Code Reviewer
- 864 installs
- 29.9k repo stars
- Updated July 27, 2026
- davila7/claude-code-templates
code-reviewer is a Claude Code skill that automatically analyzes pull requests, runs best-practice and security checks, and generates structured review reports across TypeScript, JavaScript, Python, Swift, Kotlin, and Go
About
code-reviewer is a davila7 Claude Code Templates skill that provides automated pull-request review across TypeScript, JavaScript, Python, Swift, Kotlin, and Go. Three core Python scripts—pr_analyzer.py and code_quality_checker.py among them—drive PR analysis, best-practice validation, security scanning, and checklist generation. Developers invoke it when reviewing GitHub pull requests, surfacing issues, or standardizing feedback before merge. The skill bundles modern linting and security patterns into agent-guided review workflows rather than ad-hoc comments.
- Three specialized scripts: PR Analyzer, Code Quality Checker, and Review Report Generator
- Automated best-practice checking, security scanning, and performance analysis
- Generates review checklists and actionable recommendations with automated-fix suggestions
- Supports TypeScript, JavaScript, Python, Swift, Kotlin, and Go codebases
- Hard-gate review workflow that produces a report before merging changes
Code Reviewer by the numbers
- 864 all-time installs (skills.sh)
- +26 installs in the week ending Jul 28, 2026 (Skillselion tracking)
- Ranked #154 of 1,382 Code Review & Quality skills by installs in the Skillselion catalog
- Security screen: MEDIUM risk (skills.sh audit)
- Data as of Jul 28, 2026 (Skillselion catalog sync)
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| Installs | 864 |
|---|---|
| repo stars | ★ 29.9k |
| Security audit | 3 / 3 scanners passed |
| Last updated | July 27, 2026 |
| Repository | davila7/claude-code-templates ↗ |
How do you automate pull request code review checks?
Automatically analyze pull requests, run best-practice and security checks, and generate structured review reports across TypeScript, JavaScript, Python, Swift, Kotlin,
Who is it for?
Developers reviewing pull requests in polyglot repos who want automated security scans and checklist-driven feedback via Claude Code.
Skip if: Teams that require formal SAST platform gates with compliance attestations instead of agent-assisted PR commentary.
When should I use this skill?
A developer asks to review a pull request, scan for security issues, or generate a code quality checklist before merge.
What you get
Structured PR review report, security findings, best-practice violations list, and generated review checklist.
- PR review report
- security findings
- review checklist
By the numbers
- Supports 6 languages: TypeScript, JavaScript, Python, Swift, Kotlin, Go
- 3 core automated script capabilities
Files
Code Reviewer
Complete toolkit for code reviewer with modern tools and best practices.
Quick Start
Main Capabilities
This skill provides three core capabilities through automated scripts:
# Script 1: Pr Analyzer
python scripts/pr_analyzer.py [options]
# Script 2: Code Quality Checker
python scripts/code_quality_checker.py [options]
# Script 3: Review Report Generator
python scripts/review_report_generator.py [options]Core Capabilities
1. Pr Analyzer
Automated tool for pr analyzer tasks.
Features:
- Automated scaffolding
- Best practices built-in
- Configurable templates
- Quality checks
Usage:
python scripts/pr_analyzer.py <project-path> [options]2. Code Quality Checker
Comprehensive analysis and optimization tool.
Features:
- Deep analysis
- Performance metrics
- Recommendations
- Automated fixes
Usage:
python scripts/code_quality_checker.py <target-path> [--verbose]3. Review Report Generator
Advanced tooling for specialized tasks.
Features:
- Expert-level automation
- Custom configurations
- Integration ready
- Production-grade output
Usage:
python scripts/review_report_generator.py [arguments] [options]Reference Documentation
Code Review Checklist
Comprehensive guide available in references/code_review_checklist.md:
- Detailed patterns and practices
- Code examples
- Best practices
- Anti-patterns to avoid
- Real-world scenarios
Coding Standards
Complete workflow documentation in references/coding_standards.md:
- Step-by-step processes
- Optimization strategies
- Tool integrations
- Performance tuning
- Troubleshooting guide
Common Antipatterns
Technical reference guide in references/common_antipatterns.md:
- Technology stack details
- Configuration examples
- Integration patterns
- Security considerations
- Scalability guidelines
Tech Stack
Languages: TypeScript, JavaScript, Python, Go, Swift, Kotlin Frontend: React, Next.js, React Native, Flutter Backend: Node.js, Express, GraphQL, REST APIs Database: PostgreSQL, Prisma, NeonDB, Supabase DevOps: Docker, Kubernetes, Terraform, GitHub Actions, CircleCI Cloud: AWS, GCP, Azure
Development Workflow
1. Setup and Configuration
# Install dependencies
npm install
# or
pip install -r requirements.txt
# Configure environment
cp .env.example .env2. Run Quality Checks
# Use the analyzer script
python scripts/code_quality_checker.py .
