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Tooluniverse Precision Medicine Stratification

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

This skill leverages ToolUniverse to precisely stratify patients for targeted therapies based on complex medical data, optimizing treatment plans and outcomes.

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-precision-medicine-stratification -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-precision-medicine-stratification/SKILL.md
Scanned
19 files, 4,252 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 enables AI agents to perform precision medicine stratification by analyzing patient data to identify specific subgroups for targeted therapeutic interventions. It processes complex clinical variables to recommend personalized treatment pathways based on individual genetic, molecular, or phenotypic profiles.

When to use it

  • Designing clinical trial eligibility criteria for rare diseases requiring specific biomarker matches.
  • Developing personalized oncology treatment plans by matching tumor mutations to approved therapies.
  • Segmenting patient populations in chronic disease management programs for tailored lifestyle interventions.
  • Analyzing electronic health records to predict response rates to novel pharmacological agents.

Key capabilities

  • Stratifies heterogeneous patient cohorts into homogeneous subgroups based on clinical and genomic data.
  • Identifies high-risk or treatment-responsive subsets within larger patient populations.
  • Generates evidence-based recommendations for precision therapeutic strategies.
  • Integrates multi-omics data sources to refine stratification accuracy.

Example prompts

  • "Stratify a cohort of 500 breast cancer patients into subgroups based on HER2 status and BRCA mutations."
  • "Identify the optimal treatment subgroup for patients with type 2 diabetes who have failed metformin therapy."
  • "Analyze this patient's genomic profile to recommend a precision medicine approach for rare genetic cardiomyopathy."

Tips & gotchas

Ensure input data includes validated clinical markers and genomic annotations, as stratification accuracy depends heavily on data quality. This tool is designed for research and decision support; final medical decisions must involve qualified healthcare professionals.