WSClean Agent Skill

A comprehensive Cursor agent skill for WSClean radio interferometry imaging.

Overview

This skill enables Cursor agents to work with WSClean, a radio interferometry imaging and deconvolution tool used with data from telescopes like LOFAR, MWA, VLA, ASKAP, and SKA.

Installation

Option 1: Via skills.sh (if published to GitHub)

npx skills add YOUR_USERNAME/wsclean-cursor-skill

Option 2: Direct Install (works with any git host)

For Cursor and other compatible agents:

mkdir -p ~/.agents/skills
cd ~/.agents/skills
git clone https://gitlab.com/YOUR_USERNAME/wsclean-cursor-skill.git wsclean
# Or from GitHub: git clone https://github.com/YOUR_USERNAME/wsclean-cursor-skill.git wsclean

The skill will be automatically available to Cursor agents.

For Docker Users

WSClean is available via Docker (compilation is complex):

docker run --rm -it d3vnull0/rapthor-mildtec:smoke wsclean --help

Structure

• SKILL.md - Main skill file with essential commands and workflows
• reference/ - Detailed documentation
  • parameters.md - Complete parameter reference
  • workflows.md - Advanced workflow examples
  • troubleshooting.md - Troubleshooting guide

Usage

Once installed, Cursor agents can:

• Generate WSClean commands for various imaging scenarios
• Explain parameters and options
• Troubleshoot common issues
• Suggest optimal settings for different data types
• Create complete imaging workflows

Example Prompts

• "Create a WSClean command for deep multi-scale cleaning"
• "How do I do multi-frequency synthesis with 8 channels?"
• "My cleaning is diverging, help me troubleshoot"
• "Set up a self-calibration imaging workflow"
• "Explain the difference between mgain and gain parameters"

Features

• Complete parameter documentation - All WSClean 3.6 parameters with defaults and ranges
• Ready-to-use workflows - Common and advanced imaging scenarios
• Docker integration - Examples for containerized WSClean
• Troubleshooting guide - Solutions for common problems
• Best practices - Recommended settings for different scenarios

Supported WSClean Features

• Basic imaging and CLEAN deconvolution
• Multi-scale cleaning for resolved sources
• Multi-frequency synthesis (wideband imaging)
• Polarimetric imaging (Stokes I,Q,U,V and instrumental)
• Facet-based imaging with direction-dependent corrections
• Primary beam correction (LOFAR, MWA, VLA, etc.)
• Self-calibration model generation
• Spectral line imaging
• RM synthesis

Requirements

• WSClean 3.6 or compatible version
• Or Docker with d3vnull0/rapthor-mildtec:smoke image
• Radio interferometry data in Measurement Set (.ms) format

Documentation

• WSClean Official Docs
• Algorithm Paper - Offringa & Smirnov 2017

Contributing

Contributions welcome! Please:

1. Fork the repository
2. Create a feature branch
3. Submit a pull request

Areas for contribution:

• Additional workflow examples
• Telescope-specific optimizations
• More troubleshooting scenarios
• Performance tuning guides

License

This skill documentation is provided as-is for educational purposes.

WSClean itself is GPL v3 licensed. See WSClean repository for details.

Version

Skill Version: 1.0.0
WSClean Version Documented: 3.6 (2025-02-07)

Author

Created for Cursor agent skills.

Changelog

v1.0.0 (2026-04-23)

• Initial release
• Complete parameter documentation
• Common and advanced workflows
• Comprehensive troubleshooting guide