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Tooluniverse Target Research

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

This skill uses ToolUniverse to rapidly gather targeted research data across multiple sources, saving time and boosting investigative efficiency.

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-target-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-target-research/SKILL.md
Scanned
18 files, 9,225 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

The Tooluniverse Target Research skill enables AI agents to rapidly gather comprehensive research data about specific gene targets. It explores nine parallel research paths using ToolUniverse tools, supporting identification by gene symbol, UniProt accession, Ensembl ID, or gene name. The skill emphasizes thoroughness, requiring evidence grading, citation attribution, and documentation of negative results for a complete target intelligence profile.

When to use it

  • When users ask about a drug target, protein, or gene.
  • For target validation or assessment purposes.
  • To request comprehensive target profiling, including target-disease associations and safety profiles.
  • When a user asks "what do we know about [target]?"

Key capabilities

  • Supports identification of targets using gene symbol, UniProt accession, Ensembl ID, or gene name.
  • Explores nine parallel research paths for comprehensive data gathering.
  • Requires evidence grading (T1-T4) and inline source attribution for all facts.
  • Mandates documentation of negative results ("No data" notes).
  • Translates user queries to English before tool calls, using original language as a fallback.

Example prompts

  • “What do we know about the EGFR gene?”
  • “Can you assess the druggability of protein ABC123?”
  • “Give me a comprehensive profile for target UniProt accession P53456.”
  • "What are the known disease associations for Ensembl ID ENSG00000141510?"

Tips & gotchas

  • Always verify gene names, functions, and disease associations using databases like UniProt or Ensembl before reasoning.
  • This skill is not suitable for simple protein lookups, drug-only queries, or disease-centric requests; use specialized skills instead.
  • The skill prioritizes English language searches – be aware that translations may occur.