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python-init

Scaffold Python package projects with strict linting, type checking, and testing configurations.

Updated May 15, 2026
Search Tags:#pytest#python#uv#mypy#ruff#src-layout#package scaffolding
One-click install
npx skills add https://github.com/M4RC0Sx/claude-skills --skill python-init-m4rc0sx
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: python-init
Source: https://github.com/M4RC0Sx/claude-skills/tree/main/skills/python-init
Command: npx skills add https://github.com/M4RC0Sx/claude-skills --skill python-init-m4rc0sx

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Manually configuring Python package projects with consistent linting, type checking, and testing baselines is time-consuming and prone to version drift and inconsistent rule sets across team projects.

Core Features & Use Cases

  • Strict Baseline Automation: Automates setup of uv, Ruff with curated lint rules, strict mypy with extended error codes, pytest with common async and coverage plugins, and src-layout package structure.
  • Version Alignment: Prompts for a target Python version and configures all tooling to match, eliminating cross-tool version mismatch errors.
  • Use Case: Use this skill to quickly bootstrap new internal tools, CLIs, or analysis packages with a standardized, maintainable foundation that enforces best practices out of the box, no manual config required.

Quick Start

Use the python-init skill to scaffold a new Python package named 'my-data-tool' targeting Python 3.14 with the full strict baseline.

Frequently Asked Questions about python-init

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

FAQPage Schema
How do I bootstrap a Python package with strict linting and type checking?▼

You can bootstrap a Python package with strict linting and type checking by using an automated scaffolding tool to instantly generate a src-layout structure with pre-configured Ruff, mypy, and pytest environments.

What is the best way to configure uv for Python dependency management?▼

Configuring uv for Python dependency management is best handled by automated project initialization, which sets up the uv environment alongside strict mypy and Ruff configurations to prevent version drift across team projects.

How do I set up a Python project with src-layout and pytest?▼

You can set up a Python project with src-layout and pytest by using scaffolding tools that automatically generate the directory structure and pre-configure pytest with async, coverage, and mocking plugins.

Does this Python scaffolding approach support specific Python versions?▼

Yes, this Python scaffolding approach supports specific Python versions by prompting for a target version during initialization and configuring all tooling, including Ruff and mypy, to match that specified version.

Why should I use an automated Python package initializer instead of manual config?▼

Using an automated Python package initializer instead of manual config eliminates version drift, ensures consistent best practices across teams, and saves time setting up strict linting and testing baselines.

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