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nature-data

Prepare, audit, or revise Nature-ready Data Availability statements, data repository plans, dataset citations, and FAIR metadata checklists for manuscripts. Use when the user asks about Nature data availability, research data sharing, repository selection, accession numbers, restricted or sensitive data, source data, supplementary datasets, DataCite-style dataset references, FAIR metadata for academic publication, or Chinese-to-English data availability wording for Chinese-speaking authors preparing Nature-family submissions.

72

Quality

89%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

SKILL.md
Quality
Evals
Security

Quality

Content

78%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, domain-dense skill body with excellent progressive disclosure and an actionable output template. The main gaps are minor: no inline example statement, no explicit audit-retry loop, and a couple of trimmable passages.

Suggestions

Include one compact example Data Availability statement (or cite the exact section of references/statement-patterns.md) inline so the most common drafting case is covered without opening a reference file.

Make the audit loop explicit after workflow step 7: 'If the FAIR/metadata audit finds gaps, resolve them and re-run before finalizing' — this adds the error-recovery feedback loop for workflow clarity.

Tighten the opening paragraph (which repeats the frontmatter) and compress the ml-paper-writing memory paragraph to two lines stating when to consult it and what takes precedence.

DimensionReasoningScore

Conciseness

The body is dense and domain-specific with no padding explaining concepts Claude already knows, but a few passages could be trimmed: the opening paragraph restates the frontmatter description, and the ml-paper-writing memory paragraph is hedgy ("may offer relevant phrasing examples... If the memory is absent or irrelevant, continue without it").

4 / 5

Actionability

Guidance is concrete for an instruction-only skill — a copy-paste-ready output template, an enumerated access-route taxonomy, and explicit CN-to-EN term mappings — but no finished example statement appears inline (all patterns are delegated to references/statement-patterns.md), leaving a minor gap for the most common case.

4 / 5

Workflow Clarity

The 8-step workflow is clearly sequenced with an explicit audit checkpoint ("Run the FAIR and metadata audit before finalizing") and journal-conflict escalation, but there is no explicit fix-and-revalidate loop after the audit fails, so it falls between the 4 and 5 anchors.

4 / 5

Progressive Disclosure

The SKILL.md is a lean overview with a 'Related files' table giving an explicit 'Open when' condition per file; all six referenced files exist in references/ and contain no nested references, so navigation is one level deep and clearly signaled.

5 / 5

Total

17

/

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.

An exemplary description: concrete capabilities, an explicit 'Use when...' clause with comprehensive natural trigger terms (including bilingual variants), and a clearly bounded niche with minimal conflict risk.

DimensionReasoningScore

Specificity

The description names three concrete actions ("Prepare, audit, or revise") across four specific deliverables ("Data Availability statements, data repository plans, dataset citations, and FAIR metadata checklists"), giving comprehensive coverage of the skill's capabilities with no vague filler.

5 / 5

Completeness

It explicitly answers both questions: the "what" via concrete verbs and artifacts, and the "when" via an explicit "Use when the user asks about..." clause listing concrete trigger phrases — exactly the pattern of the top anchor.

5 / 5

Trigger Term Quality

Trigger coverage is extensive and natural: "Nature data availability", "repository selection", "accession numbers", "restricted or sensitive data", "source data", "supplementary datasets", "DataCite-style dataset references", and "Chinese-to-English data availability wording" cover synonyms and situational phrasings users would actually say.

5 / 5

Distinctiveness Conflict Risk

The niche is clearly bounded to Nature-family data availability, repository, and FAIR metadata work; triggers are domain-specific and would not plausibly fire for unrelated skills.

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.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
Galaxy-Dawn/claude-scholar
Reviewed

Table of Contents

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