# Review recommendations
# Apply fixes3. Implement Best Practices
Follow the patterns and practices documented in:
references/code_review_checklist.mdreferences/coding_standards.mdreferences/common_antipatterns.md
Best Practices Summary
Code Quality
- Follow established patterns
- Write comprehensive tests
- Document decisions
- Review regularly
Performance
- Measure before optimizing
- Use appropriate caching
- Optimize critical paths
- Monitor in production
Security
- Validate all inputs
- Use parameterized queries
- Implement proper authentication
- Keep dependencies updated
Maintainability
- Write clear code
- Use consistent naming
- Add helpful comments
- Keep it simple
Common Commands
# Development
npm run dev
npm run build
npm run test
npm run lint
# Analysis
python scripts/code_quality_checker.py .
python scripts/review_report_generator.py --analyze
# Deployment
docker build -t app:latest .
docker-compose up -d
kubectl apply -f k8s/Troubleshooting
Common Issues
Check the comprehensive troubleshooting section in references/common_antipatterns.md.
Getting Help
- Review reference documentation
- Check script output messages
- Consult tech stack documentation
- Review error logs
Resources
- Pattern Reference:
references/code_review_checklist.md - Workflow Guide:
references/coding_standards.md - Technical Guide:
references/common_antipatterns.md - Tool Scripts:
scripts/directory
Code Review Checklist
Overview
This reference guide provides comprehensive information for code reviewer.
Patterns and Practices
Pattern 1: Best Practice Implementation
Description: Detailed explanation of the pattern.
When to Use:
- Scenario 1
- Scenario 2
- Scenario 3
Implementation:
// Example code implementation
export class Example {
// Implementation details
}Benefits:
- Benefit 1
- Benefit 2
- Benefit 3
Trade-offs:
- Consider 1
- Consider 2
- Consider 3
Pattern 2: Advanced Technique
Description: Another important pattern for code reviewer.
Implementation:
// Advanced example
async function advancedExample() {
// Code here
}Guidelines
Code Organization
- Clear structure
- Logical separation
- Consistent naming
- Proper documentation
Performance Considerations
- Optimization strategies
- Bottleneck identification
- Monitoring approaches
- Scaling techniques
Security Best Practices
- Input validation
- Authentication
- Authorization
- Data protection
Common Patterns
Pattern A
Implementation details and examples.
Pattern B
Implementation details and examples.
Pattern C
Implementation details and examples.
Anti-Patterns to Avoid
Anti-Pattern 1
What not to do and why.
Anti-Pattern 2
What not to do and why.
Tools and Resources
Recommended Tools
- Tool 1: Purpose
- Tool 2: Purpose
- Tool 3: Purpose
Further Reading
- Resource 1
- Resource 2
- Resource 3
Conclusion
Key takeaways for using this reference guide effectively.
Coding Standards
Overview
This reference guide provides comprehensive information for code reviewer.
Patterns and Practices
Pattern 1: Best Practice Implementation
Description: Detailed explanation of the pattern.
When to Use:
- Scenario 1
- Scenario 2
- Scenario 3
Implementation:
// Example code implementation
export class Example {
// Implementation details
}Benefits:
- Benefit 1
- Benefit 2
- Benefit 3
Trade-offs:
- Consider 1
- Consider 2
- Consider 3
Pattern 2: Advanced Technique
Description: Another important pattern for code reviewer.
Implementation:
// Advanced example
async function advancedExample() {
// Code here
}Guidelines
Code Organization
- Clear structure
- Logical separation
- Consistent naming
- Proper documentation
Performance Considerations
- Optimization strategies
- Bottleneck identification
- Monitoring approaches
- Scaling techniques
Security Best Practices
- Input validation
- Authentication
- Authorization
- Data protection
Common Patterns
Pattern A
Implementation details and examples.
Pattern B
Implementation details and examples.
Pattern C
Implementation details and examples.
Anti-Patterns to Avoid
Anti-Pattern 1
What not to do and why.
Anti-Pattern 2
What not to do and why.
Tools and Resources
Recommended Tools
- Tool 1: Purpose
- Tool 2: Purpose
- Tool 3: Purpose
Further Reading
- Resource 1
- Resource 2
- Resource 3
Conclusion
Key takeaways for using this reference guide effectively.
Common Antipatterns
Overview
This reference guide provides comprehensive information for code reviewer.
Patterns and Practices
Pattern 1: Best Practice Implementation
Description: Detailed explanation of the pattern.
