Custom AI agents and skills for Research Software Engineering (RSE) and Scientific Computing tasks, designed for use with Claude Code and compatible AI coding assistants.
This repository provides specialized agents and skills that understand the unique challenges of scientific software development, including:
- Modern Scientific Python development following community best practices
- Reproducible environment management with pixi
- Python packaging and distribution with pyproject.toml
- Comprehensive testing strategies with pytest
- Scientific computing workflows and numerical methods
- Research software engineering practices
- Domain-specific scientific computing (astronomy, geospatial analysis, climate science)
- Interactive data visualization with the HoloViz ecosystem (Panel, hvPlot, HoloViews, Datashader, GeoViews, Lumen)
- Scientific Python ecosystem (NumPy, Pandas, SciPy, Matplotlib, Xarray, Astropy, etc.)
To use these agents and skills in Claude Code, add this repository to your plugin marketplace:
/plugin marketplace add uw-ssec/rse-pluginsOnce installed, the agents and skills will be available in your Claude Code environment and can be invoked when working on scientific software projects.
The repository provides Claude Code plugins organized by domain. Each plugin contains agents (specialized AI personas) and skills (reusable knowledge modules).
Expert agents and comprehensive skills for modern Scientific Python development.
Agents:
- Scientific Python Expert - Comprehensive agent for scientific Python development following Scientific Python Development Guide best practices
Skills:
- pixi-package-manager - Fast, reproducible scientific Python environments with unified conda and PyPI management
- python-packaging - Modern packaging with pyproject.toml, src layout, and Hatchling build backend
- python-testing - Robust testing strategies with pytest following Scientific Python community guidelines
- code-quality-tools - Linting, formatting, and type checking tools for Python code quality
When to use: Scientific computing projects, data analysis pipelines, research software development, package creation, reproducible research workflows
Domain-specific scientific computing agents and skills for astronomy, geospatial analysis, climate science, and interactive visualization.
Agents:
- Astronomy & Astrophysics Expert - Expert in astronomical data analysis, FITS files, coordinate systems, and photometry/spectroscopy pipelines with Astropy
Skills:
- xarray-for-multidimensional-data - Work with labeled multidimensional arrays and NetCDF/Zarr datasets for climate and Earth science
- astropy-fundamentals - Astronomical data formats, coordinate transformations, physical units, and time handling with Astropy
When to use: Astronomy research, telescope data processing, climate data analysis, Earth science workflows, geospatial analysis
Expert agents and comprehensive skills for interactive data visualization using the HoloViz ecosystem (Panel, hvPlot, HoloViews, Datashader, GeoViews, Lumen).
Agents:
- Panel Specialist - Expert in building interactive dashboards, web applications, and component systems with Panel and Param
- Visualization Designer - Strategic guide for multi-library visualization design using HoloViz ecosystem tools
- Data Engineer - Specialist in large-scale data rendering and performance optimization with Datashader (100M+ points)
- Geo-Spatial Expert - Expert in geographic and mapping visualizations with GeoViews and spatial data handling
Skills:
- panel-dashboards - Interactive dashboard and application development with Panel and Param
- plotting-fundamentals - Quick plotting and interactive visualization with hvPlot
- data-visualization - Advanced declarative visualization with HoloViews
- advanced-rendering - High-performance rendering for large datasets with Datashader
- geospatial-visualization - Geographic and mapping visualizations with GeoViews
- colormaps-styling - Color management and visual styling with Colorcet
- parameterization - Declarative parameter systems with Param for type-safe configuration
- lumen-dashboards - Declarative, no-code data dashboards with Lumen YAML specifications
- lumen-ai - AI-powered natural language data exploration with Lumen AI
When to use: Interactive dashboards, web applications, large-scale data visualization, geographic mapping, real-time data streaming, exploratory data analysis, publication-quality visualizations
Browse the plugins directory to explore all available plugins.
