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Tooluniverse Gwas Study Explorer

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

This skill explores GWAS studies for relevant genetic variants and their associations with diseases, accelerating research discovery.

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-gwas-study-explorer -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-gwas-study-explorer/SKILL.md
Scanned
7 files, 3,539 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.
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About this skill

The tooluniverse-gwas-study-explorer skill enables AI agents to discover and install specialized tools for exploring Genome-Wide Association Studies (GWAS). It facilitates the retrieval of genetic study metadata, allowing agents to analyze specific traits, populations, and statistical results within large-scale genomic datasets.

When to use it

  • Investigating genetic associations between specific biomarkers and complex diseases like diabetes or heart conditions.
  • Extracting summary statistics from published GWAS catalogs to identify significant loci for further research.
  • Comparing effect sizes and p-values across different cohorts to validate findings in meta-analyses.
  • Automating the initial data gathering phase for bioinformatics pipelines focused on polygenic risk scores.

Key capabilities

  • Accesses comprehensive databases of published genetic association studies.
  • Filters results by specific phenotypes, ethnic populations, or sample sizes.
  • Retrieves detailed metadata including lead SNPs, odds ratios, and confidence intervals.
  • Supports integration with downstream analysis tools for statistical modeling.

Example prompts

  • "Find all GWAS studies related to Type 2 Diabetes in East Asian populations with a p-value less than 5e-8."
  • "Extract the top 10 genetic variants associated with height from the latest meta-analysis available in the database."
  • "List the sample sizes and effect directions for schizophrenia risk factors identified in European cohorts."

Tips & gotchas

Ensure your AI agent has read-access permissions to the underlying genomic repositories, as some datasets may be restricted. Always verify that the retrieved summary statistics align with the specific ancestry group relevant to your research question to avoid population stratification biases.