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tooluniverse-metabolomics

Metabolomics research — metabolite identification, study analysis, and database searches across HMDB, MetaboLights, Metabolomics Workbench, KEGG. Use for annotating mass-spec features to known metabolites, finding metabolomics studies of a disease, and structured metabolomics research reports with metabolite-pathway mapping.

64

Quality

75%

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SecuritybySnyk

Low

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tessl review fix ./plugin/skills/tooluniverse-metabolomics/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

50%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body delivers genuinely useful, specific operational guidance — exact SOAP parameters, numeric mass-spec thresholds, an L1–L4 confidence scheme, and a clear 4-phase pipeline with fallbacks — but it is padded with a redundant Summary and marketing bullets, contains no copy-paste executable examples, lacks explicit validation checkpoints, and references bundle files that don't exist while leaving the bundled metabolism_ref.py unlinked.

Suggestions

Fix the bundle references: create the cited QUICK_START.md and .env.template (or remove the citations), and add a link to the existing scripts/metabolism_ref.py so the implementation is discoverable; consider moving the detailed Reasoning Framework and Tool Discovery into separate reference files to slim the ~290-line SKILL.md.

Delete the redundant '## Summary' section and its marketing-style 'Key Features'/'Best for' bullets ('✅ 100% test coverage'), which restate earlier phases without adding actionable instruction, and trim the '## Use Case' example queries that duplicate the frontmatter description triggers.

Add at least one concrete copy-paste tool-call example (e.g., a real HMDB_search(operation="search", ...) invocation with response handling) and an explicit validation checkpoint in the pipeline (e.g., 'Verify each returned HMDB ID is non-empty before Phase 2; if empty, route to PubChem fallback and retry') to turn graceful degradation into a true validate→retry feedback loop.

DimensionReasoningScore

Conciseness

The core operational guidance is lean and specific, but the body is padded with a redundant '## Summary' that restates the four phases and marketing-style 'Key Features'/'Best for' bullets ('✅ 100% test coverage'), plus a '## Use Case' whose example queries duplicate the frontmatter description — verbosity that could be cut without losing clarity.

2 / 3

Actionability

Concrete tool-level detail is present — exact SOAP parameters ('Requires operation="search"', 'Do not use endpoint or method'), numeric thresholds ('[M+H]+ = -1.0073', '±5 ppm', 'fold-change > 1.5'), and named tool signatures — but there is no copy-paste executable call example or complete request/response syntax, and the bundled implementation script (metabolism_ref.py) is never referenced, so the executable form is effectively missing.

2 / 3

Workflow Clarity

The 4-phase pipeline and per-phase numbered steps are clearly sequenced and a fallback hierarchy plus 'Common Issues' troubleshooting is documented, but there are no explicit validation checkpoints or validate→retry feedback loops — error handling is reactive ('Show error message with context, continue with remaining phases') rather than in-pipeline verification.

2 / 3

Progressive Disclosure

Internal sectioning is reasonable (not a wall of text), but cross-file structure is mismanaged: the body cites QUICK_START.md and .env.template which do not exist in the bundle, while the present scripts/metabolism_ref.py is orphaned and never linked, and the ~290-line monolith keeps content (Reasoning Framework, Tool Discovery) inline that could live in separate reference files.

2 / 3

Total

8

/

12

Passed

Description

100%

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description crisply states what the skill does (metabolite identification, study analysis, database searches across named sources) and provides an explicit 'Use for ...' trigger clause covering mass-spec annotation, disease-study lookup, and structured reports, giving it a clear, low-conflict niche with strong natural trigger terms.

DimensionReasoningScore

Specificity

Multiple concrete actions are named — 'metabolite identification, study analysis, and database searches', 'annotating mass-spec features to known metabolites', 'finding metabolomics studies of a disease', and 'structured metabolomics research reports with metabolite-pathway mapping' — matching the 'lists multiple specific concrete actions' anchor; it is not the level below because the actions are specific and enumerated rather than a single vague domain mention.

3 / 3

Completeness

It answers both 'what' ('metabolite identification, study analysis, and database searches across HMDB, MetaboLights, Metabolomics Workbench, KEGG') and 'when' with an explicit 'Use for ...' trigger clause covering mass-spec annotation, disease-study lookup, and structured reports, matching the anchor that requires both with explicit triggers.

3 / 3

Trigger Term Quality

It covers natural terms a metabolomics user would say — 'metabolomics', 'metabolite identification', 'mass-spec features', 'metabolomics studies of a disease', plus concrete source names (HMDB, MetaboLights, Metabolomics Workbench, KEGG) — giving good coverage of the domain's natural vocabulary rather than just one generic keyword.

3 / 3

Distinctiveness Conflict Risk

The niche is sharply scoped to metabolomics with named databases (HMDB, MetaboLights, Metabolomics Workbench, KEGG) and specialized triggers ('annotating mass-spec features', 'metabolomics studies of a disease'), making it unlikely to fire for unrelated skills; it is well above the 'somewhat specific but could overlap' anchor.

3 / 3

Total

12

/

12

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
mims-harvard/ToolUniverse
Reviewed

Table of Contents

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