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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.

60

Quality

76%

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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, fully executable API skill with excellent copy-paste examples and a properly signaled one-level-deep reference file. Main weakness is redundancy: workflows and bullet lists repeat endpoint material already shown in the capability sections, which inflates token cost without adding guidance.

Suggestions

Deduplicate the 'Common Workflows' by referencing the capability sections or showing only the novel endpoint in each step instead of repeating full URLs.

Trim the 'Key operations' bullet lists where they restate what the adjacent code examples already demonstrate.

Make workflow checkpoints explicit (e.g. 'Verify the response contains study_id values before requesting data') to strengthen validation in the multi-step workflows.

DimensionReasoningScore

Conciseness

The three 'Common Workflows' repeat endpoint URLs verbatim from the capability sections, and each capability pairs a 'Key operations' bullet list with near-duplicate code examples — mostly efficient, but noticeably tighten-able redundancy keeps it below the 'minor trimmings' anchor at 4.

3 / 5

Actionability

Every example is a complete, copy-paste-ready `requests.get(...)` call with concrete URLs covering the common cases across all six API contexts; fully executable with no pseudocode.

5 / 5

Workflow Clarity

Three workflows are clearly sequenced with numbered steps and concrete endpoints, but checkpoints ('Review candidate compounds from results', 'Review study IDs from results') are implicit rather than explicit validation steps — matching 'clear sequence with most checkpoints present, minor gaps'.

4 / 5

Progressive Disclosure

The bundle's `references/api_reference.md` exists, is one level deep, and is well signaled with explicit load conditions; however, some inline endpoint detail (e.g. four example queries per capability) could be trimmed or moved to the reference, so it does not fully meet the 'concise overview' anchor at 5.

4 / 5

Total

16

/

20

Passed

Description

70%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 capabilities and good trigger terms, but it lacks an explicit 'Use when...' trigger clause, leaving the 'when to use' guidance only weakly implied. Adding a trigger sentence would raise completeness substantially.

Suggestions

Append an explicit trigger clause, e.g. 'Use when querying the Metabolomics Workbench, standardizing metabolite names via RefMet, or performing m/z-based compound identification.'

Include one or two additional natural user phrasings such as 'mass spectrometry searches' or 'metabolite identification' to broaden trigger coverage.

Mention the gene/protein association queries the body documents so the description's capability list matches the skill's full scope.

DimensionReasoningScore

Specificity

Lists several specific actions ('Query metabolites, RefMet nomenclature, MS/NMR data, m/z searches, study metadata') with only minor gaps — gene/protein lookups and structure downloads covered by the skill body are absent, so it falls short of the comprehensive anchor at 5.

4 / 5

Completeness

The 'what' is clear and concrete (REST API access, querying metabolites, RefMet, m/z searches), but there is no explicit 'Use when...' clause — only a weakly implied 'for metabolomics and biomarker discovery', which caps completeness at 3 per the rubric guidelines.

3 / 5

Trigger Term Quality

Good natural keyword coverage ('metabolomics', 'biomarker discovery', 'm/z', 'metabolites', 'RefMet'), but a few natural terms users would say are missing, e.g. 'mass spectrometry' spelled out or 'study data' phrasing.

4 / 5

Distinctiveness Conflict Risk

Names a clearly distinctive niche (NIH Metabolomics Workbench, RefMet, m/z searches) with domain-specific triggers; minimal risk of firing for the wrong skill.

5 / 5

Total

16

/

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.

Validation — 14 / 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

Path

backend/cli/skills/databases/metabolomics-workbench-database/SKILL.md

Reviewed

Table of Contents

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