python-patterns

Choosing the right Python framework requires evaluating your project context: FastAPI suits async APIs, Django handles full-stack apps, and Flask fits lightweight services. Describe your project goals to receive a tailored decision framework for selecting the optimal architecture and patterns.

Use async Python code for I/O-bound tasks like concurrent API requests, and sync code for CPU-bound data processing. This Skill provides async vs sync guidance and typing strategies using Pydantic to improve reliability and readability.

The best way to structure a scalable Python project is enforcing type hints and applying architectural decision patterns. This Skill provides project structure guidance and testing patterns to ensure maintainable, readable code with concrete examples.

Yes, this Python architecture guidance applies to API design, data processing, scripting, and educational contexts. It helps you select appropriate patterns and frameworks to reduce rework and accelerate delivery across various project types.

To implement testing patterns and type hints for maintainable Python code, apply decision-making principles for typing strategies like Pydantic. This Skill outlines testing approaches and project structure guidelines with concrete examples to enforce reliability.