Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add AndyZhuang/Opentest --skill metabolomics-workbench-databasegit clone --depth 1 https://github.com/AndyZhuang/OpentestWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/andyzhuang/opentest/metabolomics-workbench-database)<a href="https://agentmods.dev/skills/andyzhuang/opentest/metabolomics-workbench-database"><img src="https://agentmods.dev/badge/skills/andyzhuang/opentest/metabolomics-workbench-database/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/andyzhuang/opentest/metabolomics-workbench-database"><img src="https://agentmods.dev/badge/skills/andyzhuang/opentest/metabolomics-workbench-database.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00053 | $0.02503 |
| Opus 5 | $0.00026 | $0.01252 |
| Sonnet 5 | $0.00011 | $0.00501 |
| Haiku 4.5 | $0.00005 | $0.00250 |
Grade A, and why
metabolomics-workbench-database scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
response = requests.get('https://www.metabolomicsworkbench.org/rest/compound/pubchem_cid/5281365/all/json') This is a copy
84% identical to metabolomics-workbench-database — 6 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 259 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Metabolomics Workbench Database
Overview
The Metabolomics Workbench is a comprehensive NIH Common Fund-sponsored platform hosted at UCSD that serves as the primary repository for metabolomics research data. It provides programmatic access to over 4,200 processed studies (3,790+ publicly available), standardized metabolite nomenclature through RefMet, and powerful search capabilities across multiple analytical platforms (GC-MS, LC-MS, NMR).
When to Use This Skill
This skill should be used when querying metabolite structures, accessing study data, standardizing nomenclature, performing mass spectrometry searches, or retrieving gene/protein-metabolite associations through the Metabolomics Workbench REST API.
Core Capabilities
1. Querying Metabolite Structures and Data
Access comprehensive metabolite information including structures, identifiers, and cross-references to external databases.
Key operations:
- Retrieve compound data by various identifiers (PubChem CID, InChI Key, KEGG ID, HMDB ID, etc.)
- Download molecular structures as MOL files or PNG images
- Access standardized compound classifications
- Cross-reference between different metabolite databases
Example queries:
import requests
# Get compound information by PubChem CID
response = requests.get('https://www.metabolomicsworkbench.org/rest/compound/pubchem_cid/5281365/all/json')
# Download molecular structure as PNG
response = requests.get('https://www.metabolomicsworkbench.org/rest/compound/regno/11/png')
# Get compound name by registry number
response = requests.get('https://www.metabolomicsworkbench.org/rest/compound/regno/11/name/json')
2. Accessing Study Metadata and Experimental Results
Query metabolomics studies by various criteria and retrieve complete experimental datasets.
Key operations:
- Search studies by metabolite, institute, investigator, or title
- Access study summaries, experimental factors, and analysis details
- Retrieve complete experimental data in various formats
- Download mwTab format files for complete study information
- Query untargeted metabolomics data
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 259 lines · 53 tokens per session scan A 084a5d9c7f2c
metabolomics-workbench-database is a skill published in the GitHub repository AndyZhuang/Opentest (22 stars, last pushed 6mo ago), licensed MIT. It adds 53 tokens to every session and 2,503 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 84% identical to metabolomics-workbench-database, differing in 6 lines, and is treated as a copy.
Other skills, from other repositories
lamindb
Use when working with LaminDB, the open-source lineage-native lakehouse for biological datasets and models. Covers setup, artifact registration, query/search, lineage tracking, validation, ontology-backed annotation with Bionty, collections, branches, storage, and workflow integrations.
tiledbvcf
Efficient storage and retrieval of genomic variant data using TileDB. Scalable VCF/BCF ingestion, incremental sample addition, compressed storage, parallel queries, and export capabilities for population genomics.
defining-cohort-phenotypes
Authors computable phenotype and cohort definitions in the OHDSI ATLAS / CIRCE style over the OMOP CDM, combining standard concept sets with NLP-derived features that OpenMed extracts. Use when the user wants to define a patient cohort, write a computable phenotype, reuse PheKB or OHDSI Phenotype Library logic, build…
benchling-integration
Benchling R&D platform integration. Access registry (DNA, proteins), inventory, ELN entries, workflows via API, build Benchling Apps, query Data Warehouse, for lab data management automation.
chembl-database
Query the ChEMBL database for bioactive molecules, drug targets, bioactivity data, approved drugs, and chemical structures. Use when the user asks about compounds, targets, IC50/Ki values, drug mechanisms, or structure searches.
nvalchemi-data-storage
How to write, read, compose, and load atomic data using nvalchemi's composable Zarr-backed storage pipeline (Writer, Reader, Dataset, MultiDataset, DataLoader). Use when saving simulation outputs or trajectories to disk, converting structures (e.g. ASE / extxyz) into Zarr stores, assembling datasets for training or…