rse-plugins

Configure and use automated code quality tools (ruff, mypy, pre-commit) for scientific Python projects. Use when setting up linting, formatting, type checking, or automated quality gates. Ideal for enforcing code style, catching type errors, managing pre-commit hooks, or integrating quality checks in CI/CD pipelines.

Manage scientific Python dependencies and environments using pixi package manager with unified conda-forge and PyPI support. Use when setting up project environments, managing dependencies, creating reproducible workflows, or working with complex scientific packages requiring compiled libraries. Ideal for multi-environment projects, cross-platform development, and replacing conda/mamba workflows.

Create and publish distributable scientific Python packages following Scientific Python community best practices with pyproject.toml, src layout, and Hatchling. Use when building Python libraries, publishing to PyPI, structuring research software, creating command-line tools, or preparing packages for distribution. Ideal for package metadata configuration, dependency management, and automated publishing workflows.

Write and organize tests for scientific Python packages using pytest following Scientific Python community best practices. Use when setting up test suites, writing unit tests, integration tests, testing numerical algorithms, configuring test fixtures, parametrizing tests, or setting up continuous integration. Ideal for testing scientific computations, validating numerical accuracy, and ensuring code correctness.

This skill should be used when the user asks to "set up documentation", "create docs for Python package", "configure Sphinx", "set up MkDocs", "write docstrings", "use NumPy-style docstrings", "set up Read the Docs", "integrate Jupyter notebooks in docs", "organize documentation with Diataxis", "create API reference docs", "build documentation with nox", "fix documentation build errors", "documentation build fails", "sphinx warning", "autodoc error", "fix sphinx errors", "make documentation accessible", "accessibility guidelines for docs", "accessible images", "alt text for figures", "colorblind-friendly plots", "color contrast in docs", or needs guidance on scientific Python documentation best practices, Sphinx extensions, documentation themes (pydata-sphinx-theme, furo, material), documentation hosting, accessibility standards, or troubleshooting documentation issues.

Work with astronomical data using Astropy for FITS file I/O, coordinate transformations, physical units, precise time handling, and catalog operations. Use when processing telescope images, matching celestial catalogs, handling time-series observations, or building photometry/spectroscopy pipelines. Ideal for astronomy research requiring proper unit handling, coordinate frame transformations, and astronomical time scales.

Work with labeled multidimensional arrays for scientific data analysis using Xarray. Use when handling climate data, satellite imagery, oceanographic data, or any multidimensional datasets with coordinates and metadata. Ideal for NetCDF/HDF5 files, time series analysis, and large datasets requiring lazy loading with Dask.