# tooluniverse-precision-medicine-stratification

Splits patients into responder groups, risk tiers, or treatment-decision groups. Useful when you have genomic and clinical data and need to work out who gets what.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 375 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-precision-medicine-stratification/SKILL.md
- Updated: 2026-10-24
- HTML: https://affpapa.org/en/nevibecode/skills/tooluniverse-precision-medicine-stratification

## What it does

The skill runs a nine-phase workflow, from resolving a disease name to an EFO ID through to integrated scoring and recommendations. Input is a disease plus at least one of: germline variants, somatic mutations, a gene list, or clinical biomarkers. Age, sex, stage and current medications sharpen the result but are optional.

Routing depends on disease type. Cancer, metabolic, cardiovascular, neurological, rare and autoimmune conditions diverge at phase three, so the skill tells you to classify the disease before running any tools. Every finding carries an evidence tier from T1 to T4, and the output lands on a risk score from 0 to 100.

Under the hood it queries ClinVar, OpenTargets, gnomAD, cBioPortal, PharmGKB and CPIC, then computes with Python via Bash — pandas, scipy, statsmodels, matplotlib. Reference files sit alongside: `TOOLS_REFERENCE.md`, `SCORING_REFERENCE.md`, `REPORT_TEMPLATE.md` and `EXAMPLES.md` with worked cases. Watch the parameter names: `MyGene_query_genes` takes `query`, `EnsemblVEP_annotate_rsid` takes `variant_id`, and `ensembl_lookup_gene` requires `species='homo_sapiens'`. gnomAD sometimes returns a service-overloaded error, so plan for a graceful skip rather than a failed run.

## Install

### Claude Code (native platform)

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

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

### Codex

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

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

### Cursor

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

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

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install mims-harvard/ToolUniverse tooluniverse-precision-medicine-stratification
```

2. **Manually: put the files in place**
- File: `.github/skills/tooluniverse-precision-medicine-stratification/SKILL.md`

### Gemini CLI

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

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

### Windsurf (Devin Desktop)

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

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add mims-harvard/ToolUniverse --skill tooluniverse-precision-medicine-stratification -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-precision-medicine-stratification/SKILL.md

## When to use it
- split a cohort into responders and non-responders
- build a therapeutic strategy report per patient subgroup
- grade germline variant pathogenicity with ClinVar and VEP
- pick a treatment using PharmGKB and CPIC pharmacogenomics
- check whether a patient matches a clinical trial

## Example prompts
- Stratify these NSCLC patients by EGFR status and suggest therapy options
- Split this cohort into risk tiers using germline variants and clinical features
- Report which patients on this list are likely to respond to checkpoint inhibitors

## 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: a4510ac688eed4aa.

BEGIN THIRD-PARTY FILE a4510ac688eed4aa
```text
---
name: tooluniverse-precision-medicine-stratification
description: Patient stratification for precision medicine — integrate genomic, clinical, and therapeutic data to split patients into responder/non-responder groups, risk tiers, or treatment-decision groups. Use for stratification-by-biomarker, treatment-selection logic, and personalized therapeutic strategy reports per patient subgroup.
disable-model-invocation: true
---

# Precision Medicine Patient Stratification

Transform patient genomic and clinical profiles into actionable risk stratification, treatment recommendations, and personalized therapeutic strategies.

## Reasoning Before Searching

Stratification means splitting patients into groups that respond differently to a treatment or have different prognoses. Ask these questions before running any tools:

1. **What molecular feature predicts response?** Candidates: somatic mutation (e.g., EGFR L858R), germline variant (e.g., BRCA1 LoF), expression level (e.g., HER2 overexpression), germline pharmacogenomic variant (e.g., CYP2C19 PM), or composite biomarker (e.g., TMB-H + MSI-H).
2. **Is the predictive feature actionable?** Knowing it must change treatment — either the drug choice, dose, or monitoring plan. A variant with prognostic value but no therapeutic consequence is not a stratification biomarker.
3. **What is the evidence level for the stratifier?** FDA-approved companion diagnostic (T1) vs. exploratory (T4) changes how much weight to place on the finding.

Route to the correct Phase 3 path BEFORE running Phase 2 tools — cancer, metabolic, CVD, rare disease, and autoimmune pipelines require different stratifiers.

**LOOK UP DON'T GUESS**: Never assume a variant is pathogenic, never assume a gene is relevant to a disease, never assign metabolizer status without PharmGKB or CPIC evidence.

**KEY PRINCIPLES**:
```
END THIRD-PARTY FILE a4510ac688eed4aa

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