python-best-practices

Apply Python best practices for style, typing, and testing.

Updated Dec 4, 2025

Search Tags:#pytest #python #ruff #black #best-practices #typing #design-patterns

Ready to run

npx skills add https://github.com/eous/dotclaude --skill python-best-practices-eous

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: python-best-practices
Source: https://github.com/eous/dotclaude/tree/main/skills/python-best-practices
Command: npx skills add https://github.com/eous/dotclaude --skill python-best-practices-eous

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Python development often suffers from inconsistent style, unclear typing, and brittle project structures. This Skill guides teams to adopt consistent conventions, robust typing, and scalable patterns across Python codebases.

Core Features & Use Cases

Enforces naming, formatting, and style guidelines for Python codebases.
Promotes typing with Type hints, Pydantic models, and dataclasses for reliable data handling.
Covers async patterns, testing practices, and project organization to reduce cognitive load.
Use Cases: applying to new projects, code reviews, or refactoring sessions to align with best practices.

Quick Start

Provide a Python code snippet or project context and have the assistant apply Python best practices to improve structure, typing, and testing.

Frequently Asked Questions about python-best-practices

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

FAQPage Schema

How do I enforce consistent Python code style and typing conventions across my project?▼

Enforce Python code style and typing by applying standard conventions using tools like Black for formatting, Ruff for linting, and mypy for type checking. This approach ensures consistent naming, robust type hints, and clean data models across the codebase.

What's the best way to structure a FastAPI or Django project for reliability?▼

Structure FastAPI or Django projects by adopting scalable patterns and robust typing using Pydantic models and dataclasses. Organizing project structure this way reduces cognitive load and ensures reliable data handling across common Python ecosystems.

Does this approach work with both standard pytest testing and async patterns?▼

Yes, this approach works with pytest testing and async patterns by specifying testing practices and asynchronous conventions. It targets pytest for test suites and covers async patterns to ensure reliable execution within typical Python projects.

How do I apply Python best practices during a code review or refactoring session?▼

Apply Python best practices during reviews or refactoring by providing the code snippet or project context to the assistant. The assistant then improves structure, typing, and testing alignment to guide teams toward consistent conventions.

When should I introduce type hints and data models in my Python codebase?▼

Introduce type hints and data models like Pydantic or dataclasses early to prevent brittle project structures and unclear typing. Promoting typing ensures reliable data handling and reduces cognitive load for teams scaling Python codebases.