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DVNghiem
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python-patterns

Guide Python development with type hints, dataclasses, async, and pytest patterns.

20|3|Updated Apr 30, 2026
Search Tags:#pytest#python#patterns#asyncio#idioms#dataclasses#typing
One-click install
npx skills add https://github.com/DVNghiem/FlowDeck --skill python-patterns-dvnghiem
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: python-patterns
Source: https://github.com/DVNghiem/FlowDeck/tree/main/src/skills/python-patterns
Command: npx skills add https://github.com/DVNghiem/FlowDeck --skill python-patterns-dvnghiem

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Replace common Python antipatterns with modern idioms, giving developers a clear, reliable blueprint for writing correct, maintainable Python code using type hints, dataclasses, async patterns, and robust testing.

Core Features & Use Cases

  • Type hints and typing patterns: guidance on annotations, Protocols, and type aliases for clearer interfaces.
  • Data modeling decisions: guidance on Dataclass vs TypedDict vs Pydantic for domain objects and input validation.
  • Async and iteration patterns: best practices for async IO, concurrency, and generator-based pipelines.
  • Testing and tooling: examples with pytest, fixtures, and mocking to ensure reliability.
  • Common pitfalls and best practices: practical tips to avoid mutable defaults, late binding, and circular imports.

Quick Start

Review your Python project and begin applying modern idioms like type hints, dataclasses, and async patterns to improve clarity and safety.

Frequently Asked Questions about python-patterns

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

FAQPage Schema
What is the best way to decide between using dataclasses, TypedDict, or Pydantic for Python data models?▼

Choosing between dataclasses, TypedDict, and Pydantic depends on your need for internal domain objects, structural typing, or input validation respectively. This guide provides decision criteria to select the correct modern Python data modeling approach.

How do I write idiomatic asyncio patterns for concurrent Python code?▼

Idiomatic asyncio patterns apply modern concurrency best practices to manage async IO operations and generator-based pipelines safely. This guide demonstrates how to structure asynchronous Python code for maintainable and robust execution.

How do I set up pytest fixtures and mocking for Python testing?▼

Setting up pytest fixtures and mocking ensures reliability by isolating test environments and managing dependencies. This guide provides practical examples for writing robust Python tests using modern testing tooling.

Why do mutable defaults and late binding cause bugs in Python, and how do I avoid them?▼

Mutable defaults and late binding cause bugs by sharing state across function calls or closures unexpectedly. This guide outlines common Python pitfalls and best practices to write safe, maintainable code.

Can I use type hints and Protocols to define clearer Python interfaces?▼

Type hints and Protocols define clearer Python interfaces by specifying structural subtyping and expected attributes without inheritance. This guide shows how to apply PEP 484+ annotations and type aliases for robust code.

Does this Python patterns guide cover context managers and exception handling?▼

This guide covers context managers and exception handling to ensure proper resource cleanup and robust error management. It provides idiomatic patterns for writing maintainable Python code in production environments.

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