# tooluniverse-clinical-trial-matching

This skill turns a patient's molecular profile and clinical state into a ranked list of clinical trials with evidence tiers and match scores.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 404 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-clinical-trial-matching/SKILL.md
- Updated: 2026-10-24
- HTML: https://affpapa.org/en/nevibecode/skills/tooluniverse-clinical-trial-matching

## What it does

The skill takes a disease name, molecular alterations (mutations, biomarkers, expression), stage and prior treatments. It then searches ClinicalTrials.gov, the EU CTIS register for European and EEA trials, and the ISRCTN registry for UK and international studies, cross-referencing CIViC, OpenTargets, ChEMBL and FDA label data.

The workflow runs in ten phases: standardising the patient profile against ontology IDs, broad trial discovery, characterising each trial, parsing inclusion and exclusion criteria, aligning drugs with biomarkers, assessing evidence, checking geography and feasibility, then looking at basket trials and expanded access. Every trial gets a Trial Match Score from 0 to 100 and an evidence tier from T1 to T4. Molecular matching outranks stage and prior treatment, so a patient with EGFR L858R lands on EGFR-targeted trials first.

It needs Python for the scoring and analysis steps, and access to the ToolUniverse databases. Tool calls are always issued in English while the answer comes back in the user's language. If you only need to interpret a single variant without trial context, that is a different job — this one stays on trial matching.

## Install

### Claude Code (native platform)

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

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

### Codex

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

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

### Cursor

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

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

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install mims-harvard/ToolUniverse tooluniverse-clinical-trial-matching
```

2. **Manually: put the files in place**
- File: `.github/skills/tooluniverse-clinical-trial-matching/SKILL.md`

### Gemini CLI

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

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

### Windsurf (Devin Desktop)

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

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add mims-harvard/ToolUniverse --skill tooluniverse-clinical-trial-matching -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-clinical-trial-matching/SKILL.md

## When to use it
- find trials for a specific mutation rather than just a cancer type
- check whether a patient passes inclusion and exclusion criteria
- compare US, European and UK trials in one ranked list
- see which trial drugs are already FDA-approved for a given biomarker
- look for options after progression on standard therapy

## Example prompts
- NSCLC with EGFR L858R, stage IV, progressed on osimertinib — what trials are available?
- Find basket trials for an NTRK fusion
- BRAF V600E melanoma, failed ipilimumab — what's in the EU and UK registries?

## 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: b04b2c5d6f86b644.

BEGIN THIRD-PARTY FILE b04b2c5d6f86b644
```text
---
name: tooluniverse-clinical-trial-matching
description: AI-driven patient-to-trial matching for precision oncology and rare-disease care. Transforms a patient's molecular profile (mutations, biomarkers, expression) and clinical state into ranked clinical-trial recommendations with evidence tiers. Searches ClinicalTrials.gov, the EU CTIS register (European/EEA trials), AND the ISRCTN registry (UK/international) plus cross-references CIViC, OpenTargets, ChEMBL, and FDA labels. Use for matching patients to trials by genotype, biomarker-driven trial selection, trial-eligibility scoring, and finding trials across the US, Europe, and the UK.
disable-model-invocation: true
---

# Clinical Trial Matching for Precision Medicine

Transform patient molecular profiles and clinical characteristics into prioritized clinical trial recommendations. Searches ClinicalTrials.gov and cross-references with molecular databases (CIViC, OpenTargets, ChEMBL, FDA) to produce evidence-graded, scored trial matches.

**KEY PRINCIPLES**:
1. **Report-first approach** - Create report file FIRST, then populate progressively
2. **Patient-centric** - Every recommendation considers the individual patient's profile
3. **Molecular-first matching** - Prioritize trials targeting patient's specific biomarkers

## Molecular Matching Priority

Match patients to trials by molecular profile FIRST (specific mutations), then by disease stage, then by prior treatments. A patient with EGFR L858R should match to EGFR-targeted trials regardless of other factors.
4. **Evidence-graded** - Every recommendation has an evidence tier (T1-T4)
5. **Quantitative scoring** - Trial Match Score (0-100) for every trial
6. **Eligibility-aware** - Parse and evaluate inclusion/exclusion criteria
7. **Actionable output** - Clear next steps, contact info, enrollment status
8. **Source-referenced** - Every statement cites the tool/database source
9. **Completeness checklist** - Mandatory section showing analysis coverage
10. **English-first queries** - Always use English terms in tool calls. Respond in user's language
```
END THIRD-PARTY FILE b04b2c5d6f86b644

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First 2090 of 12962 bytes. © mims-harvard · Apache-2.0 · https://github.com/mims-harvard/ToolUniverse/tree/HEAD/skills/tooluniverse-clinical-trial-matching/SKILL.md

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