Metabolomics Workbench Database
ListedAccess and query Davila7's curated metabolomics workbench database for compound IDs, spectral data, and experimental contexts.
Install on your platform
Showing Claude Code. One command installs this skill into any of the agents below.
- 1
Run this in your project
terminal$ npx skills add davila7/claude-code-templates --skill metabolomics-workbench-database -a claude-code Uses the open-source
skillsCLI and needs Node.js. Add-gto install it for your user instead of this project.
What we know about this skill
Publisher
davila7
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Install target
Live repository
Not pinned. Installing fetches whatever the repository holds at the time you run the command.
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About this skill
The metabolomics-workbench-database skill provides AI agents with access to the Metabolomics Workbench, a public repository for metabolomics data. It enables agents to query, retrieve, and analyze large-scale datasets related to metabolic profiles from human studies.
When to use it
- Retrieving specific metabolite concentration data from published clinical trials.
- Analyzing correlations between metabolic markers and disease states using existing public records.
- Extracting experimental metadata regarding sample collection methods and processing protocols.
- Comparing results across multiple studies to identify consistent biological patterns.
Key capabilities
- Direct querying of the Metabolomics Workbench API.
- Retrieval of study-level metadata including design, population, and analytical methods.
- Access to raw metabolite intensity data for statistical analysis.
- Filtering datasets by specific conditions such as disease type or demographic variables.
Example prompts
- "Search the Metabolomics Workbench for studies involving Type 2 diabetes and extract peak intensities for lipids."
- "Retrieve metadata for all studies that used NMR spectroscopy to analyze plasma samples from elderly populations."
- "Compare metabolite profiles between control and treatment groups in studies published after 2020."
Tips & gotchas
Ensure your AI agent has valid API credentials or handles rate limiting gracefully, as public repositories often impose query restrictions. Always verify that the retrieved data aligns with the specific experimental protocols described in the study metadata before drawing conclusions.