python-patterns

Guide Python project architecture decisions for frameworks, async patterns, and type hinting.

Updated Jun 5, 2026
One-click install
npx skills add https://github.com/0xRayAI/xray --skill python-patterns-0xrayai
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: python-patterns
Source: https://github.com/0xRayAI/xray/tree/main/integrations/python-patterns
Command: npx skills add https://github.com/0xRayAI/xray --skill python-patterns-0xrayai

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill addresses the common struggle of choosing the right Python architecture, framework, and async patterns, helping developers move beyond rote memorization to informed decision-making.

Core Features & Use Cases

  • Architectural Decision Trees: Provides clear guidance on selecting between FastAPI, Django, and Flask based on project requirements.
  • Async vs. Sync Strategy: Offers a definitive framework for determining when to implement asynchronous code versus synchronous execution.
  • Project Structuring: Outlines best practices for organizing small scripts, medium APIs, and large-scale applications for maintainability.

Quick Start

Use the python-patterns skill to evaluate whether my current project architecture should prioritize FastAPI or Django based on my specific requirements.

Frequently Asked Questions about python-patterns

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

FAQPage Schema
How do I choose between FastAPI and Django for my Python project architecture?

To choose between FastAPI and Django for your Python project architecture, evaluate your specific project requirements using an architectural decision tree that weighs framework features against scalability and async needs.

When should I use async programming patterns instead of synchronous execution in Python?

You should use async programming patterns instead of synchronous execution in Python when a definitive framework determines your operations involve high-concurrency I/O tasks, ensuring optimal resource utilization over blocking calls.

What are the best practices for structuring a large-scale Python application for maintainability?

Best practices for structuring a large-scale Python application involve organizing medium APIs and complex projects into maintainable modules, applying context-aware engineering principles rather than relying on generic code snippets.

Does this Python architecture guidance provide type hinting strategies for framework selection?

Yes, this Python architecture guidance provides type hinting strategies and decision-making frameworks that directly apply to framework selection and project structure design.

What is the best way to evaluate Python architecture decisions for small scripts versus medium APIs?

The best way to evaluate Python architecture decisions for small scripts versus medium APIs is to apply structured decision-making frameworks that outline project structuring best practices based on scale and maintainability requirements.

Can I apply these Python design decisions to move beyond rote memorization to informed architecture choices?

Yes, you can apply these Python design decisions to move beyond rote memorization to informed architecture choices, solving the common struggle of selecting the right frameworks and asynchronous patterns.