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Tooluniverse Immunotherapy Response Prediction

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

Predicts immunotherapy patient response using a unique toolset, aiding clinicians in personalized treatment decisions and improved 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-immunotherapy-response-prediction -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-immunotherapy-response-prediction/SKILL.md
Scanned
19 files, 3,739 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

tooluniverse-immunotherapy-response-prediction

What it does

This skill leverages the ToolUniverse ecosystem to predict patient responses to immunotherapy treatments. It processes clinical data to forecast therapeutic outcomes, aiding oncologists in personalizing cancer care plans based on predictive modeling.

When to use it

  • Evaluating potential efficacy of checkpoint inhibitors for a specific tumor profile before treatment initiation.
  • Analyzing historical patient datasets to identify biomarkers associated with high or low response rates.
  • Supporting clinical decision-making by providing data-driven probability estimates for treatment success.
  • Researching correlations between genetic markers and immunotherapy performance in oncology studies.

Key capabilities

  • Integration with the ToolUniverse framework for advanced medical data processing.
  • Predictive modeling specifically tailored for immunotherapy response scenarios.
  • Support for analyzing complex clinical variables to generate outcome forecasts.
  • Deployment by mims-harvard within the TrustedSkills registry.

Example prompts

  • "Run a prediction model on this patient's genomic data to estimate their likelihood of responding to PD-1 inhibitors."
  • "Analyze the provided tumor microenvironment features and forecast the probability of durable remission after immunotherapy."
  • "Generate a risk assessment report comparing predicted outcomes for two different treatment regimens based on current clinical markers."

Tips & gotchas

  • Ensure input data includes standardized clinical metrics required by the underlying predictive algorithms.
  • Always validate AI-generated predictions with traditional clinical judgment and peer-reviewed guidelines before making medical decisions.