skills/motif-clustering/SKILL.md
# motif-clustering Clusters melodic motifs using scikit-learn (KMeans or hierarchical clustering) with optional UMAP projection. Takes motif JSON (from motif-detection) and groups them by interval similarity. ## Usage ```bash python3 skills/motif-clustering/scripts/motif_clustering.py --query "bach motifs" --n-clusters 8 --method kmeans python3 skills/motif-clustering/scripts/motif_clustering.py --query "bach motifs" --method hierarchical ``` ## Output ```json { "method": "kmeans", "n_c
npx skillsauth add lamm-mit/scienceclaw skills/motif-clusteringInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Clusters melodic motifs using scikit-learn (KMeans or hierarchical clustering) with optional UMAP projection. Takes motif JSON (from motif-detection) and groups them by interval similarity.
python3 skills/motif-clustering/scripts/motif_clustering.py --query "bach motifs" --n-clusters 8 --method kmeans
python3 skills/motif-clustering/scripts/motif_clustering.py --query "bach motifs" --method hierarchical
{
"method": "kmeans",
"n_clusters": 8,
"clusters": [
{
"cluster_id": 0,
"n_members": 12,
"centroid_intervals": [2, -1, 2],
"genre_distribution": {"baroque": 8, "folk": 4},
"top_motifs": ["m_0001", "m_0012"]
}
],
"silhouette_score": 0.47,
"inertia": 234.5
}
tools
Onboard and manage Paperclip AI for research-paper knowledge and agent orchestration
development
Perform AI-powered web searches with real-time information using Perplexity models via LiteLLM and OpenRouter. This skill should be used when conducting web searches for current information, finding recent scientific literature, getting grounded answers with source citations, or accessing information beyond the model knowledge cutoff. Provides access to multiple Perplexity models including Sonar Pro, Sonar Pro Search (advanced agentic search), and Sonar Reasoning Pro through a single OpenRouter API key.
testing
Generate a structured scientific PDF report from a JSON description. Accepts a JSON file specifying title, authors, abstract, sections (headings, text, tables, figures), and inline data panels (heatmap, bar, scatter, line). Produces a publication-style A4 PDF using reportlab with no LaTeX dependency. All figures are either loaded from PNG paths or generated on-the-fly from inline data.
development
Execute arbitrary Python code and return stdout. NumPy, pandas, scipy, matplotlib, and other scientific libraries are available.