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metabolights-skill

Submit compact MetaboLights requests for study discovery and study-level metabolomics metadata. Use when a user wants concise MetaboLights summaries

67

Quality

80%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

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tessl review fix ./plugins/life-science-research/skills/metabolights-skill/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

93%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 a lean, executable single-purpose REST skill with a copy-paste example and well-organized sections referencing its one bundle file, with only a minor gap in explicit error-handling/validation guidance.

Suggestions

Add an explicit checkpoint telling Claude to inspect the `ok` field and branch on `error.code`/`error.message` (e.g., narrow paging or retry) before producing the markdown summary.

Optionally show a one-line failure example so the error contract is concrete rather than only described.

DimensionReasoningScore

Conciseness

The body is lean and assumes Claude's competence: short operating rules, field lists, and one executable example with no padding or explanation of what MetaboLights/libraries are, so every token earns its place.

5 / 5

Actionability

It gives a copy-paste-ready bash invocation plus concrete JSON patterns for the common cases (study discovery with record_path/max_items and a targeted studies/<accession> record), and lists exact required/optional input fields, fully covering the common usage.

5 / 5

Workflow Clarity

The single action (pipe a JSON object to scripts/rest_request.py) is unambiguous with preferred paths stated, but there is no explicit validation/error-recovery checkpoint telling Claude to check the `ok` field and react to `ok=false` before summarizing, a minor gap below the explicit-feedback-loop anchor at 5.

4 / 5

Progressive Disclosure

The under-50-line skill is organized into clear sections and correctly references its single one-level-deep bundle file (scripts/rest_request.py, which exists), with the References section explicitly stating no further runtime references are needed, satisfying the simple-skill well-organized-sections bar.

5 / 5

Total

19

/

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 a distinct, niche-scoped trigger for MetaboLights with an explicit 'Use when' clause, but its capability list is narrow and its trigger phrasing covers only the concise-summary use case rather than the full discovery/metadata range.

Suggestions

Broaden the 'Use when' trigger to also cover study discovery and accession lookup (e.g., 'Use when a user wants MetaboLights study discovery, study-level metadata, or concise summaries').

Add one or two more concrete capabilities (e.g., browsing the studies archive, fetching a specific MTBLS accession record) to move specificity toward comprehensive coverage.

Include common user phrasings like 'MTBLS accession' or 'metabolomics study data' as trigger terms so the natural vocabulary is more complete.

DimensionReasoningScore

Specificity

It names the MetaboLights domain and two concrete actions ('study discovery', 'study-level metabolomics metadata'), but does not enumerate several distinct actions, matching the 1-2-actions anchor rather than the several-actions anchor at 4.

3 / 5

Completeness

Both the 'what' (submit compact MetaboLights requests for study discovery and metadata) and an explicit 'when' ('Use when a user wants concise MetaboLights summaries') are present, but the trigger only covers the summaries use and omits study-discovery/accession triggers, so it is not the fully comprehensive anchor at 5.

4 / 5

Trigger Term Quality

It surfaces 'MetaboLights', 'metabolomics', and 'study discovery' as relevant terms, but misses common variations/accession forms like MTBLS accessions or 'metabolomics data', fitting 'some relevant keywords but missing synonyms' rather than full coverage at 4.

3 / 5

Distinctiveness Conflict Risk

MetaboLights is a specific named database with distinct, domain-bound triggers ('MetaboLights', 'metabolomics metadata'), giving a clear niche with minimal risk of firing for unrelated skills.

5 / 5

Total

15

/

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
openai/plugins
Reviewed

Table of Contents

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