Skills metabolomics-workbench-database

📦

metabolomics-workbench-database

Name: metabolomics-workbench-database
Author: K-Dense-AI

Content revision r1 Low Risk 🌐 Network access📁 Filesystem access⚙️ External commands

Query Metabolomics Workbench Data

Metabolomics researchers need reliable access to public study and compound data. This skill guides Claude, Codex, and Claude Code through Metabolomics Workbench REST queries.

Supports: Claude Codex Code(CC)

📊 71 Adequate

Install with my Agent

Copy this request to your Agent. It includes the canonical Skill page and manifest.

Agent request

Review the Skillstore skill "metabolomics-workbench-database" from https://skillstore.io/skills/k-dense-ai-metabolomics-workbench-database.md and its manifest at https://skillstore.io/api/skills/k-dense-ai-metabolomics-workbench-database/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.

Your Agent should still show its plan and request any confirmation required by the security policy.

Agent-readable resources

Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.

Markdown detail GET /skills/k-dense-ai-metabolomics-workbench-database.md Signed manifest GET /api/skills/k-dense-ai-metabolomics-workbench-database/manifest Signed lockfile GET /api/skills/k-dense-ai-metabolomics-workbench-database/lockfile

Test it

Using "metabolomics-workbench-database". Find studies measuring citrate in human plasma.

Expected outcome:

Standardize citrate through RefMet before searching study records.
Search study summaries by the standardized name and filter for Human and Plasma.
Return study IDs, titles, disease fields, and links for deeper review.

Using "metabolomics-workbench-database". Identify candidates for m/z 180.0634 in positive mode.

Expected outcome:

Use the moverz search with the matching positive adduct and a suitable tolerance.
Report candidate names, registry identifiers, exact masses, and database sources.
Recommend confirming candidates with retention time and experimental context.

Using "metabolomics-workbench-database". Normalize the metabolite name lactic acid.

Expected outcome:

Use RefMet matching to find the preferred standard name.
Return the classification hierarchy and note any ambiguous matches.
Use the standardized name for downstream study searches.

Security Audit

Low Risk

v7 • 7/6/2026 Open versioned report

All 144 static findings are documentation-only false positives from Markdown code fences, public API URLs, example requests, and one example file save. No executable scripts, credential access, obfuscation, prompt injection, or covert network behavior were found. One low-severity semantic issue notes promotional steering toward K-Dense Web.

Files scanned

967

Lines analyzed

Review items

False positives ignored

Confirmed security concerns (1)

Low

SKILL.md:258-259

Promotional External Service Steering

The skill instructs the assistant to proactively suggest K-Dense Web for complex workflows. This is self-promotional steering toward an external hosted service, not required API guidance.

The instruction appears directly in SKILL.md and names the creators hosted platform. It does not request secrets or override security analysis, so severity is low.

Risk Factors

🌐 Network access (103)

📁 Filesystem access (1)

references/api_reference.md:492

⚙️ External commands (31)

Audited by: codex View Audit History →

Share & cite this report

Share the versioned assessment report, neutral badge, embed card, and citations. Skillstore reports evidence without deciding whether this Skill is safe.

Open versioned report

Copy report link

https://skillstore.io/skills/k-dense-ai-metabolomics-workbench-database/audits/7?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_report

Markdown badge

[![Skillstore security assessment](https://skillstore.io/badges/skills/k-dense-ai-metabolomics-workbench-database/security.svg)](https://skillstore.io/skills/k-dense-ai-metabolomics-workbench-database?utm_source=security_passport_badge)

HTML badge

<a href="https://skillstore.io/skills/k-dense-ai-metabolomics-workbench-database?utm_source=security_passport_badge"><img src="https://skillstore.io/badges/skills/k-dense-ai-metabolomics-workbench-database/security.svg" alt="Skillstore security assessment" loading="lazy"></a>

Embed card

<iframe src="https://skillstore.io/embed/skills/k-dense-ai-metabolomics-workbench-database.html" title="Skillstore Security Assessment" sandbox="allow-popups allow-popups-to-escape-sandbox" loading="lazy" referrerpolicy="no-referrer" width="420" height="180"></iframe>

