python-patterns

Choosing between FastAPI, Django, and Flask requires evaluating your project context, such as async needs and typing strictness. This skill provides decision tree guidance to direct your framework selection based on specific architectural requirements.

You should use async code in Python data processing pipelines when handling I/O-bound workloads, while sync code suits CPU-bound tasks. This skill offers decision-making principles to guide your async vs sync choices based on your specific processing context.

The best way to structure a scalable Python API backend is to apply principled project organization patterns. This skill outlines project structure principles that enforce clear typing practices and scalable organization across various Python ecosystems.

Implementing a robust type hints strategy for Python scripts involves applying clear typing practices tailored to your project scale. This skill guides type hints strategy to ensure robust type systems for small scripts and complex backends alike.

Copying standard Python patterns does not always work because it ignores specific project context. This skill replaces copied patterns with decision-making principles to ensure context-aware framework choices and scalable project organization.