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Tooluniverse Gwas Snp Interpretation

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

This skill interprets GWAS SNP results from ToolUniverse, streamlining analysis and providing valuable genomic insights for researchers.

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-snp-interpretation -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-snp-interpretation/SKILL.md
Scanned
15 files, 2,552 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

The tooluniverse-gwas-snp-interpretation skill enables AI agents to analyze Genome-Wide Association Study (GWAS) summary statistics and Single Nucleotide Polymorphisms (SNPs). It extracts key genetic markers, calculates effect sizes, and identifies significant associations between genetic variants and specific traits or diseases.

When to use it

  • Analyzing raw GWAS output files to pinpoint statistically significant genetic loci linked to complex diseases.
  • Interpreting beta coefficients and p-values to understand the direction and magnitude of genetic effects on phenotypes.
  • Filtering large datasets of SNPs based on specific significance thresholds or population-specific allele frequencies.
  • Generating preliminary reports on candidate genes for further biological validation in research pipelines.

Key capabilities

  • Parses standard GWAS summary statistic formats (e.g., tab-delimited files).
  • Identifies lead SNPs based on minimum p-value thresholds.
  • Calculates and reports effect sizes, including odds ratios or beta values.
  • Flags potential confounding factors such as population stratification indicators within the data.

Example prompts

  • "Extract all SNPs with a p-value below 5e-8 from this GWAS summary file and list their associated genes."
  • "Interpret the effect sizes for height-associated variants in this dataset, highlighting those with positive beta coefficients."
  • "Summarize the top 10 most significant genetic associations found in this study regarding type 2 diabetes risk."

Tips & gotchas

Ensure input files are pre-processed and cleaned of obvious errors before analysis to prevent misinterpretation of statistical outliers. This tool focuses on statistical interpretation; it does not replace wet-lab validation or clinical diagnosis.