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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.

54

Quality

62%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

Fix and improve this skill with Tessl

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

Quality

Content

58%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.

The content is well-structured and highly actionable, with executable examples and a clear one-level-deep reference to a real bundle file. It is held back by repeated boilerplate across snippets and workflows that lack explicit validation checkpoints.

Suggestions

Trim repeated 'import requests' and full base-URL boilerplate from each snippet; define once and show only the differing path.

Add explicit validation checkpoints to workflows (e.g., check HTTP status, confirm non-empty result set before iterating, handle paginated/large responses with a concrete loop).

Move the duplicated endpoint-pattern examples that also appear in api_reference.md into the reference to reduce token overhead.

DimensionReasoningScore

Conciseness

The body is mostly useful and example-driven, but repeats 'import requests' and full URL boilerplate in every snippet and restates endpoint patterns the bundled api_reference.md already covers, which could be tightened.

3 / 5

Actionability

Code examples are concrete and executable copy-paste-ready REST calls covering the common cases, with only minor gaps (e.g., no shown response parsing or error handling).

4 / 5

Workflow Clarity

The three workflows are clearly sequenced, but they lack explicit validation checkpoints despite being read-only API calls; more importantly, the batch/large-result guidance ('Handle pagination', 'Cache reference data') is described rather than enforced, so checkpoints are missing or implicit.

3 / 5

Progressive Disclosure

Good structure: an overview body with well-signaled, one-level-deep pointer to references/api_reference.md (verified to exist), with key examples kept inline and bulk detail deferred; only minor organization gaps keep it from a 5.

4 / 5

Total

14

/

20

Passed

Description

66%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.

The description is specific and well-targeted to a distinct niche, naming concrete capabilities and natural domain keywords. Its main weakness is the absence of an explicit 'Use when...' trigger clause, leaving the invocation condition only weakly implied.

Suggestions

Add an explicit trigger clause, e.g. 'Use when querying the Metabolomics Workbench for metabolites, RefMet name standardization, m/z searches, or study metadata.'

Lead with concrete verbs ('Query', 'Search', 'Standardize') rather than noun tags to sharpen the action list.

Add common synonyms such as 'mass spectrometry' and 'metabolomics studies' to broaden natural-term coverage.

DimensionReasoningScore

Specificity

Lists several specific actions ('Query metabolites, RefMet nomenclature, MS/NMR data, m/z searches, study metadata') with concrete nouns, but phrases them as capability tags rather than distinct verbs and leaves some gaps (e.g., gene/protein access omitted).

4 / 5

Completeness

It clearly states what the skill does, but there is no explicit 'Use when...' trigger clause, so the 'when' is only weakly implied by the trailing 'for metabolomics and biomarker discovery' purpose phrase, which caps completeness at 3 per the rubric.

3 / 5

Trigger Term Quality

Strong natural-domain keywords ('metabolites', 'RefMet', 'm/z searches', 'biomarker discovery') that a user would plausibly say, though it lacks common synonyms/variations like 'mass spectrometry' spelled out beyond 'MS/NMR'.

4 / 5

Distinctiveness Conflict Risk

The NIH Metabolomics Workbench niche is distinct and unlikely to trigger for unrelated skills, with only minor overlap risk against other bioinformatics/database skills.

4 / 5

Total

15

/

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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