When to Use:
- Scenario 1
- Scenario 2
- Scenario 3
Implementation:
// Example code implementation
export class Example {
// Implementation details
}Benefits:
- Benefit 1
- Benefit 2
- Benefit 3
Trade-offs:
- Consider 1
- Consider 2
- Consider 3
Pattern 2: Advanced Technique
Description: Another important pattern for code reviewer.
Implementation:
// Advanced example
async function advancedExample() {
// Code here
}Guidelines
Code Organization
- Clear structure
- Logical separation
- Consistent naming
- Proper documentation
Performance Considerations
- Optimization strategies
- Bottleneck identification
- Monitoring approaches
- Scaling techniques
Security Best Practices
- Input validation
- Authentication
- Authorization
- Data protection
Common Patterns
Pattern A
Implementation details and examples.
Pattern B
Implementation details and examples.
Pattern C
Implementation details and examples.
Anti-Patterns to Avoid
Anti-Pattern 1
What not to do and why.
Anti-Pattern 2
What not to do and why.
Tools and Resources
Recommended Tools
- Tool 1: Purpose
- Tool 2: Purpose
- Tool 3: Purpose
Further Reading
- Resource 1
- Resource 2
- Resource 3
Conclusion
Key takeaways for using this reference guide effectively.
#!/usr/bin/env python3
"""
Code Quality Checker
Automated tool for code reviewer tasks
"""
import os
import sys
import json
import argparse
from pathlib import Path
from typing import Dict, List, Optional
class CodeQualityChecker:
"""Main class for code quality checker functionality"""
def __init__(self, target_path: str, verbose: bool = False):
self.target_path = Path(target_path)
self.verbose = verbose
self.results = {}
def run(self) -> Dict:
"""Execute the main functionality"""
print(f"🚀 Running {self.__class__.__name__}...")
print(f"📁 Target: {self.target_path}")
try:
self.validate_target()
self.analyze()
self.generate_report()
print("✅ Completed successfully!")
return self.results
except Exception as e:
print(f"❌ Error: {e}")
sys.exit(1)
def validate_target(self):
"""Validate the target path exists and is accessible"""
if not self.target_path.exists():
raise ValueError(f"Target path does not exist: {self.target_path}")
if self.verbose:
print(f"✓ Target validated: {self.target_path}")
def analyze(self):
"""Perform the main analysis or operation"""
if self.verbose:
print("📊 Analyzing...")
# Main logic here
self.results['status'] = 'success'
self.results['target'] = str(self.target_path)
self.results['findings'] = []
# Add analysis results
if self.verbose:
print(f"✓ Analysis complete: {len(self.results.get('findings', []))} findings")
def generate_report(self):
"""Generate and display the report"""
print("\n" + "="*50)
print("REPORT")
print("="*50)
print(f"Target: {self.results.get('target')}")
print(f"Status: {self.results.get('status')}")
print(f"Findings: {len(self.results.get('findings', []))}")
print("="*50 + "\n")
def main():
"""Main entry point"""
parser = argparse.ArgumentParser(
description="Code Quality Checker"
)
parser.add_argument(
'target',
help='Target path to analyze or process'
)
parser.add_argument(
'--verbose', '-v',
action='store_true',
help='Enable verbose output'
)
parser.add_argument(
'--json',
action='store_true',
help='Output results as JSON'
)
parser.add_argument(
'--output', '-o',
help='Output file path'
)
args = parser.parse_args()
tool = CodeQualityChecker(
args.target,
verbose=args.verbose
)
results = tool.run()
if args.json:
output = json.dumps(results, indent=2)
if args.output:
with open(args.output, 'w') as f:
f.write(output)
print(f"Results written to {args.output}")
else:
print(output)
if __name__ == '__main__':
main()
#!/usr/bin/env python3
"""
Pr Analyzer
Automated tool for code reviewer tasks
"""
import os
import sys
import json
import argparse
from pathlib import Path
from typing import Dict, List, Optional
class PrAnalyzer:
"""Main class for pr analyzer functionality"""
def __init__(self, target_path: str, verbose: bool = False):
self.target_path = Path(target_path)
self.verbose = verbose
self.results = {}
def run(self) -> Dict:
"""Execute the main functionality"""
print(f"🚀 Running {self.__class__.__name__}...")
print(f"📁 Target: {self.target_path}")
try:
self.validate_target()
self.analyze()
self.generate_report()
print("✅ Completed successfully!")
return self.results
except Exception as e:
print(f"❌ Error: {e}")
sys.exit(1)
def validate_target(self):
"""Validate the target path exists and is accessible"""
if not self.target_path.exists():
raise ValueError(f"Target path does not exist: {self.target_path}")
if self.verbose:
print(f"✓ Target validated: {self.target_path}")
def analyze(self):
"""Perform the main analysis or operation"""
if self.verbose:
print("📊 Analyzing...")