rse-plugins/
├── .claude-plugin/
│ └── marketplace.json # Claude plugin marketplace configuration
├── plugins/
│ ├── agents/ # All agent definitions
│ │ ├── scientific-python-expert.md
│ │ ├── astronomy-astrophysics-expert.md
│ │ ├── panel-specialist.md
│ │ ├── visualization-designer.md
│ │ ├── data-engineer.md
│ │ └── geo-spatial-expert.md
│ ├── skills/ # All skill modules
│ │ ├── pixi-package-manager/
│ │ ├── python-packaging/
│ │ ├── python-testing/
│ │ ├── code-quality-tools/
│ │ ├── xarray-for-multidimensional-data/
│ │ ├── astropy-fundamentals/
│ │ ├── panel-dashboards/
│ │ ├── plotting-fundamentals/
│ │ ├── data-visualization/
│ │ ├── advanced-rendering/
│ │ ├── geospatial-visualization/
│ │ ├── colormaps-styling/
│ │ ├── parameterization/
│ │ ├── lumen-dashboards/
│ │ └── lumen-ai/
│ └── resources/ # Supporting resources
│ └── holoviz/ # HoloViz ecosystem documentation
│ ├── holoviz-ecosystem.md
│ ├── library-matrix.md
│ ├── best-practices/
│ ├── patterns/
│ ├── troubleshooting/
│ ├── lumen-dashboards/
│ ├── lumen-ai/
│ └── colormaps/
├── CONTRIBUTING.md # Contribution guidelines
├── LICENSE # BSD 3-Clause License
└── README.md # This file
This repository uses the Claude Code plugin marketplace architecture:
- Plugins - Top-level containers organized by domain (e.g., python-development, scientific-computing)
- Agents - Specialized AI personas with comprehensive expertise in specific areas
- Skills - Reusable knowledge modules that provide detailed guidance on specific topics
The agents and skills follow the Scientific Python Development Guide principles:
- Collaborate - Adopt conventions and tooling used by the broader scientific Python community
- Refactor Fearlessly - Leverage tests and tools to enable confident iteration
- Prefer Wide Over Deep - Build reusable, extensible solutions for unforeseen applications
- Agents - Comprehensive personas that handle complete workflows, make decisions, and provide end-to-end guidance
- Skills - Focused knowledge modules on specific topics (e.g., testing patterns, packaging workflows) that agents can reference
We welcome contributions of new agents, skills, and improvements! Please see CONTRIBUTING.md for guidelines on:
- Creating new agents and skills
- Plugin organization and structure
- Naming conventions and best practices
- Testing and validation
- Submitting pull requests
For detailed information about the plugins and their contents:
- Contributing Guidelines - How to contribute to this repository
- HoloViz Ecosystem Overview - Introduction to the HoloViz ecosystem
- HoloViz Library Matrix - Comparison of HoloViz libraries and when to use each
- Scientific Python Development Guide - Community best practices
- Scientific Python Lectures - Educational materials
- NumPy, SciPy, Pandas - Core libraries
- HoloViz.org - Main HoloViz ecosystem portal
- Panel - Build interactive dashboards and web applications
- hvPlot - High-level plotting API for pandas and xarray
- HoloViews - Declarative data visualization
- Datashader - Render large datasets accurately
- GeoViews - Geographic data visualization
- Lumen - No-code dashboards with AI capabilities
- Param - Declarative parameter management
- Colorcet - Perceptually uniform colormaps
- Astropy - Astronomy and astrophysics in Python
- Xarray - Labeled multidimensional arrays for climate and Earth science
- UW Scientific Software Engineering Center
- Best Practices for Scientific Computing
- The Turing Way - Guide to reproducible research
This project is licensed under the BSD 3-Clause License - see the LICENSE file for details.
Developed and maintained by the University of Washington Scientific Software Engineering Center (UW-SSEC).
Please open an issue on GitHub.