Skip to content
TrustedSkillsbeta
Back to all skills

Tooluniverse Clinical Guidelines

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

Accesses and summarizes clinical guidelines from MIMS and Harvard resources to inform patient care decisions.

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-guidelines -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-guidelines/SKILL.md
Scanned
6 files, 1,674 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 allows AI agents to access and summarize clinical guidelines from MIMS and Harvard resources, specifically targeting patient care decisions. It prioritizes guideline sources based on their rigor and update frequency, favoring NICE and WHO guidelines first, followed by society guidelines (AHA, ADA, NCCN, SIGN), aggregator databases (GIN, TRIP, OpenAlex), and finally literature databases (PubMed, EuropePMC). The skill also enables the agent to perform computational analysis of retrieved data using Python.

When to use it

  • When needing evidence-based recommendations for patient care.
  • To quickly find guidelines related to a specific medical condition or treatment.
  • For research purposes, requiring access to and summarization of clinical guidance.
  • When needing to compare guidelines from multiple sources.
  • When computational analysis (statistics, data processing) is needed on retrieved guideline data.

Key capabilities

  • Guideline Source Prioritization: Ranks guidelines based on evidence grade and update frequency (NICE/WHO > Society > Aggregator > Literature).
  • Multi-Source Search: Queries NICE, TRIP, GIN, AHA, ADA, NCCN, CPIC, PubMed, and EuropePMC.
  • Search Strategy Refinement: Starts with narrow searches then broadens to disease categories if needed.
  • Publication Date Emphasis: Highlights the publication date of guidelines and notes potential newer updates.
  • Computational Analysis: Supports Python-based data analysis (pandas, scipy, statsmodels, matplotlib) for retrieved guideline information.

Example prompts

  • "Find clinical guidelines for managing type 2 diabetes."
  • "What are the NICE guidelines on treating hypertension?"
  • "Summarize the ADA's recommendations for gestational diabetes management and include the publication year."
  • "Retrieve guidelines related to heart failure, specifically HFpEF with SGLT2 inhibitors, from at least three sources."

Tips & gotchas

  • Publication Date is Crucial: Always check the guideline’s publication date as older guidelines may be superseded.
  • Source Verification: When using aggregator databases (GIN, TRIP, OpenAlex), verify the original source of the guideline.
  • Computational Analysis Requires Python Proficiency: The skill leverages Python for data analysis; familiarity with libraries like pandas and matplotlib is needed to utilize this capability fully.