Does this skill include patterns for unit testing?

Yes, it provides detailed patterns for Pytest, including the use of fixtures, asynchronous testing, and sophisticated mocking techniques.

How does this differ from the base language-patterns skill?

This skill extends the base patterns. While the base skill handles universal principles like naming, this skill provides the specific Python implementation of those concepts.

Can I use this for Data Science or Jupyter Notebooks?

While some patterns apply, this skill is specifically optimized for software engineering and application development. Data science often follows different conventions for notebooks.

Which Python versions are supported by this skill?

This skill targets modern Python 3.10 and above, utilizing syntax features like the union type operator (X | Y) and structural pattern matching.

Python Best Practices & Patterns

Name: Python Best Practices & Patterns
Author: Zate

byZate

Desarrollo de API

Implements idiomatic Python 3.10+ patterns for modern, type-safe, and highly maintainable application development.

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The python-patterns skill provides Claude with a comprehensive framework for writing high-quality Python code using modern 3.10+ standards. It covers everything from structural typing with Protocols and advanced type hinting to robust data modeling via Pydantic and Dataclasses. By applying this skill, developers can ensure their codebases utilize efficient asynchronous patterns, standardized error handling, and sophisticated testing strategies with Pytest. It is specifically designed for software engineers building scalable backends, APIs, and CLI tools who want to move beyond basic scripts and into production-grade Python architecture.

Características Principales

Modern Python 3.10+ syntax and type hinting (including | union operators)
Advanced data validation patterns using Pydantic V2 and Dataclasses
Standardized Pytest implementation including fixtures, mocking, and parametrization
Structural pattern matching and idiomatic common expressions (Walrus operator, etc.)
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Asynchronous I/O patterns including concurrent execution and async context managers

Casos de Uso

Refactoring legacy Python code to modern, type-safe standards
Implementing complex asynchronous workflows for high-performance I/O tasks
Designing robust backend architectures with strict data validation requirements

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Características Principales

Modern Python 3.10+ syntax and type hinting (including | union operators)
Advanced data validation patterns using Pydantic V2 and Dataclasses
Standardized Pytest implementation including fixtures, mocking, and parametrization
Structural pattern matching and idiomatic common expressions (Walrus operator, etc.)
0 GitHub stars
Asynchronous I/O patterns including concurrent execution and async context managers

Casos de Uso

Refactoring legacy Python code to modern, type-safe standards
Implementing complex asynchronous workflows for high-performance I/O tasks
Designing robust backend architectures with strict data validation requirements