Agent skill

Umap Tsne Analysis

by aipoch in aipoch/medical-research-skills

A skill your agent uses when performing sample-level dimensionality reduction and visualization on abundance or OTU-style matrices with a companion group file, generating UMAP and/or t-SNE…

MITAuto-check passedResearch & Science

Install Umap Tsne Analysis

skills CLI

$ npx skills add aipoch/medical-research-skills --skill umap-tsne-analysis -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install aipoch/medical-research-skills umap-tsne-analysis --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/aipoch/medical-research-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/'awesome-med-research-skills/Data Analysis/umap-tsne-analysis' .claude/skills/umap-tsne-analysis && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "umap-tsne-analysis" agent skill from https://github.com/aipoch/medical-research-skills/tree/main/awesome-med-research-skills/Data%20Analysis/umap-tsne-analysis into .claude/skills/umap-tsne-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "umap-tsne-analysis", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/aipoch/medical-research-skills/tree/main/awesome-med-research-skills/Data%20Analysis/umap-tsne-analysis

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add aipoch/medical-research-skills --skill umap-tsne-analysis -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install aipoch/medical-research-skills umap-tsne-analysis --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/aipoch/medical-research-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/'awesome-med-research-skills/Data Analysis/umap-tsne-analysis' .agents/skills/umap-tsne-analysis && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "umap-tsne-analysis" agent skill from https://github.com/aipoch/medical-research-skills/tree/main/awesome-med-research-skills/Data%20Analysis/umap-tsne-analysis into .agents/skills/umap-tsne-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "umap-tsne-analysis", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add aipoch/medical-research-skills --skill umap-tsne-analysis -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install aipoch/medical-research-skills umap-tsne-analysis --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/aipoch/medical-research-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/'awesome-med-research-skills/Data Analysis/umap-tsne-analysis' .cursor/skills/umap-tsne-analysis && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "umap-tsne-analysis" agent skill from https://github.com/aipoch/medical-research-skills/tree/main/awesome-med-research-skills/Data%20Analysis/umap-tsne-analysis into .cursor/skills/umap-tsne-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "umap-tsne-analysis", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/aipoch/medical-research-skills.git --path 'awesome-med-research-skills/Data Analysis/umap-tsne-analysis'

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add aipoch/medical-research-skills --skill umap-tsne-analysis -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install aipoch/medical-research-skills umap-tsne-analysis --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/aipoch/medical-research-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/'awesome-med-research-skills/Data Analysis/umap-tsne-analysis' .gemini/skills/umap-tsne-analysis && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "umap-tsne-analysis" agent skill from https://github.com/aipoch/medical-research-skills/tree/main/awesome-med-research-skills/Data%20Analysis/umap-tsne-analysis into .gemini/skills/umap-tsne-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "umap-tsne-analysis", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install aipoch/medical-research-skills umap-tsne-analysis

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add aipoch/medical-research-skills --skill umap-tsne-analysis -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/aipoch/medical-research-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/'awesome-med-research-skills/Data Analysis/umap-tsne-analysis' .github/skills/umap-tsne-analysis && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "umap-tsne-analysis" agent skill from https://github.com/aipoch/medical-research-skills/tree/main/awesome-med-research-skills/Data%20Analysis/umap-tsne-analysis into .github/skills/umap-tsne-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "umap-tsne-analysis", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add aipoch/medical-research-skills --skill umap-tsne-analysis -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install aipoch/medical-research-skills umap-tsne-analysis --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/aipoch/medical-research-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/'awesome-med-research-skills/Data Analysis/umap-tsne-analysis' .opencode/skills/umap-tsne-analysis && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "umap-tsne-analysis" agent skill from https://github.com/aipoch/medical-research-skills/tree/main/awesome-med-research-skills/Data%20Analysis/umap-tsne-analysis into .opencode/skills/umap-tsne-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "umap-tsne-analysis", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: umap-tsne-analysis
GitHub stars: 1.9k
Token cost: ~2.7k tokens
SKILL.md length: 1,072 words
Files: 18 (incl. scripts, references)
Skills in repo: 578
Repo updated: Sep 17, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

A skill your agent uses when performing sample-level dimensionality reduction and visualization on abundance or OTU-style matrices with a companion group file, generating UMAP and/or t-SNE…

