UMAP dimensionality reduction. Fast nonlinear manifold learning for 2D/3D visualization, clustering preprocessing (HDBSCAN), supervised/parametric UMAP, for high-dimensional data.
8.3
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Machine Learning
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Excellent skill for UMAP dimensionality reduction. The description clearly conveys the capability (nonlinear manifold learning, visualization, clustering preprocessing, supervised/parametric variants). SKILL.md provides comprehensive task knowledge including critical preprocessing requirements, detailed parameter tuning guidance with practical effects, complete workflows for visualization/clustering/ML pipelines, supervised/semi-supervised usage, transform methods, and advanced features (Parametric UMAP, inverse transforms, AlignedUMAP). Structure is logical with clear sections progressing from basics to advanced usage. References API details appropriately in separate file. Novelty is strong - UMAP parameter tuning and proper configuration for different use cases (visualization vs clustering) requires significant domain knowledge that would consume many tokens for a CLI agent to derive. Minor deduction: while structure is good, a few sections could be slightly more concise, and novelty could be higher if UMAP weren't becoming a standard tool. Overall an exceptionally well-documented, immediately actionable skill.
K-Dense-AI
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