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metabolomics-workbench-database

Access NIH Metabolomics Workbench via REST API (4,200+ studies). Query metabolites, RefMet nomenclature, MS/NMR data, m/z searches, study metadata, for metabolomics and biomarker discovery.

62

Quality

75%

Does it follow best practices?

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SecuritybySnyk

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tessl review fix ./backend/cli/skills/databases/metabolomics-workbench-database/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

75%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, highly actionable API skill with copy-paste-ready REST examples and a properly signaled one-level-deep reference file. The main weaknesses are repeated full URLs across examples and a slightly padded overview that could be tightened.

Suggestions

Define the base URL once and use a variable or relative paths in subsequent examples to reduce repetition and token cost.

Trim the Overview paragraph to focus on what the API offers programmatically rather than institutional background.

Add a brief note on handling failed/empty responses or pagination in the workflows so read-only lookups have a light validation checkpoint.

DimensionReasoningScore

Conciseness

Mostly efficient with concrete examples, but the full base URL is repeated in every code block and the Overview paragraph ('comprehensive NIH Common Fund-sponsored platform hosted at UCSD...') adds padding that could be trimmed.

3 / 5

Actionability

Fully executable, copy-paste-ready REST URLs across all six capability areas and three workflows, with concrete examples covering the common query cases.

5 / 5

Workflow Clarity

Three workflows are clearly numbered and sequenced (standardize name → search studies → retrieve data); these are read-only GETs so the destructive-validation cap does not apply, but no error/validation checkpoints are given for failed lookups.

4 / 5

Progressive Disclosure

Good structure with a clearly signaled, one-level-deep reference ('references/api_reference.md') and a 'Load this reference file when...' cue, though the inline SKILL.md is fairly heavy for an overview.

4 / 5

Total

16

/

20

Passed

Description

75%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 specific, distinctive description with concrete actions and natural domain keywords, but it lacks an explicit 'Use when...' trigger clause, leaving the 'when to use' guidance only weakly implied. Adding a clear trigger phrase would lift the completeness dimension.

Suggestions

Add an explicit 'Use when...' clause, e.g. 'Use when querying metabolomics data, standardizing metabolite names with RefMet, performing m/z searches, or retrieving study metadata from the Metabolomics Workbench.'

Include common user phrasings and synonyms such as 'mass spectrometry', 'metabolite identification', or 'metabolomics database' to broaden trigger coverage.

Drop the parenthetical study count '(4,200+ studies)' or move it to the body to tighten the description and emphasize triggers.

DimensionReasoningScore

Specificity

Lists multiple concrete actions — 'Query metabolites, RefMet nomenclature, MS/NMR data, m/z searches, study metadata' — giving comprehensive coverage of the skill's capabilities.

5 / 5

Completeness

The 'what' is clear ('Access NIH Metabolomics Workbench via REST API...Query metabolites...'), but there is no explicit 'Use when...' trigger clause — only the weak purpose phrase 'for metabolomics and biomarker discovery', which caps completeness at 3.

3 / 5

Trigger Term Quality

Good natural domain keywords ('metabolomics', 'biomarker discovery', 'metabolites', 'm/z') that a user would say, but a few common variations or synonyms are missing.

4 / 5

Distinctiveness Conflict Risk

Occupies a clear niche ('Metabolomics Workbench', 'RefMet nomenclature', 'm/z searches') with distinct triggers and minimal conflict risk against other skills.

5 / 5

Total

17

/

20

Passed

Validation

87%

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

Validation14 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

metadata_version

'metadata.version' is missing

Warning

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

14

/

16

Passed

Repository
synthetic-sciences/openscience
Reviewed

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