python-pro

Generate type-safe async Python 3.11+ code with pytest and mypy checks.

10.9k|1.0k|Updated Oct 20, 2025
One-click install
npx skills add https://github.com/Jeffallan/claude-skills --skill python-pro-jeffallan
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: python-pro
Source: https://github.com/Jeffallan/claude-skills/tree/main/skills/python-pro
Command: npx skills add https://github.com/Jeffallan/claude-skills --skill python-pro-jeffallan

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps craft robust Python 3.11+ code with typing, async IO, and testing best practices.

Core Features & Use Cases

  • Type hints & mypy: End-to-end type coverage and static checks.
  • Async patterns: Async IO, asyncio, and task coordination.
  • Testing: Pytest, fixtures, and mocking.

Quick Start

Scaffold a small async module with type hints and a pytest suite.

Frequently Asked Questions about python-pro

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

FAQPage Schema
How do I write fully type-hinted Python code with mypy checking?

Type-hinted Python code uses type annotations on function parameters, return values, and variables, then validated with mypy static analysis. This catches type errors before runtime and is mandatory on all public APIs in production Python 3.11+ projects.

What's the best way to structure async code in Python with asyncio?

Async Python uses async/await syntax with asyncio for non-blocking I/O operations. Organizing async tasks with proper coordination patterns, context managers, and dataclasses ensures clean, concurrent code suitable for web services and data processing workloads.

How do I write pytest tests with >90% code coverage?

Pytest with fixtures and mocking enables comprehensive test suites that exercise code paths systematically. High coverage (>90%) combined with strict assertions ensures reliability and catches regressions across public APIs and critical logic.

Can I use dataclasses with type hints for production Python applications?

Dataclasses paired with type hints provide type-safe data structures ideal for production Python. They reduce boilerplate, integrate seamlessly with mypy validation, and work across web services, automation, and tooling projects requiring robust typing.

Why enforce Black and Ruff code quality standards in Python projects?

Black formatting and Ruff linting enforce consistent style and catch common errors automatically. Combined with mypy and pytest, they form a complete quality gate ensuring code meets production standards for readability and correctness.