umap-learn

combined

UMAP dimensionality reduction. Fast nonlinear manifold learning for 2D/3D visualization, clustering preprocessing (HDBSCAN), supervised/parametric UMAP, for high-dimensional data. Applies UMAP-learn to nonlinear dimensionality reduction, 2D/3D embeddings, clustering preprocessing, supervised or semi-supervised UMAP, DensMAP, AlignedUMAP, and Parametric UMAP workflows.

When to use

2D/3D visualization
clustering preprocessing
supervised/parametric UMAP
high-dimensional data embedding
nonlinear dimensionality reduction
feature engineering for downstream ML models

Skill page

Conflict

The K-Dense-AI copy is the more rigorous, upstream-aligned procedure: it pins a verified release, adds interpretation caveats, and corrects the min_dist semantics. The Vibe-Skills and OpenClaw copies are near-identical mirrors that omit those caveats and include a parameter-tuning example block plus a fuller metric list. Because the K-Dense copy would hide the tuning examples and metric list, and the mirror copies would hide the version pin and interpretation caveats, crowning one would lose material steps.