robust-python

To write robust Python code, apply typing discipline using Optional and Union, implement data models with Enum and dataclass, and enforce strict mypy type checking. This framework uses Protocols and layered testing to keep your codebase maintainable.

Protocols in Python provide structural subtyping for type-safety without tight inheritance coupling. You use them to define interfaces explicitly, ensuring modules adhere to contracts while maintaining modularity and allowing controlled inheritance across your architecture.

Refactoring Python code for open/closed principle compliance involves separating policy from mechanism and using multi-layer safety nets. This framework guides you to restructure modules so they are open for extension but closed for modification during code reviews.

Yes, you can enforce immutability and type annotations using mypy with strict options. The framework mandates this strict type checking alongside sum-type representations to ensure reliable, auditable Python code without runtime type errors.

Yes, this Python code review framework operates without external dependencies. It applies design patterns and robust principles directly to your design, review, and refactor workflows natively, ensuring type-safety and scalable architecture without requiring installed packages.

You should avoid strict type checking and Protocols in Python for rapid prototyping or simple scripts where architectural overhead outweighs benefits. This framework targets designing new modules, refactoring existing code, and mentoring teams needing scalable, auditable architecture.