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Tooluniverse Clinical Trial Matching

Checked
mims-harvard·vlatest·other·Apache-2.0

This skill matches patients to relevant clinical trials using ToolUniverse's database, accelerating recruitment and improving patient access to innovative treatments.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add mims-harvard/tooluniverse --skill tooluniverse-clinical-trial-matching -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

mims-harvard

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned mims-harvard/tooluniverse at f5c37eebeb.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
f5c37eebeb
Manifest
skills/tooluniverse-clinical-trial-matching/SKILL.md
Scanned
25 files, 5,782 lines
Copies in repo
3 — all of them scanned
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

This skill matches patients to relevant clinical trials using ToolUniverse's database, accelerating recruitment and improving patient access to innovative treatments. It transforms a patient’s molecular profile and clinical characteristics into prioritized clinical trial recommendations by searching ClinicalTrials.gov and cross-referencing with databases like CIViC, OpenTargets, ChEMBL, and FDA. Each recommendation is evidence-graded and scored quantitatively, prioritizing matches based on specific biomarkers first, then disease stage and prior treatments.

When to use it

  • Finding clinical trials for a patient with NSCLC and an EGFR L858R mutation.
  • Identifying trials for a patient with BRAF V600E melanoma who has failed ipilimumab treatment.
  • Locating basket trials targeting NTRK fusions.
  • Discovering post-CDK4/6 inhibitor clinical trials for breast cancer patients with HER2 amplification.
  • Finding clinical trials for KRAS G12C colorectal cancer.

Key capabilities

  • Molecular Matching Priority: Prioritizes trials based on specific mutations, then disease stage and prior treatments.
  • Evidence-Graded Recommendations: Each trial match has an evidence tier (T1-T4).
  • Quantitative Scoring: Provides a Trial Match Score (0-100) for each trial.
  • Eligibility Evaluation: Parses and evaluates clinical trial inclusion/exclusion criteria.
  • Actionable Output: Includes clear next steps, contact information, and enrollment status.
  • Source Referencing: Every statement cites the tool or database source used.
  • Completeness Checklist: Provides a checklist showing analysis coverage.

Example prompts

  • "What clinical trials are available for my NSCLC with EGFR L858R?"
  • "Patient has BRAF V600E melanoma, failed ipilimumab - what trials?"
  • "Find basket trials for NTRK fusion."

Tips & gotchas

  • English-first queries: Always use English terms when requesting clinical trial matches. The skill will respond in the user’s language.
  • Prioritize databases: When uncertain about scientific facts, search relevant databases first rather than relying on reasoning.
  • Computation over description: The skill uses Python code (pandas, scipy, statsmodels, matplotlib) to perform calculations and data analysis; it will execute code and report results instead of describing the process.