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nature-data

Draft or audit manuscript Data/Code Availability statements, dataset access routes, repository plans, and FAIR metadata. Use for 数据可用性声明、数据共享、数据仓库选择 and dataset citations; not general data cleaning or statistical analysis.

67

Quality

84%

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SecuritybySnyk

Passed

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SKILL.md
Quality
Evals
Security

Quality

Content

71%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 router with lean prose, concrete rules, and a clearly sequenced workflow with an audit checkpoint. Its main defect is bundle integrity: the core layer files it instructs Claude to read first (manifest.yaml, static/core/*) are absent, so progressive disclosure fails at step 1 even though the references/ layer is complete.

Suggestions

Ship or verify the core layer files the body depends on — manifest.yaml, static/core/stance.md, static/core/workflow.md, and static/core/chinese-mode.md — or inline their essential content into SKILL.md, since the current bundle contains none of them.

Add an explicit feedback loop to the workflow (e.g., "if the FAIR audit finds gaps, fix the statement and re-run the audit before returning text") so the validation checkpoint functions as a recovery loop, not just a gate.

Trim the step-3 paragraph that re-enumerates the eight workflow steps already defined in core/workflow.md to remove duplicated guidance.

DimensionReasoningScore

Conciseness

The router body is lean — no explanation of what DOIs or repositories are, and it delegates detail to references. Minor trimmable duplication: step 3 re-summarizes the eight-step workflow ("identify the journal, inventory every supporting dataset, classify each...") that is also stated to live in core/workflow.md. Not 5 due to that duplication; well above the verbose level-3 anchor.

4 / 5

Actionability

Concrete instruction-only guidance: explicit load order ("Read [manifest.yaml](manifest.yaml). Then read every file listed under always_load"), an enumerated closed set of seven access routes, and hard rules ("Do not invent DOIs, accession numbers, repository names, licences..."). Not 5 because the actual drafting patterns and audit criteria live entirely in reference files, none of which are inlined, so a fresh run cannot draft anything until external files load.

4 / 5

Workflow Clarity

A clear 4-step routing sequence plus an inline enumeration of the eight-step workflow ending in a validation checkpoint ("run the FAIR/metadata audit") and a defined return format ("ready-to-paste text plus unresolved fields"). Not 5: there is no explicit feedback loop (audit fails → fix → re-audit) and the full workflow details are deferred to a file that is not in the bundle.

4 / 5

Progressive Disclosure

The design is good — on-demand references, one level deep, each named with its purpose (e.g., "references/policy-principles.md for the governing rules") and all seven references/ files named in the body exist. But judged against the actual bundle: the primary routing targets from step 1 and 2 (manifest.yaml, static/core/stance.md, static/core/workflow.md, static/core/chinese-mode.md) and ../nature-shared/journal-formats/nature-machine-intelligence.md do not exist here, so navigation breaks at the first instruction. Not 2 because the structure that is present is well-signaled and the references/ layer is real and correctly named.

3 / 5

Total

15

/

20

Passed

Description

92%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 strong description: concrete third-person capabilities, an explicit 'Use for...' trigger clause with a negative boundary, and bilingual trigger terms. The only gap is that some natural English synonyms (data sharing, data deposition) are covered only in Chinese.

DimensionReasoningScore

Specificity

"Draft or audit manuscript Data/Code Availability statements, dataset access routes, repository plans, and FAIR metadata" lists multiple concrete actions (draft, audit, plan, metadata) plus "dataset citations" — comprehensive coverage in third-person voice. Not level 4 because no meaningful capability is left unnamed for this domain.

5 / 5

Completeness

Explicitly answers what ("Draft or audit manuscript Data/Code Availability statements, dataset access routes, repository plans, and FAIR metadata") and when ("Use for ... and dataset citations; not general data cleaning or statistical analysis") with concrete trigger phrases and a negative boundary. Both clauses are explicit, so the level-4 anchor ('when could be more explicit') does not apply.

5 / 5

Trigger Term Quality

Good natural terms: "Data/Code Availability statements", "dataset citations", plus Chinese triggers "数据可用性声明、数据共享、数据仓库选择". Not 5 because common English variations users would say — e.g., "data sharing", "data deposition", "repository selection" in English — are only covered in Chinese.

4 / 5

Distinctiveness Conflict Risk

Clear niche (manuscript data-availability statements) with explicit exclusion of adjacent domains ("not general data cleaning or statistical analysis"), minimizing overlap with data-wrangling or statistics skills. Minimal conflict risk; fits the level-5 anchor.

5 / 5

Total

19

/

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

relative_links

Relative link issues: 1 missing

Warning

Total

14

/

16

Passed

Repository
Yuan1z0825/nature-skills
Reviewed

Table of Contents

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