nature-academic-search
Multi-source literature search, citation verification, strict independent other-citation audits, article-level citation metric tables, influential citer…
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,
$ npx -y skills add Yuan1z0825/nature-skills --skill nature-data --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/nature-dataContext preview
The summary Claude sees to decide when to auto-load this skill.
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,
name: nature-data description: "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." metadata: author: Yuan1z skill, refactored into static/dynamic layers
For a new task, load the core and matching resources below. Reuse already loaded guidance on follow-ups; load more only when the task needs it.
Read [manifest.yaml](manifest.yaml). Then read every file listed under `always_load`:
Unlike nature-writing or nature-figure, nature-data has no fragment axis. Its variation is handled at runtime, not by loading different content bodies:
For a wording edit or audit of one existing statement, preserve supplied repository identifiers and access conditions and check the affected claims. Report gaps relevant to that statement; do not require a full study-wide dataset inventory or repository redesign. Use the complete workflow below for a new data-sharing plan, full statement, or submission audit.
Follow the eight-step workflow in `core/workflow.md`: identify the journal, inventory every supporting dataset, classify each into one access route, choose repository and identifier strategy before drafting, draft the statement with explicit dataset-to-location mapping, add formal dataset citations, run the FAIR/metadata audit, and return ready-to-paste text plus unresolved fields.
Do not invent DOIs, accession numbers, repository names, licences, embargo dates, ethics approvals, access committees, or data-use conditions. Flag "available upon request" as weak unless there is a specific legal, ethical, commercial, or third-party restriction.
The files under `references/` are deep references, not defaults. Open them on demand per the `references.on_demand` table in the manifest — for example `references/policy-principles.md` for the governing rules and edge cases, `references/repository-and-identifiers.md` for repository/accession/DOI choices, `references/statement-patterns.md` for ready-to-adapt statements, `references/fair-metadata-checklist.md` for the FAIR audit, `references/chinese-author-alignment.md` for Chinese wording, and `references/source-basis.md` to justify a rule with its official source.
When the target is the flagship journal Nature, also open `references/nature-article-requirements.md` for statement placement, mandatory-deposition routing, central-code review access, materials and structure-file checks.
When the target is Nature Machine Intelligence, open `../nature-shared/journal-formats/nature-machine-intelligence.md`. Enforce a Data Availability statement and a separate `Code availability` section after it and before references; check reviewer access, precise restrictions, repository/identifier quality and the Software Submission Checklist for newly developed central code.
Repo: Yuan1z0825/nature-skills
Multi-source literature search, citation verification, strict independent other-citation audits, article-level citation metric tables, influential citer…
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