python

Standardize Python development with type hints, Pydantic v2, and DI principles.

21|2|Updated Jan 17, 2026

Search Tags:#testing #documentation #python #code-quality #best-practices #type-hints

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npx skills add https://github.com/siviter-xyz/dot-agent --skill python-siviter-xyz

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Please help me install this Agent Skill.
Skill: python
Source: https://github.com/siviter-xyz/dot-agent/tree/main/skills/python
Command: npx skills add https://github.com/siviter-xyz/dot-agent --skill python-siviter-xyz

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This skill provides clear Python development guidelines and best practices to improve code quality, readability, and maintainability across projects.

Core Features & Use Cases

Design Principles: Emphasizes DRY, KISS, and SOLID, promotes composition with Protocol interfaces, and supports single-responsibility modules.
Code Quality & Style: Encourages descriptive naming, comprehensive docstrings, consistent type hints, and disciplined imports.
Testing & Architecture: Recommends pytest-oriented testing structure, mock usage, and guided project organization; includes environment and data-model patterns using Pydantic v2.

Quick Start

Scaffold a Python package that includes type hints, docstrings, DI-friendly design, and unit tests.

Frequently Asked Questions about python

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

FAQPage Schema

What are the best practices for structuring a Python project with type hints and Pydantic v2?▼

Python project structuring best practices involve applying DRY, KISS, and SOLID principles alongside single-responsibility modules. You should use consistent type hints, Pydantic v2 data models, and Protocol interfaces for composition to ensure maintainability and robust design.

How do I set up pytest and mocks for Python code quality?▼

Setting up pytest for Python code quality requires a pytest-oriented testing structure combined with proper mock usage. This testing approach validates your modules while design principles like dependency injection keep your codebase maintainable and robust.

Can I apply SOLID and dependency injection principles in any size Python project?▼

Yes, SOLID and dependency injection principles apply to Python projects of any size. This approach standardizes development practices to elevate code quality and maintainability, guiding design decisions, style, testing, and environment patterns across small and large codebases.

What's the best way to enforce DRY and KISS principles in Python development?▼

The best way to enforce DRY and KISS principles in Python development is standardizing style with descriptive naming, comprehensive docstrings, and disciplined imports. Composition with Protocol interfaces and single-responsibility modules further elevates code quality and readability.

Do I need type hints and docstrings for every Python module?▼

Yes, type hints and docstrings are needed for Python modules to meet code quality standards. Encouraging descriptive naming, comprehensive docstrings, and consistent type hints ensures readability and maintainability across your project's design decisions and environment patterns.