scientific-python-development
By uw-ssec
Automate end-to-end best practices for scientific Python projects: initialize reproducible pixi environments with conda/PyPI deps, enforce code quality via ruff/mypy/pre-commit, build pytest numerical tests, create distributable Hatchling packages, and generate Sphinx/MkDocs docs with NumPy-style docstrings and Diataxis structure.
Component Overview
Install
npx claudepluginhub uw-ssec/rse-plugins --plugin scientific-python-developmentComponent Details
Commands (5)
Set up or fix ruff, mypy, and pre-commit for scientific Python code quality
Set up or manage pixi environments for reproducible scientific Python workflows
Set up or improve Python package structure with pyproject.toml, src layout, and Hatchling following Scientific Python standards
Write or improve pytest tests for scientific Python code with fixtures, parametrization, and numerical testing patterns
Set up or improve documentation for a scientific Python package using Sphinx, MkDocs, and Diataxis framework
Agents (2)
Expert scientific Python documentation architect specializing in research software documentation following the Diataxis framework. Creates comprehensive documentation including API references, tutorials, how-to guides, and explanations for scientific codebases.
Expert scientific Python developer for research computing, data analysis, and scientific software. Specializes in NumPy, Pandas, Matplotlib, SciPy, and modern reproducible workflows with pixi. Follows Scientific Python community best practices.
Skills (5)
Configure and use automated code quality tools (ruff, mypy, pre-commit) for scientific Python projects. Covers linting rules, type checking configuration, formatting, and CI integration.
Manage scientific Python dependencies and environments using pixi package manager with unified conda-forge and PyPI support, task automation, and reproducible lockfiles.
Create and publish distributable scientific Python packages following Scientific Python community best practices. Covers pyproject.toml, src layout, Hatchling, metadata, CLI entry points, and PyPI publishing.
Write and organize tests for scientific Python packages using pytest. Covers fixtures, parametrization, numerical testing with NumPy utilities, property-based testing with Hypothesis, and CI integration.
Set up and maintain documentation for scientific Python packages. Covers Sphinx, MkDocs, NumPy-style docstrings, Diataxis framework, accessibility standards, and documentation hosting with Read the Docs.
README
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