scientific-python-development

Expert scientific Python documentation architect specializing in research software documentation following the Diátaxis framework. Creates comprehensive documentation including API references, tutorials, how-to guides, and explanations for scientific codebases. Follows Scientific Python community best practices. Use this agent when the user asks to "create documentation", "set up docs for Python package", "document scientific code", "apply Diátaxis framework", "create API reference docs", "write documentation architecture", "set up Sphinx/MkDocs for scientific project", "create tutorials or how-to guides for research software", or needs comprehensive documentation planning for scientific Python codebases. <example> Context: User has a scientific Python package without documentation user: "I need to create comprehensive documentation for my astronomy data analysis library. It has about 20 modules and I want to follow best practices." assistant: "I'll use the scientific-docs-architect agent to create a comprehensive documentation plan for your astronomy library." <commentary> The user needs documentation architecture for a scientific codebase. This agent is ideal because it understands Diátaxis framework, NumPy-style docstrings, and scientific Python tooling conventions like Sphinx with intersphinx for cross-referencing scientific libraries. </commentary> </example> <example> Context: User wants to restructure existing documentation user: "Our scientific package docs are a mess. We have some docstrings and a few markdown files but no real structure. Can you help organize it using Diátaxis?" assistant: "I'll invoke the scientific-docs-architect agent to analyze your current documentation and create a Diátaxis-compliant structure." <commentary> Reorganizing documentation using Diátaxis framework is a key competency of this agent. It can analyze existing content, categorize it into tutorials/how-to/reference/explanation, and create a coherent navigation structure. </commentary> </example> <example> Context: User needs API documentation generated user: "Generate API documentation for my NumPy-based signal processing library" assistant: "I'll use the scientific-docs-architect agent to create comprehensive API reference documentation following NumPy-style conventions." <commentary> API reference generation with NumPy-style docstrings is explicitly covered by this agent. It understands numpydoc conventions, can configure autodoc/napoleon extensions, and knows how to document scientific parameters like array shapes and dtypes. </commentary> </example> <example> Context: User just completed implementing a scientific Python package user: "I just finished implementing my spectral analysis library with 15 modules" assistant: "Congratulations on completing your implementation! Your spectral analysis library would benefit from comprehensive documentation to help users understand and adopt it. I'll use the scientific-docs-architect agent to create a documentation plan following Scientific Python community standards." <commentary> The agent proactively offers documentation help after code completion, recognizing this as a natural workflow trigger point. It can analyze the new codebase structure and create a complete documentation scaffold with proper scientific context. </commentary> </example>

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 from https://learn.scientific-python.org/development/. Use this agent when the user asks to "create scientific Python code", "analyze data with NumPy/Pandas", "build research software", "set up pixi environment", "implement numerical algorithms", "create reproducible scientific workflows", or needs help with scientific computing, data analysis, or research software development. <example> Context: User needs to analyze scientific data user: "I have a CSV file with astronomical observations and need to calculate the mean magnitude and standard deviation for each star" assistant: "I'll use the scientific-python-expert agent to create a reproducible data analysis workflow for your astronomical observations." <commentary> This is a classic scientific data analysis task requiring NumPy/Pandas expertise, proper handling of numerical data, and reproducible workflows. </commentary> </example> <example> Context: User wants to create a new scientific Python package user: "I need to create a Python package for my spectral analysis research that follows community standards" assistant: "I'll invoke the scientific-python-expert agent to help you set up a properly structured scientific Python package with pixi for environment management." <commentary> Creating scientific packages with proper structure, testing, and environment management is a core competency of this agent. </commentary> </example> <example> Context: User has numerical code that needs improvement user: "My numerical simulation is giving inconsistent results between runs" assistant: "I'll use the scientific-python-expert agent to review your code for reproducibility issues like random seed management and numerical stability." <commentary> Proactive triggering for scientific code quality issues, particularly reproducibility - a key concern in research computing. </commentary> </example> <example> Context: User is starting a data science project user: "I'm starting a new research project analyzing climate data" assistant: "I'll use the scientific-python-expert agent to help you set up a reproducible scientific computing environment and data analysis workflow." <commentary> The agent should proactively engage when users mention research data analysis to ensure best practices from the start. </commentary> </example>

This skill should be used when the user asks to "set up ruff", "configure mypy", "add pre-commit hooks", "fix linting errors", "add type hints", "set up code formatting", "configure linters", "set up CI quality checks", "fix mypy errors", "configure ruff rules", or needs guidance on code quality tools (ruff, mypy, pre-commit), type checking configuration, linting rules for scientific Python, or automated code quality pipelines.

This skill should be used when the user asks to "set up pixi", "add pixi dependencies", "create pixi environment", "migrate from conda to pixi", "configure pixi.toml", "add pixi tasks", "set up reproducible environment", "manage conda-forge packages", "create multi-environment project", or needs guidance on pixi package manager, conda-forge integration, PyPI dependencies in pixi, pixi task automation, or replacing conda/mamba workflows with pixi.

This skill should be used when the user asks to "create a Python package", "set up pyproject.toml", "publish to PyPI", "configure Hatchling", "use src layout", "add package metadata", "create CLI tool", "set up package structure", "configure build backend", "add optional dependencies", or needs guidance on Python packaging best practices, pyproject.toml configuration, src layout structure, Hatchling build backend, PyPI publishing workflows, or Scientific Python packaging standards.

This skill should be used when the user asks to "write tests", "set up pytest", "add test fixtures", "parametrize tests", "test numerical code", "configure pytest", "add coverage", "write unit tests", "write integration tests", "test with numpy", "fix failing tests", or needs guidance on pytest configuration, test organization, scientific Python testing patterns, numerical algorithm testing, fixture design, or continuous integration for tests.

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.