Nonlinear dimensionality reduction with UMAP — fast manifold learning for 2D/3D visualization, clustering preprocessing (e.g., HDBSCAN), and supervised or parametric UMAP. Use when projecting high-dimensional data to low dimensions for visualization, embedding generation, or as a preprocessing step before clustering. Part of the AlterLab Academic Skills suite.
Install
npx skills add AlterLab-IEU/AlterLab-Academic-Skills --skill alterlab-umapnpx skills add AlterLab-IEU/AlterLab-Academic-Skills --skill alterlab-umap -a claude-code -gnpx skills add AlterLab-IEU/AlterLab-Academic-Skills --skill alterlab-umap -a codex -ggemini skills install https://github.com/AlterLab-IEU/AlterLab-Academic-Skills.git --path skills/data-science/alterlab-umapgh skill install AlterLab-IEU/AlterLab-Academic-Skills alterlab-umapgit clone --depth 1 https://github.com/AlterLab-IEU/AlterLab-Academic-Skills /tmp/AlterLab-IEU__AlterLab-Academic-Skills
mkdir -p ~/.claude/skills ~/.agents/skills
cp -R /tmp/AlterLab-IEU__AlterLab-Academic-Skills/skills/data-science/alterlab-umap ~/.claude/skills/alterlab-umap # Claude Code
cp -R /tmp/AlterLab-IEU__AlterLab-Academic-Skills/skills/data-science/alterlab-umap ~/.agents/skills/alterlab-umap # Codex, Cursor, Gemini CLI, Copilot, OpenCode