Tooluniverse Multiomic Disease Characterization

Name: Tooluniverse Multiomic Disease Characterization
Author: mims-harvard

Checked

mims-harvard·vlatest·other·Apache-2.0

This skill analyzes complex biological data (multiomics) to characterize diseases, providing deeper insights for research and potential therapeutic targets.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

Run this in your project

terminal

$ npx skills add mims-harvard/tooluniverse --skill tooluniverse-multiomic-disease-characterization -a claude-code

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

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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 28 Sept 2026

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-multiomic-disease-characterization/SKILL.md
Scanned: 19 files, 4,444 lines
Copies in repo: 3 — all of them scanned

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About this skill

What it does

This skill enables AI agents to perform comprehensive disease characterization by integrating and analyzing multiple layers of biological data. It specifically processes genomic, transcriptomic, proteomic, and metabolomic datasets to reveal complex disease mechanisms and biomarkers.

When to use it

Investigating the molecular drivers of rare genetic disorders where single-omics data is insufficient.
Identifying potential therapeutic targets by correlating protein expression changes with metabolic shifts in cancer cells.
Validating drug efficacy hypotheses by simulating multi-omic responses across different patient subtypes.
Constructing unified disease signatures that combine DNA variants, RNA splicing events, and metabolite concentrations.

Key capabilities

Ingests and harmonizes heterogeneous data from genomics, transcriptomics, proteomics, and metabolomics sources.
Performs cross-layer correlation analysis to link genetic mutations to downstream functional outcomes.
Generates integrated disease profiles that highlight multi-omic biomarkers for precision medicine applications.
Supports scalable processing of large-scale cohort studies with diverse molecular annotations.

Example prompts

"Analyze the provided RNA-seq and metabolomics data from pancreatic cancer samples to identify key metabolic reprogramming events driven by KRAS mutations."
"Integrate genomic variants and proteomic profiles from a cohort of Alzheimer's patients to discover novel protein biomarkers associated with early-stage cognitive decline."
"Characterize the multi-omic landscape of cystic fibrosis lung tissue, focusing on how specific SNPs influence inflammatory cytokine production and lipid metabolism."

Tips & gotchas

Ensure input datasets are preprocessed for quality control, as noise in any single omics layer can skew cross-layer correlations. This skill requires substantial computational resources; verify your environment supports the necessary memory and processing power before running large-scale characterizations.