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.

Quality

75%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide

Securityby

Low

Low-risk findings worth noting

Fix and improve this skill with Tessl

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

Quality

Content

57%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 thorough, well-organized body with concrete tool guidance, but it is padded in summary sections, lacks validation feedback loops for batch metabolite processing, and ships broken/missing file references. Tightening prose and fixing the QUICK_START.md link would meaningfully raise the score.

Suggestions

Create or remove the referenced 'QUICK_START.md' (and link the existing 'scripts/metabolism_ref.py') so every reference in the body resolves to a real file.

Add explicit validation/retry checkpoints for batch metabolite processing (e.g., verify HMDB hit count before annotating, retry PubChem fallback on failure) to satisfy the workflow-clarity feedback-loop requirement.

Trim the restating 'Summary', 'Key Features', and 'Best for' sections to eliminate redundancy with the workflow and database sections.

Dimension	Reasoning	Score
Conciseness	Mostly efficient but padded in places — the 'Summary', 'Key Features' checkmark list, and 'Best for' sections restate earlier workflow content, and some Implementation Notes over-explain concepts Claude already knows.	3 / 5
Actionability	Provides concrete tool names, SOAP parameter handling ('operation="search"'), response-format branches, and fallback hierarchy, but stops at description rather than copy-paste ready code examples, leaving minor gaps.	4 / 5
Workflow Clarity	The 4-phase pipeline is clearly sequenced, but batch metabolite processing and report generation lack explicit validation checkpoints or error-recovery feedback loops, which caps the score at 3 per the batch-operation guideline.	3 / 5
Progressive Disclosure	Structure exists, but the body references a non-existent 'QUICK_START.md' and never points to the bundled 'scripts/metabolism_ref.py', so navigation is partly broken and bulk detail is inlined rather than split out.	3 / 5
	Total	13 / 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, specific description that clearly states both capabilities and concrete use triggers within a well-defined metabolomics niche. Minor gains possible from adding common synonyms and file extensions to the trigger terms.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions — 'metabolite identification, study analysis, and database searches', 'annotating mass-spec features to known metabolites', 'finding metabolomics studies of a disease', 'structured metabolomics research reports' — giving comprehensive coverage rather than vague language.	5 / 5
Completeness	Explicitly answers both what the skill does and when to use it via the 'Use for ...' clause with concrete trigger phrases ('annotating mass-spec features', 'finding metabolomics studies of a disease').	5 / 5
Trigger Term Quality	Strong natural terms ('mass-spec features', 'metabolomics studies', 'disease', 'metabolite-pathway mapping') but missing common synonyms and concrete file extensions a user might say, so just below the comprehensive anchor.	4 / 5
Distinctiveness Conflict Risk	Occupies a clear metabolomics niche with distinct triggers (HMDB, MetaboLights, Metabolomics Workbench, KEGG, mass-spec annotation) that are unlikely to fire for unrelated skills.	5 / 5
	Total	19 / 20 Passed

Validation

100%

Warnings & errors only

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: plugin/skills/tooluniverse-metabolomics/SKILL.md
Commit: 1b02920

Reviewed: 1 day ago

Table of Contents

Discovery Implementation Validation

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.