python-patterns

Guide Python framework selection, async versus sync execution, and project structure decisions.

Updated Mar 24, 2026

Search Tags:#pydantic #fastapi #project structure #python #type hints #async #django

Ready to run

npx skills add https://github.com/Icebane84/Synarche_Workshop --skill python-patterns-icebane84

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: python-patterns
Source: https://github.com/Icebane84/Synarche_Workshop/tree/main/.agent/skills/lang/python-patterns
Command: npx skills add https://github.com/Icebane84/Synarche_Workshop --skill python-patterns-icebane84

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It solves inconsistent, copy-pasted, and poorly structured Python implementations by teaching decision-making principles for framework choice, async vs sync behavior, and maintainable project organization.

Core Features & Use Cases

Framework selection guidance: Helps you choose between FastAPI, Django, and Flask based on context like API-first needs, admin requirements, and team familiarity.
Async vs sync decision framework: Guides when to use async for I/O-bound workloads versus sync for CPU-bound work, preventing common concurrency mistakes.
Type hints and structure principles: Recommends how to apply type hints, when to use Pydantic for validation, and how to organize code for scalability and testability.

Quick Start

Ask the AI: "Help me decide between FastAPI and Django for my next service, and explain whether I should use async or sync for the main workload, including what type hints and project structure to use."

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 service?▼

Choosing between FastAPI and Django depends on your project context. Use FastAPI for API-first services requiring async execution, or Django if you need built-in admin features and team familiarity for full-stack web apps.

When should I use async versus sync execution in Python?▼

Use async for I/O-bound workloads like API calls or database operations, and sync for CPU-bound work. Applying async concurrency rules correctly prevents common execution bottlenecks and improves API responsiveness.

What is the best way to structure a Python project for maintainability?▼

A maintainable Python project structure applies type hinting strategy and Pydantic for validation. Organizing code for scalability ensures clarity and testability across API-first services and background workers.

Do I need Pydantic for type hints and validation in FastAPI?▼

Yes, you need Pydantic for validation in FastAPI. It provides a type-hinting strategy that ensures correctness in validation-heavy systems, automatically validating incoming API requests and data models.

How does async concurrency work with background workers in Python?▼

Async concurrency for background workers handles I/O-bound tasks efficiently. By applying async execution rules, you prevent common concurrency mistakes and improve workload throughput for background operations.

What are the limitations of using async for CPU-bound work in Python?▼

Using async for CPU-bound work limits performance because it blocks the event loop. You should not use async for heavy processing; instead, apply sync execution to ensure correct CPU utilization and avoid concurrency mistakes.