python-patterns

Guide Python development patterns covering type hints, concurrency, and tooling.

1|Updated Mar 4, 2026

Search Tags:#code quality #best practices #python #type hints #concurrency #idioms

Ready to run

npx skills add https://github.com/flatrick/mdt --skill python-patterns-flatrick

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: python-patterns
Source: https://github.com/flatrick/mdt/tree/main/skills/python-patterns
Command: npx skills add https://github.com/flatrick/mdt --skill python-patterns-flatrick

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides guidance and examples for writing clean, efficient, and maintainable Python code, adhering to best practices and modern standards.

Core Features & Use Cases

Idiomatic Python: Learn and apply Pythonic ways of writing code (e.g., EAFP, comprehensions).
Type Hinting: Understand and implement type hints for better code clarity and static analysis.
Error Handling: Implement robust error handling with custom exceptions and context managers.
Concurrency: Explore patterns for threading, multiprocessing, and async programming.
Tooling Integration: Learn about essential Python development tools and configuration.

Quick Start

Use the python-patterns skill to generate a Python function that processes a list of user dictionaries, returning only active users with their names and emails.

Frequently Asked Questions about python-patterns

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

FAQPage Schema

What are the best practices for writing Pythonic code with type hints?▼

Pythonic code best practices involve applying EAFP, comprehensions, and modern type hinting syntax to improve readability and enable static analysis. This approach ensures your codebase remains robust, explicit, and maintainable.

How do I implement custom exceptions and context managers in Python?▼

To implement custom exceptions and context managers in Python, define custom exception hierarchies for specific error states and use context managers to manage resources cleanly. This pattern provides robust error handling and maintainable application logic.

What is the difference between threading, multiprocessing, and async concurrency in Python?▼

Python concurrency patterns differ by use case: threading is for I/O-bound tasks, multiprocessing bypasses the GIL for CPU-bound work, and async/await manages high-concurrency I/O operations. Choosing the right model ensures efficient application execution.

When should I use data classes, named tuples, or __slots__ for memory optimization?▼

Use data classes and named tuples for structured data without boilerplate, and apply __slots__ for memory optimization when creating numerous instances. These Python patterns reduce memory overhead and structure data efficiently.

How do I structure Python packages and integrate essential development tooling?▼

Structure Python packages by organizing modules logically and configure essential development tooling to enforce code quality. Proper package organization and tooling integration result in maintainable and standardized Python applications.