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tooluniverse-metabolomics-pathway

Metabolomics pathway analysis — metabolite identification (HMDB, KEGG, ChEBI), pathway mapping (Reactome, KEGG, MetaCyc), disease associations, enzyme/gene linkage. Use for metabolite-to-pathway-to-disease connections, BridgeDb-based ID conversion, and integrating metabolomics with gene-level pathway analyses.

Quality

91%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

The canonical home for this skill is tooluniverse-metabolomics-pathway in mims-harvard/ToolUniverse

SKILL.md
Quality
Evals
Security

Quality

Content

85%Weight 40%Scale 1-5

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

A strong, token-efficient domain skill: concrete tool contracts with real example identifiers, a clearly phased workflow, and genuine error-recovery guidance. Its main structural weakness is that everything lives inline in SKILL.md with no progressive disclosure into reference files, which pushes it toward a monolithic API listing.

Suggestions

Move the per-tool parameter reference (Phases 0–5) into a references/tool-api.md file, keeping SKILL.md as a phased overview with clearly signaled links, so the heavy API detail is loaded on demand.

Consolidate the 'LOOK UP DON'T GUESS' bullets into the phase sections (or vice versa) and drop the opening line that duplicates the frontmatter description, trimming duplicated tokens.

Consider splitting the Common Mistakes and Fallback Strategies tables into a references file or merging fallback guidance into each phase so recovery guidance sits next to the step it recovers from.

DimensionReasoningScore

Conciseness

The body is dense reference material (tool signatures, example IDs, mistake/fallback tables) that assumes Claude's competence, with only minor trim candidates: the opening line duplicates the frontmatter description and the 'LOOK UP DON'T GUESS' bullets restate content already in the phase sections.

4 / 5

Actionability

Every tool is listed with required parameters, literal ID formats ("hsa:5230", "R-HSA-70171", "HMDB0000122"), and exact API constraints ("space-separated string, NOT array", "moverz/REFMET/{mz}/{adduct}/{tolerance}"), giving fully executable lookup guidance for this instruction-only skill.

5 / 5

Workflow Clarity

A clear Phase 0–5 sequence with an overview diagram, an identity-resolution checkpoint ('Always verify metabolite identity first'), and explicit error-recovery loops via Fallback Strategies and Common Mistakes; no destructive/batch operations so the validation cap does not apply.

5 / 5

Progressive Disclosure

The skill is well-sectioned with headers, but it is a single ~150-line file with no bundle files or references at all — the per-tool API reference and the mistake/fallback tables are inlined and could be split into one-level-deep reference files to keep SKILL.md an overview.

3 / 5

Total

17

/

20

Passed

Description

92%Weight 40%Scale 1-5

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

A strong description: it enumerates concrete capabilities in third person, names the relevant databases, and includes an explicit 'Use for' trigger clause. The only gap is modest coverage of natural synonym phrasings beyond the technical vocabulary.

DimensionReasoningScore

Specificity

The description lists multiple concrete, third-person actions — 'metabolite identification (HMDB, KEGG, ChEBI), pathway mapping (Reactome, KEGG, MetaCyc), disease associations, enzyme/gene linkage' — with comprehensive coverage of the domain's capabilities.

5 / 5

Completeness

Both questions are explicitly answered: the 'what' is the enumerated capability list, and the 'when' is the concrete trigger clause 'Use for metabolite-to-pathway-to-disease connections, BridgeDb-based ID conversion, and integrating metabolomics with gene-level pathway analyses' — explicit enough for anchor 5 rather than 4.

5 / 5

Trigger Term Quality

Good coverage of natural domain terms ('metabolomics', 'metabolite', 'pathway mapping', 'disease associations', 'enzyme/gene linkage'), but a few natural synonyms users might say — e.g. 'metabolic pathway', 'compound', 'ID mapping/conversion' as a plain phrase — are missing, and database jargon (ChEBI, BridgeDb) dominates.

4 / 5

Distinctiveness Conflict Risk

It occupies a clear metabolomics-pathway niche with distinct database-name triggers and is unlikely to fire for unrelated skills; the 'gene-level pathway analyses' mention slightly broadens overlap with generic genomics skills but not enough to drop from the 'clear niche' anchor.

5 / 5

Total

19

/

20

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.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository

mims-harvard/ToolUniverse

Path

plugins/tooluniverse/skills/tooluniverse-metabolomics-pathway/SKILL.md

Reviewed

Table of Contents

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