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project-guidelines

Configure uv/Jupyter Python projects with src, notebooks, tests, and quality checks.

Updated Nov 15, 2025
One-click install
npx skills add https://github.com/nkhokhla/claude-setup --skill project-guidelines
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: project-guidelines
Source: https://github.com/nkhokhla/claude-setup/tree/main/.claude-example/.claude/skills/project-guidelines
Command: npx skills add https://github.com/nkhokhla/claude-setup --skill project-guidelines

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill eliminates the struggle of inconsistent Python project setups, complex dependency management, and manual quality checks. It provides a clear, automated path to a well-structured and maintainable data analysis project, saving you time and reducing setup headaches.

Core Features & Use Cases

  • Standardized Python Setup: Ensures consistent project structure and best practices for Python 3.12+ with uv and Jupyter.
  • Jupyter Integration: Guides on effectively using Jupyter notebooks for data exploration, separating exploratory code from reusable modules.
  • Automated Quality Checks: Integrates pytest, mypy, and ruff for automated testing, type checking, and linting, reducing manual review effort.
  • Use Case: When starting a new data analysis project, this Skill provides instant, tailored guidance on setting up your Python environment, structuring your code, and ensuring quality, allowing you to immediately focus on the analysis itself.

Quick Start

Help me set up a new Python data analysis project using uv and Jupyter. Provide the initial directory structure and key configuration files.

Frequently Asked Questions about project-guidelines

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

FAQPage Schema
How do I set up a Python project with uv and automated quality checks?▼

Set up a Python project with uv by creating a standardized directory structure (src/, notebooks/, tests/), configuring uv for dependency management, and integrating pytest, mypy, and ruff for automated testing, type checking, and linting. This ensures consistent code quality and reduces manual review effort across your workflow.

What's the best way to organize Jupyter notebooks alongside production Python code?▼

Organize Jupyter notebooks in a dedicated notebooks/ directory for exploratory data analysis, while keeping reusable code in src/ modules with type hints and Google-style docstrings. This separation lets you iterate quickly in Jupyter while maintaining clean, testable code for analysis logic.

Can I use uv for dependency management in a Jupyter-based data analysis project?▼

Yes, uv manages dependencies efficiently for Jupyter-based projects. It automates dependency resolution and versioning across your entire workflow, integrating seamlessly with Python 3.12+ environments and supporting both notebook exploration and modular code in src/.

How do I enforce type hints and code style across a Python data project?▼

Enforce type hints and code style by configuring mypy for type checking, ruff for linting and formatting, and pytest for unit testing. Automate these checks in your uv-based workflow to catch inconsistencies early without manual intervention.

What's included in a project-guidelines setup for Python 3.12?▼

A project-guidelines setup for Python 3.12 includes standardized directory structure (src/, notebooks/, tests/), uv dependency configuration, pytest unit tests, mypy type checking, ruff linting and formatting, and Google-style docstrings to eliminate setup inconsistencies.

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