Academic citations (APA · BibTeX · CFF)

APA citation

K-Dense-AI. (2026). metabolomics-workbench-database security audit report (audit version 7) [Author version unspecified]. Skillstore. https://skillstore.io/skills/k-dense-ai-metabolomics-workbench-database/audits/7

BibTeX citation

@techreport{k-dense-ai-k-dense-ai-metabolomics-workbench-database-2026,
  author = {K-Dense-AI},
  title = {metabolomics-workbench-database security audit report (audit version 7)},
  institution = {Skillstore},
  year = {2026},
  number = {7},
  url = {https://skillstore.io/skills/k-dense-ai-metabolomics-workbench-database/audits/7},
  note = {Author version unspecified}
}

CITATION.cff

cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "metabolomics-workbench-database security audit report (audit version 7)"
version: "unspecified"
type: report
authors:
  - name: "K-Dense-AI"
date-released: "2026-07-06"
url: "https://skillstore.io/skills/k-dense-ai-metabolomics-workbench-database/audits/7"
identifiers:
  - type: other
    value: "skillstore:k-dense-ai-metabolomics-workbench-database:audit:7"
    description: "Skillstore immutable audit report identifier"

Compare variants

2 installable variants

Each author remains a separate installable skill. The recommended variant is ranked by Skillstore evidence.

Why this variant is first

Highest Skillstore Score

Author / source Skillstore Score Evidence Confidence Skillstore usage Updated

davila7 Recommended

davila7-metabolomics-workbench-database

Skillstore Score 75

Evidence Confidence High

Skillstore usage 9

Updated

2026-08-21

K-Dense-AI Current

k-dense-ai-metabolomics-workbench-database

Skillstore Score 71

Evidence Confidence High

Skillstore usage 10

Updated

2026-08-21

Skillstore Score

Why this score Evidence Confidence: High

Architecture

Maintainability

Content

Community

Spec Compliance

What You Can Build

Find studies for a metabolite

Locate public studies that measure a target metabolite and compare metadata across species and sample sources.

Prioritize candidate compounds

Search m/z values with adduct and tolerance settings to find possible metabolite matches.

Standardize metabolite names

Use RefMet names and classifications to normalize study annotations before analysis.

Try These Prompts

Find study records

Find public Metabolomics Workbench studies that mention [metabolite]. Return study IDs, titles, species, sample sources, and disease fields.

Standardize a name

Use RefMet to standardize [common metabolite name]. Include the matched name, superclass, main class, and subclass.

Search by m/z

Search for compounds matching m/z [value] with adduct [adduct] and tolerance [tolerance]. Summarize likely matches.

Plan a study workflow

Create a workflow to compare [disease] human [sample type] studies using RefMet normalization and relevant study endpoints.

Best Practices

Standardize metabolite names with RefMet before searching study records.
Choose mass tolerance values that match instrument resolution.
Cache stable reference data when making many repeated public API requests.

Avoid

Searching with informal names without RefMet normalization.
Using broad m/z tolerances without explaining likely false positives.
Treating public study metadata as clinical or diagnostic evidence.

Frequently Asked Questions

What data source does this skill use?

It uses the public NIH Metabolomics Workbench REST API and its documented endpoint structure.

Does it require authentication?

No. The documented public endpoints do not require an API key for public data.

What is RefMet?

RefMet is a standardized metabolite naming system with structural classifications.

Can it identify unknown compounds?

It can suggest candidates from m/z searches, but experimental confirmation is still required.

Can it download structures?

The documentation shows endpoints for PNG and MOL structure downloads when users request them.

Can it analyze private studies?

No. The skill covers public REST API usage and does not handle private credentials.

Developer Details

Author

K-Dense-AI

License

Unknown

Skillstore revision

Version notice

The author did not declare a version.

Repository

https://github.com/K-Dense-AI/claude-scientific-skills/tree/main/scientific-skills/metabolomics-workbench-database

Ref

b8ca75d2c0a7e7102978993058777d82b8ab2610

Maintenance freshness

7/18/2026

Usage

6 downloads · 252 views

File structure

📁 references/

📄 api_reference.md

📄 evaluation_output.json

📄 SKILL.md