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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.

72

Quality

87%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

82%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 tightly written, highly actionable reference body that excels at concise domain-specific guidance and concrete tool signatures. Main gaps are the absence of a runnable Python example despite the 'COMPUTE, DON'T DESCRIBE' directive and no external reference files to offload the inlined API catalog.

Suggestions

Add one short runnable Python snippet under 'COMPUTE, DON'T DESCRIBE' showing the retrieve-then-analyze pattern (e.g., ToolUniverse fetch into a pandas/scipy enrichment calc) to make the directive copy-paste executable.

Introduce an explicit validation checkpoint between phases (e.g., 'Confirm the metabolite ID resolved before moving to Phase 1') and fold Fallback Strategies into the phases as if-then recovery steps.

Move the full tool parameter reference and/or Evidence Grading table into a single references/ file linked from the body to reduce SKILL.md length and create one-level-deep progressive disclosure.

DimensionReasoningScore

Conciseness

Dense, lean body with no padding about what metabolomics or pathways are; every section (Domain Reasoning, Common Mistakes, Limitations) supplies non-obvious domain knowledge that earns its tokens, matching the 'lean and efficient' anchor.

5 / 5

Actionability

Concrete tool signatures with required/optional params, example IDs, and ID-format notes (e.g., BridgeDb source codes, 'hsa:5230') are mostly executable guidance, but the 'COMPUTE, DON'T DESCRIBE' section instructs running Python yet provides no copy-paste runnable snippet, leaving a minor gap.

4 / 5

Workflow Clarity

A clear six-phase pipeline with a diagram and per-phase tool lists, plus explicit chaining in Phase 3 and Fallback Strategies for recovery, but it lacks explicit per-phase validation checkpoints woven into the sequence ('verify X before proceeding'), capping it below 5.

4 / 5

Progressive Disclosure

Well-organized single file with clear section headers and navigable structure, but no bundle files exist and sizable reference material (full tool parameter reference, evidence-grading tables) is fully inlined rather than split into one-level-deep references.

4 / 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 highly specific, well-constructed description that names concrete capabilities with their backing databases and gives explicit 'Use for' trigger guidance. Only minor weakness is slightly incomplete synonym/variation coverage in trigger terms.

Suggestions

Add common user-facing synonyms to the trigger clause, e.g., 'metabolite enrichment' or 'pathway enrichment', so the description fires on those natural phrasings too.

Consider naming the ToolUniverse tool surface briefly (e.g., 'via ToolUniverse') so users referencing that platform land on this skill.

DimensionReasoningScore

Specificity

Lists multiple concrete actions with named databases — 'metabolite identification (HMDB, KEGG, ChEBI)', 'pathway mapping (Reactome, KEGG, MetaCyc)', 'disease associations', 'enzyme/gene linkage', 'BridgeDb-based ID conversion' — matching the comprehensive-coverage anchor.

5 / 5

Completeness

Explicitly answers both 'what' (identification, mapping, disease associations, enzyme/gene linkage) and 'when' ('Use for metabolite-to-pathway-to-disease connections, BridgeDb-based ID conversion, and integrating metabolomics with gene-level pathway analyses') with concrete triggers.

5 / 5

Trigger Term Quality

Domain terms a metabolomics user would naturally say are present ('metabolomics pathway analysis', 'metabolite-to-pathway-to-disease connections'), but a few natural phrasings and synonyms (e.g., 'pathway enrichment') are missing, so it sits just below the comprehensive anchor.

4 / 5

Distinctiveness Conflict Risk

Occupies a clear, specialized niche with distinct named databases (HMDB, KEGG, Reactome, BridgeDb, MetaCyc) and trigger phrasing that minimizes overlap with unrelated skills.

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.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
mims-harvard/ToolUniverse
Reviewed

Table of Contents

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