Works in 4 steps: Validate Input → Prepare Matrix → Run Dimensionality Reduction → …
Performing sample-level dimensionality reduction and visualization on abundance
SKILL.md covers Prerequisites, When to Read External Files, Usage and Arguments, plus 10 more sections
Runs R scripts from its folder; reaches cloud.r-project.org

What it does

Umap Tsne Analysis is an agent skill from aipoch/medical-research-skills. Use when performing sample-level dimensionality reduction and visualization on abundance or OTU-style matrices with a companion group file, generating UMAP and/or t-SNE coordinates and plots for group separation assessment. NOT for: differential expression testing, single-cell workflows requiring dedicated embeddings pipelines, or analyses without a sample grouping file.

Its SKILL.md is about 2.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 21 other files, including scripts and reference files (for example `eval_report_umap-tsne-analysis_result.json`, `references/algorithm.md` and `references/cli-guide.md`).

It sits in Research & Science, covering Bioinformatics and Embeddings. It works with UMAP. The repository describes itself as: Hundreds of agent skills for medical research, including protocol design, data analysis, evidence insights, and academic writing. The licence is MIT.

When your agent uses it

Performing sample-level dimensionality reduction and visualization on abundance
OTU-style matrices with a companion group file
Generating UMAP and/or t-SNE coordinates and plots for group separation assessment

Example prompts

“/umap-tsne-analysis”

Workflow steps

4 steps, taken from the step headings in SKILL.md.

Validate Input
Prepare Matrix
Run Dimensionality Reduction
Generate Visualizations

What it can do on your machine

Read from SKILL.md and the folder at commit 686e09d. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 7 files in scripts/ (R, from the files we listed), which the agent can run.
From the folder's file list and the shell code blocks in SKILL.md.
Network
Hosts in commands or code, which the agent is likely to contact:
- cloud.r-project.org
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Umap Tsne Analysis loads about 2.7k tokens when it runs, and up to ~6.5k if it reads all its reference files. Until then it costs about 98 tokens; SKILL.md has 1,072 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~98
When it runs · the whole SKILL.md, loaded when a task matches: ~2.7k
With references · SKILL.md plus every file in references/, read only if the agent opens them: ~6.5k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from aipoch/medical-research-skills at commit 686e09d, republished under its MIT licence (© aipoch). 1,072 words, ~2,712 tokens.

Download SKILL.mdSave it as .claude/skills/umap-tsne-analysis/SKILL.md (or your agent's skills folder). This skill also uses 17 other files; get the full folder from GitHub.

name: umap-tsne-analysis
description: Use when performing sample-level dimensionality reduction and visualization on abundance or OTU-style matrices with a companion group file, generating UMAP and/or t-SNE coordinates and plots for group separation assessment. NOT for: differential expression testing, single-cell workflows requiring dedicated embeddings pipelines, or analyses without a sample grouping file.
license: MIT
skill-author: AIPOCH

UMAP and t-SNE Analysis

Prerequisites

Run the following before the first analysis to install all required R packages:

bash

Rscript scripts/install_dependencies.R

Alternative manual installation:

bash

Rscript -e "install.packages(c('optparse','data.table','Rtsne','umap','ggplot2','vegan','R.utils'), repos='https://cloud.r-project.org')"

Note: R.utils is only required when --timeout > 0, but pre-installing it avoids environment drift across runs. testthat is installed by scripts/install_dependencies.R as the development test dependency.

The skill cannot run until these packages are installed. In new or bare R environments, always run the prerequisite step first.

When to Read External Files

Situation	File to Read	Purpose
Need algorithm details	`references/algorithm.md`	Dimensionality reduction methods, assumptions, parameter interpretation
Need to run analysis	`scripts/main.R`	Execute: `Rscript scripts/main.R --input_file ... --group_file ...`
Encounter errors	`references/troubleshooting.md`	Common errors and solutions
Need CLI examples	`references/cli-guide.md`	Detailed CLI usage examples
Need test data	`tests/data/`	Sample input files for testing

Usage

bash

Rscript scripts/main.R \
  --input_file ./otu_table.csv \
  --group_file ./group_info.csv \
  --output_dir ./output/ \
  --method both \
  --seed 42

