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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.

74

Quality

92%

Does it follow best practices?

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SecuritybySnyk

Passed

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SKILL.md
Quality
Evals
Security

Quality

Content

85%

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

The body is well-structured, actionable, and uses progressive disclosure effectively with a clear workflow and audit checkpoint. The main weakness is minor redundancy across the DeepScientist integration and Default stance sections that slightly inflates the token budget.

Suggestions

De-duplicate the 'Do not invent DOIs, accession numbers, repositories, licences...' guidance, which appears in both the DeepScientist integration and Default stance sections; state it once.

Tighten the Default stance list by merging overlapping bullets (e.g., the repository-preference and availability-description guidance) to reduce token cost without losing meaning.

Consider moving the detailed Chinese-to-English term conversion table into references/chinese-author-alignment.md since that file is already opened for Chinese users, keeping the main body to the most common conversions.

DimensionReasoningScore

Conciseness

The body is mostly efficient and domain-specific rather than explaining concepts Claude already knows, but guidance like 'Do not invent DOIs, accession numbers, repositories...' is repeated in both the DeepScientist integration and Default stance sections, and several stances could be tightened.

2 / 3

Actionability

Provides concrete, executable guidance: a 7-route access classification taxonomy, a named identifier strategy (DOI, accession, Handle, ARK), an explicit Chinese-to-English term mapping table, and a copy-paste-ready output format template.

3 / 3

Workflow Clarity

An 8-step workflow is clearly sequenced with an explicit validation checkpoint ('Run the FAIR and metadata audit before finalizing') and a return of unresolved fields, plus a referenced FAIR checklist for the audit step.

3 / 3

Progressive Disclosure

A 'Related files' table clearly signals when to open each of six one-level-deep reference files, all of which exist in references/, giving well-organized navigation off a concise overview.

3 / 3

Total

11

/

12

Passed

Description

100%

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, third-person, and supplies both concrete capabilities and a rich explicit 'Use when' trigger clause. It is a strong, well-scoped description with no notable weaknesses.

DimensionReasoningScore

Specificity

Lists multiple concrete actions ('Prepare, audit, or revise') applied to specific objects ('Data Availability statements, data repository plans, dataset citations, and FAIR metadata checklists'), matching the level-3 anchor of multiple specific concrete actions.

3 / 3

Completeness

It explicitly answers both what ('Prepare, audit, or revise...') and when ('Use when the user asks about...') with explicit triggers, satisfying the level-3 anchor for both what AND when.

3 / 3

Trigger Term Quality

The 'Use when the user asks about...' clause covers a broad set of natural terms a user would say (Nature data availability, repository selection, accession numbers, restricted data, source data, supplementary datasets, DataCite-style references, FAIR metadata), matching good coverage of natural terms.

3 / 3

Distinctiveness Conflict Risk

It carves a clear niche (Nature-family Data Availability statements and FAIR metadata) with distinct, domain-specific triggers unlikely to fire for unrelated skills.

3 / 3

Total

12

/

12

Passed

Validation

93%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

15

/

16

Passed

Repository
ResearAI/DeepScientist
Reviewed

Table of Contents

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