• GitHub PR Workflow - Git practices
• gptme patch tool - Code modification