Agent skill · data analytics · k-dense-ai
umap-learn
Use UMAP-learn for nonlinear dimensionality reduction, 2D/3D embeddings, clustering preprocessing, supervised or semi-supervised UMAP, DensMAP, AlignedUMAP, and Parametric UMAP workflows.
Why this skill is useful
Provides domain-specific knowledge on UMAP parameter tuning and workflows that the AI wouldn't reliably generate on its own.
What it needs
Requires numba, numpy, pynndescent, scikit-learn, scipy, umap-learn installed locally. About 8k tokens when loaded. Last updated 2026-08-03. 32,849 stars on the source repository.
What this skill does
UMAP-Learn Overview UMAP (Uniform Manifold Approximation and Projection) is a dimensionality reduction technique for visualization and general non-linear dimensionality reduction. Apply this skill for fast, scalable embeddings that preserve local and global structure, supervised learning, and clustering preprocessing. Quick Start Installation Current stable release: umap-learn 0.5.12 (released April 2026). Requires Python 3.9+ and depends on scikit-learn>=1.6, numba, pynndescent, numpy, and scipy. Pin to a verified release: Basic Usage UMAP follows scikit-learn conventions and can be used as a drop-in replacement for t-SNE or PCA. Preprocessing requirement: Match preprocessing to the metric. For numeric Euclidean-style metrics, scale features before fitting so high-variance columns do not dominate. For cosine, binary, precomputed-distance, or mixed-feature workflows, choose preprocessing that matches the metric instead of blindly standardizing every column. Typical Workflow Parameter Tuning Guide UMAP has four primary parameters that control the embedding behavior. Understanding these is crucial for effective usage. nneighbors (default: 15) Purpose: Balances local versus global structure in the embedding. How it works: Controls the size of the local neighborhood UMAP examines when learning manifold structure. Effects by value: Low values (2-5): Emphasizes fine local detail but may fragment data into disconnected components Medium values (15-20): Balanced view of both local structure and global relationships (recommended starting point) High values (50-200): Prioritizes broad topological structure at the expense of fine-grained details Recommendation: Start with 15 and adjust based on results. Increase for more global structure, decrease for more local detail. mindist (default: 0.1) Purpose: Controls how tightly points cluster in the low-dimensional space. How it works: Sets the minimum distance apart that points are allowed to be in the output representation. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills k-dense-ai/umap-learn