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Tooluniverse Drug Research

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

This AI agent swiftly researches drugs by leveraging ToolUniverse, providing quick access to comprehensive pharmaceutical data and insights.

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-drug-research -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-drug-research/SKILL.md
Scanned
18 files, 6,177 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 conduct comprehensive drug research by leveraging over 50 tools within ToolUniverse. It provides access to data and insights across chemical databases, clinical trials, adverse events, pharmacogenomics, and scientific literature. The agent follows a structured workflow, prioritizing report generation with inline source attribution and evidence grading for all findings.

When to use it

  • To quickly gather detailed information about a specific drug or compound.
  • When needing to understand the mechanism of action of a pharmaceutical substance.
  • For research projects requiring comprehensive data on clinical trials or adverse events related to a drug.
  • To investigate pharmacogenomic aspects and potential interactions of medications.
  • When needing a structured report summarizing all available information about a drug, even if some data is unavailable.

Key capabilities

  • Access to over 50 ToolUniverse tools for pharmaceutical research.
  • Automated generation of a standardized drug report file ([DRUG]_drug_report.md) with predefined sections.
  • Compound disambiguation – resolving identifiers before initiating research.
  • Evidence grading (T1-T4) for all claims made in the report.
  • Traceable causal chain analysis of drug mechanisms, from target engagement to physiological outcome.
  • Mandatory citation and source attribution for every fact presented.

Example prompts

  • "Research the drug ibuprofen."
  • "What are the clinical trials associated with atorvastatin?"
  • "Can you create a report on the adverse events of metformin?"
  • “Investigate the mechanism of action of penicillin.”

Tips & gotchas

  • The agent prioritizes English-language drug names in tool queries; other languages will be used as fallbacks.
  • Do not expect to see the search process or individual tool outputs – the agent focuses on delivering a complete, structured report.
  • All sections of the generated report will exist, even if data is unavailable for certain categories.