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python-patterns

Guide Python framework selection, async versus sync choices, and typing strategies.

Updated Jan 20, 2026
Search Tags:#async#decision-making#typing#project-structure#python-patterns#framework-selection
One-click install
npx skills add https://github.com/lchenrique/politron-ide --skill python-patterns-lchenrique
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: python-patterns
Source: https://github.com/lchenrique/politron-ide/tree/main/.agent/skills/python-patterns
Command: npx skills add https://github.com/lchenrique/politron-ide --skill python-patterns-lchenrique

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill helps developers avoid copying patterns by guiding structured thinking about Python development approaches.

Core Features & Use Cases

  • Decision framework: guidance on selecting frameworks (FastAPI, Django, Flask) based on context.
  • Async vs Sync guidance: criteria to choose async or sync approaches for I/O-bound vs CPU-bound tasks.
  • Typing & project structure: best practices for type hints, Pydantic usage, and scalable project organization.
  • Use Case: When starting a new API project, use this skill to determine the appropriate framework and structure without lock-in.

Quick Start

Ask clarifying questions about framework preference and project constraints to generate an optimal Python project plan.

Frequently Asked Questions about python-patterns

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

FAQPage Schema
How to choose between FastAPI, Django, and Flask for a new Python project?▼

Framework selection for Python projects depends on specific constraints like async requirements and project scale. Evaluating these criteria helps determine whether FastAPI, Django, or Flask best supports your API without early lock-in.

When should I use async vs sync approaches in Python development?▼

Choosing async or sync Python approaches depends on task types: async suits I/O-bound operations, while sync fits CPU-bound workloads. Applying these criteria ensures reliable and scalable code for concurrent operations.

What are the best practices for type hinting and Pydantic usage in Python APIs?▼

Type hinting practices in Python involve applying type hints and using Pydantic for data validation. Structured type hinting ensures reliable data parsing and supports scalable project organization across Python projects.

How do I structure a scalable Python API project from scratch?▼

Structured Python project layouts require organizing modules around specific constraints and framework selection. Defining project structure early supports scalable code and prevents architectural lock-in as the API grows.

Does this Python decision-making framework work for migrating existing sync codebases to async?▼

Python async and sync decision-making guidance applies to both new and existing projects. The skill evaluates I/O-bound versus CPU-bound task criteria to guide structured migration and scalable code refactoring.

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