How are tests structured with this skill?

It utilizes pytest with a specific focus on parameterized test cases and descriptive IDs to create comprehensive, readable, and maintainable test suites.

Does it support data validation and modeling?

Yes, the skill promotes using Pydantic for data transfer objects (DTOs) and sophisticated data modeling, while using standard dataclasses for simpler needs.

What coding style does this skill enforce?

It follows PEP 8 guidelines but adopts a modern 120-character line length, utilizing Black for consistent formatting and readability.

Can I use this for building command-line tools?

Absolutely. The skill recommends Typer for feature-rich CLI development but can also use argparse when you need to avoid external dependencies.

How does this skill handle Python package management?

The skill defaults to using 'uv' for dependency management and execution, including the implementation of PEP 723 script headers for standalone scripts.

Python Development Standards

Name: Python Development Standards
Author: tstapler

bytstapler

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API Development

Applies modern Python coding standards and best practices for type-safe, performant, and maintainable applications.

About

The Python Development Standards skill equips Claude with specialized knowledge of the modern Python ecosystem, prioritizing tools like uv for package management and Pydantic for data validation. It ensures all generated code follows PEP 8 guidelines with a 120-character line limit, utilizes comprehensive type annotations, and implements robust testing patterns with pytest. By enforcing architectural principles like the Facade pattern and Dependency Injection, this skill helps developers build professional-grade API clients, CLIs, and backend services that are consistent, scalable, and easy to maintain.

Key Features

Modern package management using uv and PEP 723 script headers
Strict type annotation enforcement for improved code clarity and IDE support
Advanced data modeling using Pydantic DTOs and dataclasses
Comprehensive pytest patterns with parameterized test cases
5 GitHub stars
Standardized code styling following PEP 8 and Black formatting rules

Use Cases

Generating robust unit tests for complex business logic with descriptive IDs
Scaffolding new Python projects with optimized dependency management and structure
Refactoring legacy Python code to utilize modern type hints and validation models

About

Key Features

Modern package management using uv and PEP 723 script headers
Strict type annotation enforcement for improved code clarity and IDE support
Advanced data modeling using Pydantic DTOs and dataclasses
Comprehensive pytest patterns with parameterized test cases
5 GitHub stars
Standardized code styling following PEP 8 and Black formatting rules

Use Cases

Generating robust unit tests for complex business logic with descriptive IDs
Scaffolding new Python projects with optimized dependency management and structure
Refactoring legacy Python code to utilize modern type hints and validation models