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.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/yuan1z0825/nature-skills --skill nature-data- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads nature-data automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
Security audits
Independent scans of the skill's instructions and bundled files.
SKILL.md
The instructions your agent reads when the skill is triggered.
Nature Data Availability — Router
Routing protocol
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.
1. Load the manifest and the core layer
Read manifest.yaml. Then read every file listed under always_load:
static/core/stance.md— what the data-availability package is, the default stance, and the source hierarchy.static/core/workflow.md— the eight-step workflow and the output format.
2. No content axis — confirm journal and language inline
Unlike nature-writing or nature-figure, nature-data has no fragment axis. Its variation is handled at runtime, not by loading different content bodies:
- journal/article type — if journal-specific instructions conflict with this skill, follow the journal.
- access route — each dataset is classified into one route (public repository, controlled access, within paper, reused public, third-party restricted, justified request, or not applicable).
- user language — if the user writes Chinese or requests Chinese guidance, read
static/core/chinese-mode.mdand add the 中文核对 block unless the user requested statement text only.
3. Run the workflow
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.
4. Reach for references only when needed
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.
FAQ
What does the nature-data skill do?
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.
How do I install nature-data?
Run npx skills add https://github.com/yuan1z0825/nature-skills --skill nature-data in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/nature-data/ (personal) or .claude/skills/nature-data/ (project).
Which AI agents can use nature-data?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is nature-data safe to install?
Security partners have reviewed it: Gen Agent Trust Hub passed; Socket passed; Snyk passed. As with any skill, read the SKILL.md and bundled scripts before installing.
Who maintains nature-data?
It is published by yuan1z0825 in yuan1z0825/nature-skills. It has 15,443 recorded installs.
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