Can this skill help with asynchronous Python code?

Yes, it specifically highlights anti-patterns like using blocking calls (time.sleep or synchronous requests) within async functions, which can stall the event loop.

What kind of Python issues does this skill detect?

It identifies common mistakes across infrastructure (like double retries), architecture (like leaking ORM models), error handling (like bare exceptions), resource management, type safety, and testing.

Does it provide solutions or just identify problems?

The skill provides both the identification of the anti-pattern and the recommended 'Good' pattern or fix to implement, including code examples.

How does this differ from standard linters like Flake8 or Pylint?

While linters catch syntax and style, this skill focuses on higher-level architectural patterns, logic flaws, and design principles that automated tools often miss.

Python Anti-Pattern Review

Name: Python Anti-Pattern Review
Author: wshobson

bywshobson

•

27,605

Security & Testing

Identifies and resolves common Python coding mistakes, architectural flaws, and performance bottlenecks using a comprehensive best-practices checklist.

About

This skill serves as an automated code reviewer for Python projects, helping developers identify and resolve common anti-patterns before they reach production. It covers critical areas such as infrastructure retry logic, architectural boundary leaks, resource management, and type safety. By integrating this skill into the development workflow, teams can ensure consistent coding standards, improve system reliability, and reduce technical debt through proactive identification of known bad practices like bare exception handling, hard-coded configurations, or blocking calls in asynchronous code.

Key Features

Comprehensive checklist for architectural, infrastructure, and error-handling anti-patterns
27,605 GitHub stars
Strategies for replacing hard-coded configurations with secure, typed environment settings
Guidance on enforcing type safety and proper type annotations across public APIs
Best practices for testing edge cases and avoiding over-mocking in test suites
Automated detection of resource management issues like unclosed files or blocking async calls

Use Cases

Debugging mysterious system failures caused by swallowed exceptions or misconfigured retry logic
Refactoring legacy Python codebases to modern, type-safe, and asynchronous standards
Pre-merge code reviews to catch common architectural mistakes and technical debt early

About

Key Features

Comprehensive checklist for architectural, infrastructure, and error-handling anti-patterns
27,605 GitHub stars
Strategies for replacing hard-coded configurations with secure, typed environment settings
Guidance on enforcing type safety and proper type annotations across public APIs
Best practices for testing edge cases and avoiding over-mocking in test suites
Automated detection of resource management issues like unclosed files or blocking async calls

Use Cases

Debugging mysterious system failures caused by swallowed exceptions or misconfigured retry logic
Refactoring legacy Python codebases to modern, type-safe, and asynchronous standards
Pre-merge code reviews to catch common architectural mistakes and technical debt early