By tykimos
Sun and Space Weather toolkit - solar data download, preprocessing, ML training, and visualization
Solar observation data downloader for SDO, STEREO, and Solar Orbiter missions. Use when Claude needs to: (1) download SDO/AIA EUV data from JSOC, (2) download STEREO/SECCHI/EUVI data, (3) download Solar Orbiter/EUI/FSI data, (4) create multi-date time series of solar observations, (5) pair multi-wavelength observations. Triggers: 'solar data download', 'SDO download', 'AIA data', 'STEREO data', 'Solar Orbiter data', 'FITS download', 'sun observation data', 'EUI data', 'EUVI data', '태양 데이터 다운로드', '태양 관측 데이터'
Machine learning for solar physics using preprocessed SSW data. Use when Claude needs to: (1) train deep learning models on solar EUV images, (2) build solar flare prediction models, (3) perform image-to-image translation between solar instruments, (4) detect/segment coronal holes or active regions, (5) create PyTorch/TensorFlow dataloaders for FITS files, (6) evaluate ML models on solar observation data. Triggers: 'solar ML', 'solar deep learning', 'flare prediction', 'coronal hole detection', 'instrument translation', 'solar image segmentation', 'FITS dataloader', 'solar neural network', '태양 ML', '태양 딥러닝', '플레어 예측', 'solar AI'
SDO/AIA solar data ML preprocessing pipeline. Use when Claude needs to: (1) preprocess AIA Level 1 FITS data for machine learning, (2) calibrate solar images (pointing, degradation, exposure), (3) register and normalize solar disk images, (4) batch convert raw FITS to ML-ready format, (5) standardize solar observation data for neural network training. Triggers: 'AIA preprocessing', 'solar data prep', 'FITS preprocessing', 'aia_prep_ml', 'ML-ready solar data', 'calibrate AIA', 'solar image registration', '태양 데이터 전처리', 'AIA 보정', 'ML 전처리'
Solar observation data visualization for EUV imagery, ML results, and analysis. Use when Claude needs to: (1) display solar EUV images from FITS files, (2) create multi-wavelength comparison panels, (3) make before/after preprocessing comparisons, (4) visualize ML model predictions on solar data, (5) create solar time-lapse animations, (6) plot intensity distributions of solar images. Triggers: 'solar visualization', 'solar image display', 'FITS visualization', 'EUV image plot', 'multi-wavelength comparison', 'solar animation', 'sun image', '태양 이미지 시각화', '태양 시각화', 'solar plot'
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Sun and Space Weather (SSW) toolkit for Claude Code. Download, preprocess, visualize, and apply machine learning to solar observation data from SDO, STEREO, and Solar Orbiter missions.
Claude Code plugins are extensions that add new capabilities to the Claude Code CLI. A plugin can provide:
/ssw-plugin:ssw-download) that give Claude specialized knowledge and workflowsPlugins are distributed through marketplaces - Git repositories that catalog one or more plugins. Users add a marketplace, then install individual plugins from it.
Open Claude Code and register the ssw-plugin marketplace:
Option A: Inside Claude Code (interactive)
/plugin marketplace add https://github.com/tykimos/ssw-plugin.git
Option B: From terminal (CLI)
claude plugin marketplace add https://github.com/tykimos/ssw-plugin.git
This clones the repository to ~/.claude/plugins/marketplaces/ssw-plugin/ and registers it in ~/.claude/plugins/known_marketplaces.json.
Option A: Inside Claude Code (interactive)
/plugin install ssw-plugin@ssw-plugin
The format is <plugin-name>@<marketplace-name>.
Option B: From terminal (CLI)
claude plugin install ssw-plugin@ssw-plugin
Option C: Interactive plugin manager
/plugin
This opens the plugin manager UI. Navigate to the Discover tab to browse and install available plugins.
You can control where the plugin is available:
# Available in all your projects (default)
claude plugin install ssw-plugin@ssw-plugin --scope user
# Available only in current project, shared with team via git
claude plugin install ssw-plugin@ssw-plugin --scope project
# Available only in current project, not shared (gitignored)
claude plugin install ssw-plugin@ssw-plugin --scope local
| Scope | Settings File | Shared via Git | Use Case |
|---|---|---|---|
user | ~/.claude/settings.json | No | Personal use across all projects |
project | .claude/settings.json | Yes | Team-wide plugin for a project |
local | .claude/settings.local.json | No | Personal use for one project |
After installation, verify the plugin is active:
/plugin
You should see ssw-plugin listed as Enabled. The following slash commands should be available:
/ssw-plugin:ssw-download/ssw-plugin:ssw-prep/ssw-plugin:ssw-ml/ssw-plugin:ssw-vizYou can also test by typing /ssw- and checking autocomplete suggestions.
The plugin skills require Python packages. Install them in your environment:
# Core: solar data download and preprocessing
pip install git+https://github.com/sswlab/ssw-tools
pip install sunpy matplotlib astropy aiapy
# Optional: for ML tasks (ssw-ml skill)
pip install torch torchvision scikit-image
# Optional: for logging in batch processing
pip install loguru
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