How does this skill handle metabolite nomenclature?

It uses the RefMet database to map common names to standardized nomenclature and systematic classifications across four structural resolution levels.

What is the Metabolomics Workbench Skill?

It is a specialized Claude Code Skill that enables programmatic access to the NIH Metabolomics Workbench REST API, allowing users to query chemical and experimental data directly within their workflow.

Can I search for specific mass spectrometry data?

Yes, you can search by mass-to-charge ratio (m/z) while specifying ion adduct types like M+H or M-H and setting custom mass tolerances.

What types of studies are available through this skill?

You can access over 4,200 studies covering diverse analytical platforms (GC-MS, LC-MS, NMR) and biological contexts including various species and disease states.

Metabolomics Workbench Database

Name: Metabolomics Workbench Database
Author: Microck

byMicrock

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Data Science & ML

Accesses the NIH Metabolomics Workbench API to query metabolites, experimental studies, and standardized chemical nomenclature.

About

The Metabolomics Workbench Database skill provides Claude with programmatic access to the NIH Common Fund-sponsored platform, the primary repository for metabolomics research. This skill allows researchers and bioinformaticians to search over 4,200 processed studies, perform mass spectrometry m/z searches, and standardize metabolite nomenclature using RefMet. It is essential for biomarker discovery, retrieving molecular structures, and exploring associations between genes, proteins, and metabolites across LC-MS, GC-MS, and NMR platforms.

Key Features

Filter studies by analytical platform, species, sample source, and disease state
Query detailed study metadata and complete experimental results
Standardize metabolite names and classifications using RefMet
Retrieve molecular structures and cross-reference PubChem, KEGG, and HMDB IDs
Perform m/z searches with custom ion adducts and mass tolerances
81 GitHub stars

Use Cases

Cross-referencing metabolite identifiers for pathway analysis
Gathering experimental datasets for meta-analysis or machine learning
Identifying candidate metabolites from mass spectrometry data

About

Key Features

Filter studies by analytical platform, species, sample source, and disease state
Query detailed study metadata and complete experimental results
Standardize metabolite names and classifications using RefMet
Retrieve molecular structures and cross-reference PubChem, KEGG, and HMDB IDs
Perform m/z searches with custom ion adducts and mass tolerances
81 GitHub stars

Use Cases

Cross-referencing metabolite identifiers for pathway analysis
Gathering experimental datasets for meta-analysis or machine learning
Identifying candidate metabolites from mass spectrometry data