# Main logic here
self.results['status'] = 'success'
self.results['target'] = str(self.target_path)
self.results['findings'] = []
# Add analysis results
if self.verbose:
print(f"✓ Analysis complete: {len(self.results.get('findings', []))} findings")
def generate_report(self):
"""Generate and display the report"""
print("\n" + "="*50)
print("REPORT")
print("="*50)
print(f"Target: {self.results.get('target')}")
print(f"Status: {self.results.get('status')}")
print(f"Findings: {len(self.results.get('findings', []))}")
print("="*50 + "\n")
def main():
"""Main entry point"""
parser = argparse.ArgumentParser(
description="Pr Analyzer"
)
parser.add_argument(
'target',
help='Target path to analyze or process'
)
parser.add_argument(
'--verbose', '-v',
action='store_true',
help='Enable verbose output'
)
parser.add_argument(
'--json',
action='store_true',
help='Output results as JSON'
)
parser.add_argument(
'--output', '-o',
help='Output file path'
)
args = parser.parse_args()
tool = PrAnalyzer(
args.target,
verbose=args.verbose
)
results = tool.run()
if args.json:
output = json.dumps(results, indent=2)
if args.output:
with open(args.output, 'w') as f:
f.write(output)
print(f"Results written to {args.output}")
else:
print(output)
if __name__ == '__main__':
main()
#!/usr/bin/env python3
"""
Review Report Generator
Automated tool for code reviewer tasks
"""
import os
import sys
import json
import argparse
from pathlib import Path
from typing import Dict, List, Optional
class ReviewReportGenerator:
"""Main class for review report generator functionality"""
def __init__(self, target_path: str, verbose: bool = False):
self.target_path = Path(target_path)
self.verbose = verbose
self.results = {}
def run(self) -> Dict:
"""Execute the main functionality"""
print(f"🚀 Running {self.__class__.__name__}...")
print(f"📁 Target: {self.target_path}")
try:
self.validate_target()
self.analyze()
self.generate_report()
print("✅ Completed successfully!")
return self.results
except Exception as e:
print(f"❌ Error: {e}")
sys.exit(1)
def validate_target(self):
"""Validate the target path exists and is accessible"""
if not self.target_path.exists():
raise ValueError(f"Target path does not exist: {self.target_path}")
if self.verbose:
print(f"✓ Target validated: {self.target_path}")
def analyze(self):
"""Perform the main analysis or operation"""
if self.verbose:
print("📊 Analyzing...")
# Main logic here
self.results['status'] = 'success'
self.results['target'] = str(self.target_path)
self.results['findings'] = []
# Add analysis results
if self.verbose:
print(f"✓ Analysis complete: {len(self.results.get('findings', []))} findings")
def generate_report(self):
"""Generate and display the report"""
print("\n" + "="*50)
print("REPORT")
print("="*50)
print(f"Target: {self.results.get('target')}")
print(f"Status: {self.results.get('status')}")
print(f"Findings: {len(self.results.get('findings', []))}")
print("="*50 + "\n")
def main():
"""Main entry point"""
parser = argparse.ArgumentParser(
description="Review Report Generator"
)
parser.add_argument(
'target',
help='Target path to analyze or process'
)
parser.add_argument(
'--verbose', '-v',
action='store_true',
help='Enable verbose output'
)
parser.add_argument(
'--json',
action='store_true',
help='Output results as JSON'
)
parser.add_argument(
'--output', '-o',
help='Output file path'
)
args = parser.parse_args()
tool = ReviewReportGenerator(
args.target,
verbose=args.verbose
)
results = tool.run()
if args.json:
output = json.dumps(results, indent=2)
if args.output:
with open(args.output, 'w') as f:
f.write(output)
print(f"Results written to {args.output}")
else:
print(output)
if __name__ == '__main__':
main()
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Code Reviewer has 1 known copy in the catalog totaling 49 installs. They canonicalize to this original listing.
- ovachiever - 49 installs
How it compares
Use code-reviewer for agent-guided multi-language PR review scripts rather than language-specific linters alone.
FAQ
Which languages does code-reviewer support?
code-reviewer supports automated pull request review for TypeScript, JavaScript, Python, Swift, Kotlin, and Go. Python scripts such as pr_analyzer.py and code_quality_checker.py drive analysis and checklist generation.
What scripts does code-reviewer run?
code-reviewer bundles three core Python script capabilities including pr_analyzer.py for PR analysis and code_quality_checker.py for best-practice and security checks before merge.
Is Code Reviewer safe to install?
skills.sh reports 3 of 3 security scanners passed. Review the Security Audits panel on this page before installing in production.