nature-academic-search
Search literature across sources, verify or manage citations, and build MeSH strategies or citation-impact audits. Use for…
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.
Claude Code
$ npx -y skills add Yuan1z0825/nature-skills --skill nature-data --agent claude-code
Codex
$ npx skills add Yuan1z0825/nature-skills --skill nature-data -a codex -y
Cursor
$ npx skills add Yuan1z0825/nature-skills --skill nature-data -a cursor -y
Gemini CLI
$ npx skills add Yuan1z0825/nature-skills --skill nature-data -a gemini-cli -y
GitHub Copilot
$ npx skills add Yuan1z0825/nature-skills --skill nature-data -a github-copilot -y
OpenCode
$ npx skills add Yuan1z0825/nature-skills --skill nature-data -a opencode -y
Windsurf
$ npx skills add Yuan1z0825/nature-skills --skill nature-data -a windsurf -y
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.
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