python-patterns

Guide Python framework selection, async decisions, and typing strategies.

Updated Aug 30, 2024
One-click install
npx skills add https://github.com/jfrometa/esquizo --skill python-patterns-jfrometa
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: python-patterns
Source: https://github.com/jfrometa/esquizo/tree/main/.agent/skills/python-patterns
Command: npx skills add https://github.com/jfrometa/esquizo --skill python-patterns-jfrometa

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill guides developers to reason about Python patterns and architecture rather than blindly copying templates.

Core Features & Use Cases

  • Decision Tree for framework selection (e.g., FastAPI, Django, Flask) based on API vs full-stack needs.
  • Async vs Sync Guidance to decide between async I/O and blocking operations.
  • Typing Best Practices covering Optional, Union, Generic patterns and Pydantic-like validation concepts.
  • Project Structure Guidance for scalable small/medium/large projects.

Quick Start

Start by describing a Python project context to an AI, then ask for a framework choice, an async approach, and a typing strategy using the decision framework.

Frequently Asked Questions about python-patterns

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I choose a Python framework for my project?

Choosing a Python framework depends on your API versus full-stack needs. Use a decision tree to select FastAPI for APIs, Django for full-stack, or Flask for lightweight applications based on your project requirements.

When should I use async patterns instead of sync in Python?

Use async patterns for I/O-bound operations and sync for blocking tasks. Async I/O guidance helps decide between async and blocking operations by evaluating your project's concurrency requirements and performance goals.

What are the best practices for Python type hints and typing strategies?

Python type hints best practices involve using Optional, Union, and Generic patterns, alongside Pydantic-like validation concepts. Typing strategies adapt to different project sizes and goals to enforce structured guidance.

How do I structure a scalable Python project?

Structure a scalable Python project by applying pragmatic layout rules. Guidance scales project structure for small, medium, and large projects to ensure maintainable architecture as complexity grows.

Does my Python project need Pydantic-like validation?

Your Python project needs Pydantic-like validation if you require structured data enforcement. Typing best practices cover Pydantic-like validation concepts to ensure data integrity across different project scales and goals.