Does this skill support the latest Python versions?

Yes, it prioritizes modern syntax for Python 3.9 through 3.12+, including the latest type union (pipe) and collection syntax.

Does it include support for testing?

Yes, the skill provides patterns for implementing robust unit and integration tests using pytest alongside standard best practices.

Can it help with project environment management?

Yes, it includes expert guidance for configuring popular tools like Poetry, uv, and pyproject.toml to streamline your development workflow.

How does it improve code quality?

By enforcing strict linting rules via Ruff, static type checking with MyPy, and applying defensive programming patterns to prevent runtime errors.

Python Best Practices

Name: Python Best Practices
Author: cameronsjo

bycameronsjo

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Developer Tools

Writes idiomatic, maintainable, and production-ready Python code following modern standards and type-safe patterns.

About

This skill equips Claude with advanced expertise in modern Python development, focusing on the 'Pythonic' code philosophy and industry-standard best practices. It provides deep integration for type safety using Pydantic and MyPy, asynchronous programming with asyncio, and streamlined tooling configuration with Ruff and Black. Whether you are setting up a new project with Poetry or optimizing performance in existing scripts, this skill ensures your code adheres to PEP standards and defensive programming principles for high-quality, scalable applications.

Key Features

Implements strict type safety using PEP 484+ type hints and Pydantic validation
Configures and optimizes modern Python tooling including Ruff, MyPy, Black, and Poetry
Provides expert patterns for high-performance asynchronous programming with asyncio
Enforces PEP 8 and PEP 20 standards for clean, readable, and idiomatic code
Applies defensive programming techniques and custom exception hierarchies for robust error handling
1 GitHub stars

Use Cases

Implementing complex data validation models and API schemas using Pydantic
Initializing a new Python project with modern linting, formatting, and type-checking configurations
Refactoring legacy Python scripts into asynchronous, type-safe, and production-grade modules

About

Key Features

Implements strict type safety using PEP 484+ type hints and Pydantic validation
Configures and optimizes modern Python tooling including Ruff, MyPy, Black, and Poetry
Provides expert patterns for high-performance asynchronous programming with asyncio
Enforces PEP 8 and PEP 20 standards for clean, readable, and idiomatic code
Applies defensive programming techniques and custom exception hierarchies for robust error handling
1 GitHub stars

Use Cases

Implementing complex data validation models and API schemas using Pydantic
Initializing a new Python project with modern linting, formatting, and type-checking configurations
Refactoring legacy Python scripts into asynchronous, type-safe, and production-grade modules