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UMAP Dimensionality Reduction

Guides an agent through UMAP embeddings for visualization, clustering prep and supervised reduction.

Source Download skill
Repository
K-Dense-AI/scientific-agent-skills
Stars
55,780
Licence
MIT
Files
2 files

What it does

Once installed, the agent knows how to use UMAP, a technique that squeezes high-dimensional data (many columns per row) into a few dimensions so it can be plotted or fed into clustering. It picks sensible settings such as neighbourhood size, minimum distance and metric for the kind of data you have, writes the Python code, and reminds you of cautions: check results against the original data, keep test data out of preprocessing, and do not read exact distances or cluster sizes off the picture as if they were measurements.

Great for

  • Data scientists who want to visualise high-dimensional datasets such as gene expression or survey features
  • Developers building clustering or feature-engineering pipelines in Python
  • Students learning how UMAP differs from PCA and t-SNE

Skip it if

  • Tasks needing calibrated or physically meaningful distances between clusters, since UMAP layout distances are not quantitatively reliable
  • Predictive work where test data might leak into scaling or fitting if the user is careless; the skill advises against this but a human must still verify the pipeline

Try asking

  • “Make a 2D UMAP plot of my customer feature table, coloured by segment”
  • “Reduce this 500-column dataset to 10 dimensions before running k-means”
  • “Compare UMAP with cosine metric against Euclidean for my document vectors”
  • “Set up a supervised UMAP using my class labels and explain the parameters”
Step-by-step guide: how to use UMAP Dimensionality ReductionInstall, first run, examples, tips and FAQs

Install in 3 steps

  1. 1

    Download the skill

    The ZIP contains the 2 original files, the MIT licence and an attribution note.

    umap-learn.zip
  2. 2

    Add it to your agent

    Claude Code (all projects)

    mkdir -p ~/.claude/skills && unzip umap-learn.zip -d ~/.claude/skills/

    Claude Code / Agent SDK (this project only)

    mkdir -p .claude/skills && unzip umap-learn.zip -d .claude/skills/

    Claude apps (web/desktop): Settings → Capabilities → Skills → Upload skill, and choose the ZIP.

  3. 3

    Ask for the task

    No special command needed — the agent reads the skill's description and loads it when your request matches. Try one of the prompts above.

Get better results

  • Give the agent a description of your data (rows, columns, units, whether features should be scaled) so it picks the right metric and preprocessing
  • Ask the agent to test sensitivity by re-running with a few seeds and neighbour counts before trusting a pattern
  • Review any code the agent runs, since the skill includes instructions that lead to executing Python and installing packages

What's inside

  • references/api_reference.md
  • SKILL.md

Redistributed unmodified from K-Dense-AI/scientific-agent-skills under the MIT licence. The assessment was drafted with AI from the skill's files on 2026-10-29. Skills can instruct your agent to run code — read SKILL.md and any scripts before installing.