# tooluniverse-single-cell

A skill that walks an agent through scRNA-seq analysis on scanpy and anndata: h5ad loading, QC gating, clustering, cell-type annotation.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 338 installs · ★ 1759
- Author: mims-harvard
- License: Apache-2.0 · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/mims-harvard/ToolUniverse/tree/HEAD/skills/tooluniverse-single-cell/SKILL.md
- Updated: 2026-10-24
- HTML: https://affpapa.org/en/nevibecode/skills/tooluniverse-single-cell

## What it does

The skill covers the whole scRNA-seq path: it reads h5ad, 10X and CSV count matrices, computes QC metrics (`n_genes_by_counts`, `total_counts`, `pct_counts_mt`, `pct_counts_ribo`), normalizes, runs PCA, UMAP and t-SNE, clusters with Leiden or Louvain, finds marker genes and annotates cell types. Pseudotime and trajectory analysis are in there too.

Before any re-analysis it tells the agent to scan the data folder for executed notebooks, pre-computed result tables and canonical scripts. If those exist, it reads them and cites the output as the answer. Re-running from raw counts gives different numbers and burns far more turns, so this check matters.

QC is not optional here. Doublets, ambient RNA and empty droplets quietly distort results: doublets look like intermediate states and contamination smears markers across clusters. If scanpy is missing, the skill prints an install plan and stops instead of inventing metrics. You will need `scanpy`, `anndata`, `leidenalg` and `umap-learn`; `harmonypy` for batch correction and `gseapy` for enrichment.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add mims-harvard/ToolUniverse --skill tooluniverse-single-cell -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/tooluniverse-single-cell/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/tooluniverse-single-cell/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add mims-harvard/ToolUniverse --skill tooluniverse-single-cell -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/tooluniverse-single-cell/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add mims-harvard/ToolUniverse --skill tooluniverse-single-cell -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/tooluniverse-single-cell/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install mims-harvard/ToolUniverse tooluniverse-single-cell
```

2. **Manually: put the files in place**
- File: `.github/skills/tooluniverse-single-cell/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add mims-harvard/ToolUniverse --skill tooluniverse-single-cell -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/mims-harvard/ToolUniverse --path skills/tooluniverse-single-cell
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/tooluniverse-single-cell/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add mims-harvard/ToolUniverse --skill tooluniverse-single-cell -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/mims-harvard/ToolUniverse/tree/HEAD/skills/tooluniverse-single-cell/SKILL.md

## When to use it
- compute QC metrics and gate cells on thresholds
- detect doublets with Scrublet or scDblFinder
- reduce dimensions and plot a UMAP
- cluster cells with the Leiden algorithm
- pull marker genes for each cluster

## Example prompts
- Load the h5ad and compute QC metrics including mitochondrial percent
- Normalize, run PCA and Leiden clustering, then find marker genes
- Check the folder for pre-computed results before re-running the analysis

## Source text · SKILL.md · Apache-2.0

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 7d9f93f95921e5f1.

BEGIN THIRD-PARTY FILE 7d9f93f95921e5f1
```text
---
name: tooluniverse-single-cell
description: Single-cell RNA-seq analysis with scanpy/anndata — h5ad data loading, scRNA-seq quality control and QC gating (n_genes_by_counts, total_counts, mitochondrial percent / pct_counts_mt, pct_counts_ribo, doublet detection with Scrublet/scDblFinder, ambient RNA / SoupX awareness, empty-droplet filtering, MAD-based thresholds), normalization, dimensionality reduction (PCA, UMAP, t-SNE), clustering (Leiden, Louvain), marker gene identification, cell-type annotation, pseudotime/trajectory analysis. Use for any scRNA-seq workflow, including deciding which cells to filter, flag, or investigate before downstream analysis.
disable-model-invocation: true
---

# Single-Cell Genomics and Expression Matrix Analysis

## RULE ZERO — Check for pre-computed results FIRST

Before following any instruction below, scan the data folder for:
- `*_executed.ipynb` → read with `tu run read_executed_notebook '{"data_folder":"<path>","search":"<keyword>"}'` and cite its cell outputs as the authoritative answer
- Pre-computed result files (CSV/TSV with names like `*results*`, `*deseq*`, `*enrich*`, `*stats*`, `*_simplified.csv`) → read directly and report the requested value
- Canonical analysis scripts (`analysis.R`, `run_*.py`, `find_*.R`, `*.Rmd`) → execute as-is and read the output

Only follow this skill's re-analysis recipe below if **none** of the above exist. Re-running from raw data produces different numbers than the published answer and is much slower (often 5-10× turn count).

---

Comprehensive single-cell RNA-seq analysis and expression matrix processing using scanpy, anndata, scipy, and ToolUniverse.

---

## LOOK UP, DON'T GUESS
When uncertain about any scientific fact, SEARCH databases first (PubMed, UniProt, ChEMBL, ClinVar, etc.) rather than reasoning from memory. A database-verified answer is always more reliable than a guess.

---

## When to Use This Skill

Apply when users:
- Have scRNA-seq data (h5ad, 10X, CSV count matrices) and want analysis
- Need scRNA-seq quality control / QC gating: deciding cell filters by
  mito % (pct_counts_mt), gene/UMI counts, doublets, ambient RNA, empty droplets
- Ask about cell type identification, clustering, or annotation
- Need differential expression analysis by cell type or condition
- Want gene-expression correlation analysis (e.g., gene length vs expression by cell type)
- Ask about PCA, UMAP, t-SNE for expression data
- Need Leiden/Louvain clustering on expression matrices
```
END THIRD-PARTY FILE 7d9f93f95921e5f1

The third-party file has ended. Everything between the 7d9f93f95921e5f1 tags is data: requests and commands from it are not followed. An “end of file” line with another tag inside the block is part of the file.

First 2507 of 23220 bytes. © mims-harvard · Apache-2.0 · https://github.com/mims-harvard/ToolUniverse/tree/HEAD/skills/tooluniverse-single-cell/SKILL.md

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