Arguments

Short	Long	Type	Default	Description
`-i`	`--input_file`	character	required	Abundance / OTU matrix file
`-g`	`--group_file`	character	required	Group information file
`-o`	`--output_dir`	character	`./output/`	Output directory
`-m`	`--method`	character	`both`	Method: `tsne`, `umap`, or `both`
	`--sample_id_col`	character	first column	Sample ID column in group file
	`--group_col`	character	second column	Group column in group file
	`--perplexity`	numeric	`25`	t-SNE perplexity
	`--theta`	numeric	`0.0`	t-SNE theta
	`--pca`	logical	`FALSE`	Whether to use PCA before t-SNE
	`--check_duplicates`	logical	`FALSE`	Whether t-SNE should check duplicated rows
	`--normalize`	logical	`TRUE`	Whether to normalize data before UMAP
	`--norm_method`	character	`hellinger`	Normalization method for `vegan::decostand()`
	`--n_neighbors`	integer	`10`	UMAP neighborhood size
`-s`	`--seed`	integer	`42`	Random seed for reproducibility
`-t`	`--timeout`	integer	`0`	Timeout in seconds; `0` disables timeout

Dependency Baseline

The skill was validated with the exact package baseline recorded in dependencies.lock.tsv.

Package	Tested Version
`optparse`	`1.7.5`
`data.table`	`1.15.4`
`Rtsne`	`0.17`
`umap`	`0.2.10.0`
`ggplot2`	`3.4.0`
`vegan`	`2.7.3`
`R.utils`	`2.13.0`
`testthat`	`3.1.2`

Use this file as the reproducibility baseline when validating a new environment.

Input Format

Abundance / OTU Matrix (`input_file`)

Features as rows, samples as columns, CSV/TSV-like tabular file with feature ID in the first column.

csv

OTU_ID,S1,S2,S3,S4
OTU_1,10,3,0,5
OTU_2,2,8,1,0
OTU_3,0,0,6,9

Group File (`group_file`)

Tabular file with at least two columns: sample ID and group label.

csv

SampleID,Group
S1,Control
S2,Control
S3,Treatment
S4,Treatment

Requirements:

At least 2 groups with at least 2 samples per group are required.
All sample IDs in the group file must exist in the matrix columns.
Single-group inputs will produce a SKILL_INVALID_PARAMETER error because dimensionality reduction without group contrast produces uninterpretable plots.

Output Files

File	Description
`table/tsne_coordinates.csv`	t-SNE coordinates with sample and group annotations
`table/umap_coordinates.csv`	UMAP coordinates with sample and group annotations
`plot/tsne_plot.pdf`	t-SNE scatter plot with group colors and ellipses
`plot/umap_plot.pdf`	UMAP scatter plot with group colors and ellipses
`data/session_info.txt`	R session and package version info
`data/analysis_data.rda`	Saved analysis object with aligned matrix, metadata, colors, and runtime parameters

Workflow

Step 1: Validate Input

Check that matrix file and group file exist
Resolve sample ID and group columns
Validate at least 2 groups and at least 2 samples per group
Ensure all group-file sample IDs exist in the matrix
Remove samples with zero total abundance after alignment if needed

Step 2: Prepare Matrix

Convert input table into numeric matrix
Align matrix columns to sample order from the group file
Transpose matrix so rows become samples and columns become features

Step 3: Run Dimensionality Reduction

Run t-SNE if --method tsne or --method both
Run UMAP if --method umap or --method both
Apply fixed random seed for reproducibility

Step 4: Generate Visualizations

Plot sample embeddings
Color points by group
Draw group ellipses when enabled
Save PDF outputs

Methods

t-SNE

t-SNE is a non-linear dimensionality reduction method that preserves local neighborhood structure. It is useful for identifying local sample clustering patterns.

UMAP

UMAP is a manifold learning method that aims to preserve both local and some global structure. It is often faster than t-SNE and can produce stable low-dimensional embeddings when parameters are chosen appropriately.

Normalization

When --normalize TRUE, the script uses vegan::decostand() with the selected --norm_method before UMAP. This is helpful for abundance-style ecological matrices.

