What build backend is used?

It uses Hatchling, which is highly recommended for its speed, standards compliance, and excellent support for versioning and file discovery.

Can I use this for non-scientific Python projects?

Yes, while it follows scientific community standards, the use of Hatchling and pyproject.toml is considered a modern 'gold standard' for almost any Python package.

How does it handle development dependencies?

It utilizes PEP 735 dependency groups within pyproject.toml, allowing you to separate runtime requirements from testing and documentation tools.

Does this skill help with publishing to PyPI?

Yes, it provides a detailed workflow using 'build' to create distributions and 'twine' to securely upload them to PyPI and TestPyPI.

Why does this skill recommend the src layout?

The src layout is a best practice that prevents importing uninstalled code from the current directory, ensuring your tests run against the actual installed package.

Scientific Python Packaging

Name: Scientific Python Packaging
Author: uw-ssec

byuw-ssec

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데이터 과학 및 ML

Standardizes the creation and distribution of Python packages using modern Hatchling and PEP 621 conventions for scientific computing.

This skill provides a comprehensive framework for structuring, configuring, and publishing Python packages specifically tailored for the scientific community. It guides users through setting up pyproject.toml using the Hatchling build backend, enforces the src layout for better code isolation, and provides robust workflows for PyPI distribution. By following Scientific Python Community standards, it ensures research software is reproducible, professional, and ready for peer review or public release.

주요 기능

0118 GitHub stars

02Step-by-step publishing workflows for TestPyPI and PyPI

03Implementation of the src layout for robust testing and distribution

04Standardized pyproject.toml configuration with PEP 621 metadata

05Automated dependency management using PEP 735 dependency groups

06Hatchling build backend integration for modern package discovery

사용 사례

01Migrating legacy setup.py projects to modern, standards-compliant pyproject.toml

02Converting research scripts into distributable and installable Python libraries

03Setting up professional CLI tools for scientific data analysis workflows

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add uw-ssec/rse-plugins python-packaging

For use in Claude.ai and ChatGPT

Download Skill