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Tooluniverse Metabolomics Analysis

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mims-harvard·vlatest·data·Apache-2.0

Analyzes metabolomic data using diverse tools to identify biomarkers and pathways related to disease or biological processes.

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-metabolomics-analysis -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

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Install target

Live repository

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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

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  • No credential access: passed

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  • Pinned to a commit: passed

    Scanned mims-harvard/tooluniverse at f5c37eebeb.

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Commit
f5c37eebeb
Manifest
skills/tooluniverse-metabolomics-analysis/SKILL.md
Scanned
9 files, 1,602 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 analyses of metabolomic data, encompassing metabolite identification and quantification, statistical analysis, pathway interpretation, and integration with other omics datasets. It utilizes various tools for tasks such as normalization (TIC or internal standard), handling missing values, and accounting for batch effects. The tool emphasizes retrieving information from established databases like HMDB, KEGG, CTD, and spectral libraries rather than relying on assumptions.

When to use it

  • You have metabolomics data acquired through LC-MS, GC-MS, or NMR techniques.
  • You need to identify differential metabolites between different conditions.
  • You want to understand which metabolic pathways are dysregulated.
  • You're looking for metabolite biomarkers related to disease classification.
  • You require integration of metabolomics data with other omics layers like transcriptomics.

Key capabilities

  • Data Import: Supports LC-MS, GC-MS, NMR, and both targeted/untargeted platforms.
  • Metabolite Identification: Matches metabolites to HMDB, KEGG, PubChem, and spectral libraries.
  • Quality Control: Includes peak quality assessment, blank subtraction, and internal standard normalization.
  • Normalization: Offers probabilistic quotient, total ion current (TIC), and internal standards normalization methods.
  • Statistical Analysis: Performs univariate and multivariate analyses like PCA, PLS-DA, and OPLS-DA.
  • Differential Analysis: Identifies significant changes in metabolite abundance.
  • Pathway Enrichment: Analyzes metabolic pathways using KEGG, Reactome, and BioCyc.
  • Metabolite-Enzyme Integration: Correlates metabolite levels with enzyme expression data.
  • Flux Analysis: Performs metabolic flux balance analysis (FBA).
  • Biomarker Discovery: Identifies multi-metabolite signatures for biomarker discovery.

Example prompts

  • "Analyze this LC-MS metabolomics data for differential metabolites."
  • "Which metabolic pathways are dysregulated between conditions?"
  • "Identify metabolite biomarkers for disease classification."

Tips & gotchas

  • Always confirm metabolite identities using Metabolite_search and Metabolite_get_info; do not assume identity based solely on m/z values.
  • Apply defined QC criteria (CV < 30%, blank ratio > 3x) – avoid overriding these with estimations.
  • Normalization methods (TIC vs. internal standards) are crucial for accurate quantification and depend on the experimental design.