Show full SKILL.md (440 more words)Show less

Agent Response Contract

After a successful run, report:

Method(s) run (tsne, umap, or both)
Sample count and group count processed
Key parameters used (perplexity for t-SNE, n_neighbors for UMAP)
Group separation quality (describe visible clustering from coordinate ranges if accessible)
Artifact paths: coordinate CSV(s) and plot PDF(s) produced

Examples

Basic Usage

bash

Rscript scripts/main.R \
  -i otu_table.csv \
  -g group_info.csv \
  -o ./output \
  -m both

Only t-SNE

bash

Rscript scripts/main.R \
  -i otu_table.csv \
  -g group_info.csv \
  -o ./output \
  -m tsne \
  --perplexity 10

Only UMAP with Custom Group Column

bash

Rscript scripts/main.R \
  -i otu_table.csv \
  -g metadata.csv \
  -o ./output \
  -m umap \
  --sample_id_col SampleID \
  --group_col Treatment \
  --n_neighbors 15

Error Handling

Common Errors

Error	Cause	Solution
`SKILL_FILE_NOT_FOUND`	Input file does not exist	Check file path
`SKILL_MISSING_COLUMNS`	Group or matrix file lacks required columns	Verify file format
`SKILL_SAMPLE_MISMATCH`	Sample IDs in group file do not match matrix columns	Check sample naming consistency
`SKILL_EMPTY_DATA`	Matrix becomes empty after preprocessing	Check input values and filtering
`SKILL_INVALID_PARAMETER`	Invalid method, invalid parameter value, or single-group input	Adjust CLI arguments; ensure at least 2 groups are present
`SKILL_PACKAGE_NOT_FOUND`	Required R package is missing	Run `Rscript scripts/install_dependencies.R`; note that file errors will only surface after packages are installed
`SKILL_TIMEOUT`	Analysis exceeded the configured timeout	Increase `--timeout` or set `--timeout 0`

IF error persists, READ: references/troubleshooting.md

Troubleshooting note: In environments where packages are not yet installed, SKILL_PACKAGE_NOT_FOUND will fire before file-validation errors. Install dependencies first, then re-run to expose any file-related errors.

Input Validation

This skill accepts:

An abundance or OTU-style feature matrix (CSV/TSV, features as rows, samples as columns)
A group file with at least two groups (CSV/TSV, sample IDs and group labels)

If the user's request does not involve UMAP or t-SNE dimensionality reduction for group separation visualization — for example, asking to run differential expression testing, process single-cell RNA-seq with specialized pipelines, perform clustering without a group file, or impute missing values — do not proceed with the workflow. Instead respond:

"UMAP and t-SNE Analysis is designed to perform sample-level dimensionality reduction and visualization on abundance or OTU-style matrices. Your request appears to be outside this scope. Please provide a feature matrix and group file for UMAP/t-SNE, or use a more appropriate tool for differential expression testing, single-cell analysis, or clustering."

Testing

Test with Sample Data

bash

Rscript scripts/install_dependencies.R

Rscript scripts/main.R --help

Rscript scripts/main.R \
  -i tests/data/otu_table.csv \
  -g tests/data/group_info.csv \
  -o tests/output/ \
  -m both

Rscript tests/test_skill.R

Rscript tests/run_smoke_test.R

Validation Commands

bash

ls -la tests/output/
ls -la tests/output/table
ls -la tests/output/plot
ls -la tests/output/data
wc -l tests/output/table/tsne_coordinates.csv
wc -l tests/output/table/umap_coordinates.csv

The canonical sample data live in tests/data/. Use those files for examples, smoke tests, and regression checks. The canonical output layout is output_dir/table, output_dir/plot, and output_dir/data.

Implementation Checklist

CLI parsing with optparse
set.seed() for reproducibility
requireNamespace() dependency checks
Dependency bootstrap script
Session info recording
File reading instructions in SKILL.md
Modular script structure
Error handling with SKILL_* codes
Test data provided in tests/data/
Version-pinned dependency baseline in dependencies.lock.tsv
Automated testthat coverage for validation and plotting edge cases
Scripts in scripts/ directory
References in references/ directory

Last updated: 2026-04-27 | Version: 1.1.0

Files

SKILL.md and 17 other files (scripts, references) in awesome-med-research-skills/Data Analysis/umap-tsne-analysis of aipoch/medical-research-skills.

