py-standards

Enforce Python development standards with uv, ruff, and ty.

Updated Aug 27, 2026
One-click install
npx skills add https://github.com/dungle-scrubs/agent-config --skill py-standards
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: py-standards
Source: https://github.com/dungle-scrubs/agent-config/tree/main/stow/shared/skills/py-standards
Command: npx skills add https://github.com/dungle-scrubs/agent-config --skill py-standards

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill standardizes Python development practices to ensure consistent output and quality.

Core Features & Use Cases

  • uv-based dependency management for fast, reproducible environments
  • ruff-based linting and formatting to replace multiple tools
  • ty-based type checking to enforce data validation
  • Shared core pattern for API + CLI projects to avoid duplication

Quick Start

uv run ruff check . uv run ty check

Frequently Asked Questions about py-standards

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

FAQPage Schema
How do I set up Python project standards for uv, ruff, and type checking?

Python project standards are enforced via uv for dependency management, ruff for linting and formatting, and ty for type checking. This combination provides consistent project structure and reliable static analysis for API and CLI codebases.

What is the best way to replace multiple Python linting and formatting tools with ruff?

Using ruff replaces multiple Python linting and formatting tools with a single unified solution. It enforces code quality standards while reducing tool overhead and configuration complexity.

How do I manage Python dependencies with uv for reproducible environments?

Managing Python dependencies with uv ensures fast and reproducible environments. It standardizes dependency resolution and locks files to prevent version conflicts across different development setups.

Does this Python standards workflow support both API and CLI codebases?

Yes, the Python standards workflow supports both API and CLI codebases. It applies a shared core pattern to avoid code duplication and enforces consistent output and quality across different project types.

Why use ty for type checking in a Python project?

Using ty for type checking enforces strict data validation in Python projects. It catches type-related errors during development before runtime, ensuring machine-friendly documentation and reliable static analysis.