python

Provide Python development guidelines for design, coding, testing, and deployment.

Updated Mar 24, 2026
One-click install
npx skills add https://github.com/tkmh04/CoffeeHouse-Management-System --skill python-tkmh04
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: python
Source: https://github.com/tkmh04/CoffeeHouse-Management-System/tree/main/.agents/skills/python
Command: npx skills add https://github.com/tkmh04/CoffeeHouse-Management-System --skill python-tkmh04

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Python development guidelines establish a standard set of best practices for writing maintainable, robust Python code across teams and projects, reducing bugs and onboarding time.

Core Features & Use Cases

  • Enforces design principles (DRY, KISS, SOLID) and consistent typing.
  • Provides environment configuration, testing, and deployment guidance for Python projects.
  • Use Case: When starting a new Python project or refactoring, apply these guidelines to ensure clean architecture and predictable quality.

Quick Start

Follow these guidelines when starting or reviewing Python code to ensure consistency and quality.

Frequently Asked Questions about python

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

FAQPage Schema
What are the best Python guidelines for writing clean and scalable code?

Python guidelines for clean code enforce design principles like DRY, KISS, and SOLID alongside consistent type annotations to improve maintainability and reduce bugs across development teams.

How do I structure a Python monorepo using uv and Hatch?

Python monorepo setups managed by uv and Hatch are supported through specific environment configuration and deployment guidance, ensuring consistent dependency handling and project structure across interconnected packages.

How do I enforce type annotations and dependency injection in Python projects?

Type annotations and dependency injection are enforced through established Python coding guidelines that apply design principles and typing standards to both new and existing project architectures.

What testing practices should I follow for Python code quality?

Python code quality testing practices are defined within the development guidelines, providing specific standards for environment handling and test execution to ensure predictable and robust application behavior.

Can I apply these Python guidelines to existing codebases during a refactor?

These Python guidelines apply to both new and existing projects, offering actionable design, coding, and testing standards to ensure clean architecture and predictable quality when refactoring legacy code.