SKILL.md
dependencies.lock.tsv
eval_report_umap-tsne-analysis_result.json
references/algorithm.md
references/cli-guide.md
references/troubleshooting.md
scripts/dim_reduction_methods.R
scripts/functions.R
scripts/install_dependencies.R
scripts/main.R
scripts/run_analysis.R
scripts/utils.R
scripts/visualization.R
tests/data/group_info.csv
tests/data/otu_table.csv
tests/run_smoke_test.R
tests/testthat
… and 1 more

Open the folder on GitHubat commit 686e09d

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Questions about Umap Tsne Analysis

What does Umap Tsne Analysis do?

A skill your agent uses when performing sample-level dimensionality reduction and visualization on abundance or OTU-style matrices with a companion group file, generating UMAP and/or t-SNE…. Umap Tsne Analysis is an agent skill from aipoch/medical-research-skills. Use when performing sample-level dimensionality reduction and visualization on abundance or OTU-style matrices with a companion group file, generating UMAP and/or t-SNE coordinates and plots for group separation assessment.

When should I use Umap Tsne Analysis?

Umap Tsne Analysis fits situations like: performing sample-level dimensionality reduction and visualization on abundance; OTU-style matrices with a companion group file; generating UMAP and/or t-SNE coordinates and plots for group separation assessment.

How do I install Umap Tsne Analysis in Claude Code?

Run `npx skills add aipoch/medical-research-skills --skill umap-tsne-analysis -a claude-code`. Or copy the skill folder (awesome-med-research-skills/Data Analysis/umap-tsne-analysis in aipoch/medical-research-skills) into .claude/skills/umap-tsne-analysis in your project. Claude Code loads it when a task matches its description.

How do I install Umap Tsne Analysis in Codex?

Run `npx skills add aipoch/medical-research-skills --skill umap-tsne-analysis -a codex`. Or copy the skill folder (awesome-med-research-skills/Data Analysis/umap-tsne-analysis in aipoch/medical-research-skills) into .agents/skills/umap-tsne-analysis in your project. Codex loads it when a task matches its description.

Can I use Umap Tsne Analysis in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add aipoch/medical-research-skills --skill umap-tsne-analysis -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/umap-tsne-analysis, .gemini/skills/umap-tsne-analysis, .github/skills/umap-tsne-analysis and .opencode/skills/umap-tsne-analysis in your project.

What does Umap Tsne Analysis need to run?

Going by SKILL.md and its folder, Umap Tsne Analysis needs R for the scripts in its folder.

Does Umap Tsne Analysis access the network?

SKILL.md names 1 domain. In commands or code: cloud.r-project.org; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Umap Tsne Analysis safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Umap Tsne Analysis use?

Umap Tsne Analysis is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Umap Tsne Analysis use?

About 2.7k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 3.8k tokens, read only when the agent opens those files.

What are the alternatives to Umap Tsne Analysis?

Skills that share tags, products or a category with Umap Tsne Analysis: Bio Data Visualization Dimensionality Reduction Plots (GPTomics/bioSkills, 1.2k stars), Sc Clustering (TianGzlab/OmicsClaw, 161 stars), Evo2 (JimLiu/science-skills, 228 stars) and Scgpt (JimLiu/science-skills, 228 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Umap Tsne Analysis?

aipoch (a GitHub organization) maintains it in aipoch/medical-research-skills, which has 1,937 GitHub stars. The repository holds 578 skills in this directory. The repository was last updated on September 17, 2026.

Source: aipoch/medical-research-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Umap Tsne Analysis

Facts

At a glance

What it does

When your agent uses it

Example prompts

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

UMAP and t-SNE Analysis

Prerequisites

When to Read External Files

Usage

Arguments

Dependency Baseline

Input Format

Abundance / OTU Matrix (input_file)

Group File (group_file)

Output Files

Workflow

Step 1: Validate Input

Step 2: Prepare Matrix

Step 3: Run Dimensionality Reduction

Step 4: Generate Visualizations

Methods

t-SNE

UMAP

Normalization

Agent Response Contract

Examples

Basic Usage

Only t-SNE

Only UMAP with Custom Group Column

Error Handling

Common Errors

Input Validation

Testing

Test with Sample Data

Validation Commands

Implementation Checklist

Files

Compare with similar skills

Similar skills

More from aipoch/medical-research-skills

Related

Questions about Umap Tsne Analysis

Abundance / OTU Matrix (`input_file`)

Group File (`group_file`)