RSE Plugins
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.
Purpose
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.)
Installation
To use these agents and skills in Claude Code, add this repository to your plugin marketplace:
/plugin marketplace add uw-ssec/rse-plugins
Once installed, the agents and skills will be available in your Claude Code environment and can be invoked when working on scientific software projects.
Available Plugins
The repository provides Claude Code plugins organized by domain. Each plugin contains agents (specialized AI personas) and skills (reusable knowledge modules).
Scientific Python Development Plugin
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
- Scientific Documentation Architect - Expert in creating comprehensive, user-friendly documentation for scientific software following Scientific Python community standards
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
- scientific-documentation - Documentation best practices for scientific software including Sphinx, API docs, tutorials, and examples
When to use: Scientific computing projects, data analysis pipelines, research software development, package creation, reproducible research workflows
Scientific Domain Applications Plugin
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
AI Research Workflows Plugin
Structured AI-enabled workflow for complex software development tasks with explicit phases for research, planning, experimentation, implementation, and validation.
Agent:
- Research Workflow Orchestrator - Guides users through structured development workflows from research to validated implementation
Commands:
/research- Document and understand existing code, patterns, and architecture/plan- Create detailed, testable implementation plans through interactive research/iterate-plan- Refine existing plans based on feedback or changed requirements/experiment- Try multiple approaches before committing to implementation (optional)/implement- Execute the plan phase by phase with verification checkpoints/validate- Systematically verify implementation against plan criteria
Skill:
- research-workflow-management - Systematic workflow methodology creating auditable trail of technical decisions in
.agents/directory
When to use: Complex feature development, architectural changes, exploratory implementation, technical research tasks, systematic code refactoring, documented decision-making