CtrlK
BlogDocsLog inGet started
Tessl Logo

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.

62

Quality

73%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide

SecuritybySnyk

Low

Low-risk findings worth noting

Fix and improve this skill with Tessl

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

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

SKILL.md
Quality
Evals
Security

Quality

Content

46%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 well-structured, detailed skill body with concrete tool names and a clear 4-phase pipeline, but it suffers from triple-redundancy of the phase descriptions, no executable code examples, and broken/incomplete bundle references. The batch operation lacks explicit verification checkpoints, capping workflow clarity at 3.

Suggestions

Remove the redundant restatement of the 4 phases in 'Usage Patterns' and 'Summary'; keep one canonical workflow description and reference it.

Fix the broken 'QUICK_START.md' reference (line 275) — either create it as a bundle file or remove the citation — and add a reference to the existing 'scripts/metabolism_ref.py' bundle file where appropriate.

Move the 'Reasoning Framework' mass-spectrometry section (lines 246-267) into a separate reference file (e.g. references/identification_reasoning.md) and link to it from the main body.

Add at least one executable Python snippet showing a complete tool call (e.g. HMDB_search with operation parameter) to lift actionability above 3.

DimensionReasoningScore

Conciseness

Mostly efficient but redundant: the 4-phase pipeline is described three times (Research Workflow lines 37-62, Usage Patterns lines 66-106, Summary lines 227-231) and the 'Key Features' checkmark block ('✅ 100% test coverage', '✅ Implementation-agnostic documentation') is marketing-style padding. Could be tightened significantly without losing clarity.

3 / 5

Actionability

Gives concrete tool names and parameters ('HMDB_search: Requires operation="search" parameter', 'FooDB_get_compound (param fdb_id, e.g. "FDB000004")', 'Always check response type with isinstance()') but provides NO executable code blocks anywhere. Specific enough to act on, but missing copy-paste-ready examples; matches the 'some concrete guidance but incomplete' anchor.

3 / 5

Workflow Clarity

4-phase pipeline is clearly sequenced (lines 37-62) with a fallback hierarchy (HMDB → PubChem) and error-recovery ('continue with remaining phases'). However this is a batch skill processing metabolite lists and writing files, and the 'validation' present is error-avoidance rather than explicit verification checkpoints — per the batch-operation cap, cannot exceed 3.

3 / 5

Progressive Disclosure

Body references 'QUICK_START.md' (line 275) which does NOT exist as a bundle file, and never references the one existing bundle file 'scripts/metabolism_ref.py'. The ~25-line 'Reasoning Framework' mass-spectrometry section (lines 246-267) is inlined content that clearly belongs in a separate reference file. Broken reference plus unreferenced bundle matches 'references are buried / content that clearly belongs in separate files is inlined.'

2 / 5

Total

11

/

20

Passed

Description

100%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.

This is a strong description: it uses third-person voice, names concrete actions and specific databases, provides explicit 'Use for...' trigger guidance with multiple natural scenarios, and occupies a clear niche with low conflict risk. It scores at the top of the rubric across all four dimensions.

DimensionReasoningScore

Specificity

Lists multiple concrete actions including 'metabolite identification, study analysis, and database searches across HMDB, MetaboLights, Metabolomics Workbench, KEGG' plus 'annotating mass-spec features to known metabolites' and 'metabolite-pathway mapping' — comprehensive coverage matching the anchor 5 example.

5 / 5

Completeness

Clearly states what ('metabolite identification, study analysis, and database searches across [databases]') and when ('Use for annotating mass-spec features to known metabolites, finding metabolomics studies of a disease, and structured metabolomics research reports') with concrete trigger phrases, matching anchor 5.

5 / 5

Trigger Term Quality

Natural metabolomics terms include 'metabolomics', 'metabolite identification', 'mass-spec features', and named databases (HMDB, MetaboLights, Metabolomics Workbench, KEGG) that a user in this domain would explicitly mention — comprehensive coverage including synonyms/extensions as in the anchor 5 example.

5 / 5

Distinctiveness Conflict Risk

Clear niche in metabolomics with named databases (HMDB, MetaboLights, Metabolomics Workbench, KEGG) and specific triggers like 'mass-spec features' and 'metabolite-pathway mapping' — minimal conflict risk with other skills, matching anchor 5.

5 / 5

Total

20

/

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

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.