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.

Quality

89%

Does it follow best practices?

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Adds up to 20 points to the overall score

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Securityby

Passed

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Quality

Content

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Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A well-organized instruction-only skill body with strong progressive disclosure and concrete, actionable guidance. The main improvement area is converting the FAIR audit step into an explicit feedback loop and tightening a few conceptual framing sentences.

Suggestions

Reframe workflow step 7 as an explicit feedback loop: after the FAIR/metadata audit, revise the statement and re-audit until no blocking issues remain, then proceed to step 8.

Tighten the Default-stance framing sentences (e.g., "Treat the Data Availability statement as a link between the paper's claims and the evidence...") into direct imperatives to reduce conceptual padding.

Add one short inline ready-to-adapt statement snippet near the Output format, or point to statement-patterns.md earlier in the workflow, to increase copy-paste actionability.

Dimension	Reasoning	Score
Conciseness	Largely lean and assumes Claude's competence (no explanations of what FAIR or a DOI is), but a few conceptual framing sentences in Default stance ("Treat the Data Availability statement as a link between the paper's claims and the evidence needed to inspect, reproduce, or reuse them") could be trimmed to direct directives.	4 / 5
Actionability	Concrete, actionable guidance throughout: an enumerated access-route taxonomy, a Chinese-term conversion table, and a copy-paste-ready output-format block. Minor gap: no inline ready-to-adapt statement snippet, instead deferring to statement-patterns.md.	4 / 5
Workflow Clarity	Clear 8-step sequence with a validation checkpoint (step 7, "Run the FAIR and metadata audit before finalizing") and unresolved-field return (step 8), but the audit step is not framed as an explicit validate-revise-recheck feedback loop.	4 / 5
Progressive Disclosure	Well-structured overview with a Related-files table giving per-file "Open when" conditions; all six referenced paths (policy-principles, chinese-author-alignment, statement-patterns, repository-and-identifiers, fair-metadata-checklist, source-basis) are real one-level-deep references with clear navigation.	5 / 5
	Total	17 / 20 Passed

Description

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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 strong, third-person description that explicitly states both capabilities and trigger conditions with an extensive set of natural trigger terms and a distinct, well-scoped niche. It is somewhat long but each clause adds a distinct trigger rather than padding.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions ("Prepare, audit, or revise") applied to four distinct concrete objects (Data Availability statements, data repository plans, dataset citations, FAIR metadata checklists), giving comprehensive coverage of the skill's capabilities.	5 / 5
Completeness	Explicitly answers both what ("Prepare, audit, or revise Nature-ready Data Availability statements...") and when ("Use when the user asks about...") with concrete trigger phrases.	5 / 5
Trigger Term Quality	Comprehensive natural-term coverage including synonyms and domain variants ("Nature data availability", "research data sharing", "repository selection", "accession numbers", "restricted or sensitive data", "source data", "DataCite-style dataset references", "FAIR metadata", plus Chinese trigger phrases).	5 / 5
Distinctiveness Conflict Risk	Occupies a clear niche (Nature-family data availability / FAIR metadata for manuscripts) with distinct triggers and minimal overlap risk with other skills.	5 / 5
	Total	20 / 20 Passed

Validation

100%

Warnings & errors only

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
Path: skills/nature-data/SKILL.md
Commit: 2847aa1

Reviewed: 1 day ago

Table of Contents

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