umap-learn
Applies UMAP-learn to nonlinear dimensionality reduction, 2D and 3D embeddings, clustering preprocessing and supervised or parametric UMAP.
- Kind
- Skill
- Works with
- Claude Code, Codex, Cursor, Gemini / Antigravity
- Source
- Scientific Agent Skills (K-Dense) @ 5f2bc0341354
- Path
- skills/umap-learn/SKILL.md
- Jobs
- Implementation
- Topics
- LLM apps & RAG, Machine learning
Credit
- Made by
- K-Dense-AI
- Original
- github.com/K-Dense-AI/scientific-agent-skills/blob/5f2bc03413…
- License
- MIT
Listed by Skill Manifest; not made by us.
InstallInstall it by hand, following the source.
gh skill install K-Dense-AI/scientific-agent-skills umap-learn
Install factsScanned 2 Nov 2026
- Adds
- .claude/skills/umap-learn/ (the skill folder)
- 1 file(s) the skill refers to
- Runs scripts
- Yes
- shell block in the text
- Uses the network
- Yes
- downloads Python packages
- Needs secrets
- None found
- Source revision
- 5f2bc0341354
- Licence
- MIT
- Last activity
- 26 Oct 2026
- Review
- Reviewed
Found by a static scan of the stored text; nothing is run. “None found” is not a guarantee, so read what you install before you run it.
- Only the main file is read; other files in the folder are not scanned
# 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 approximate neighborhood structure, supervised learning, and clustering preprocessing. Before interpreting an embedding, check finite coordinates, neighborhood retention, sensitivity to seeds/parameters, and domain evidence in the original space. UMAP axes, island areas, and gaps between clusters have no calibrated physical or probabilistic meaning. Keep predictive test data outside preprocessing and embedding fits. ## Quick Start ### Installation Verified on 2026-10-01: **umap-learn 0.5.12** is the current stable release (April 2026). Requires Python 3.9+ and depends on `scikit-learn>=1.6`, `numba`, `pynndescent`, `numpy`, and `scipy`. Pin to a verified release: ```bash uv pip install umap-learn==0.5.12 ``` ### Basic Usage …