What problem does it solve?
It prevents common Python code quality and security mistakes by providing consistent, repeatable best-practice guidance during day-to-day development.
Core Features & Use Cases
- Consistent formatting & structure: Enforces a predictable style (2-space indentation, imports at the top, no inline imports).
- Correct import organization: Promotes fully qualified imports and proper
__init__.py re-exports to avoid ImportError-class problems.
- Maintainable design & testing patterns: Recommends parameterization, configuration grouping via classes, pytest conventions, and async-first architecture; also covers security-safe error handling and credential logging rules.
Use case examples:
- A developer refactoring a Python service wants fewer regressions by following established module/import patterns and async-safe testing guidance.
- A team standardizes how new endpoints, clients, and tests are written to reduce security leaks (like logging secrets) and improve test reliability.
Quick Start
Ask the AI: “Review my Python change for best practices in indentation, imports, error handling, async-first design, and pytest patterns, and return a concise checklist of fixes.”
