How is the code review feedback structured?

Feedback is organized by severity into three tiers: Blockers (must fix before merge), Suggestions (design and performance improvements), and Nits (minor stylistic issues).

What tools does the Python Code Review skill use?

The skill integrates with industry-standard Python tools including Ruff for linting and formatting, Mypy for static type checking, and Pytest for verifying test suites.

Does it support both GitHub and GitLab?

Yes, the skill is designed to work with both Pull Requests (GitHub) and Merge Requests (GitLab) by utilizing git diff and CLI tools like 'gh' for context.

Can it help with Python-specific bugs?

Absolutely. It catches common Python pitfalls such as mutable default arguments, bare except clauses, and incorrect exception chaining.

Can this skill detect security vulnerabilities?

Yes, it specifically checks for critical security issues such as SQL injection, path traversal, hardcoded secrets, and the use of unsafe functions like eval() or exec().

Python Code Reviewer

Name: Python Code Reviewer
Author: thoo

bythoo

Collaboration Tools

Performs systematic, high-quality Python code reviews focusing on security, logic correctness, and PEP 8 compliance.

About

The Python Code Review skill enables Claude to conduct deep, structured analysis of Python code changes within pull requests and merge requests. By combining automated checks from tools like Ruff and Mypy with a rigorous manual review checklist, it identifies critical security vulnerabilities, logic edge cases, and architectural smells. This skill ensures that every code change adheres to professional standards, providing categorized feedback ranging from critical blockers to minor stylistic suggestions.

Key Features

Integrated automated linting and type checking with Ruff and Mypy
Security-first audit for SQL injection, hardcoded secrets, and unsafe input handling
Categorized feedback structured by severity: Blockers, Suggestions, and Nits
Logic verification for edge cases, error handling, and exception chaining
Detection of common Python anti-patterns like mutable default arguments
0 GitHub stars

Use Cases

Reviewing complex Python Merge Requests or Pull Requests for production readiness
Standardizing code quality and type safety across large engineering teams
Auditing legacy Python scripts for security vulnerabilities and technical debt

About

Key Features

Integrated automated linting and type checking with Ruff and Mypy
Security-first audit for SQL injection, hardcoded secrets, and unsafe input handling
Categorized feedback structured by severity: Blockers, Suggestions, and Nits
Logic verification for edge cases, error handling, and exception chaining
Detection of common Python anti-patterns like mutable default arguments
0 GitHub stars

Use Cases

Reviewing complex Python Merge Requests or Pull Requests for production readiness
Standardizing code quality and type safety across large engineering teams
Auditing legacy Python scripts for security vulnerabilities and technical debt