nature-data

Use it when:: You need a journal-ready Data/Code Availability statement or data-sharing plan, or want an existing statement audited.
It does:: Inventories each dataset, classifies its access route, chooses repositories and identifiers, drafts the statement and runs a FAIR metadata audit.
You get:: Ready-to-paste statement text mapping each dataset to a concrete location, plus a list of fields you still need to fill.
Keeps:: "available upon request" flagged as weak unless a specific restriction justifies it.

Its Nature Machine Intelligence rules are a file in the repo's nature-shared support package; install that alongside if you target NMI.

TL;DR

An eight-step workflow for the data-availability side of submission. It identifies the journal, inventories every supporting dataset, and classifies each into one access route (public repository, controlled access, within paper, reused public, third-party restricted, justified request, or not applicable), then chooses a repository and identifier strategy before drafting. The statement maps each dataset to a concrete location, adds formal dataset citations, and passes a FAIR audit, a check that the data are findable, accessible, interoperable and reusable. It never invents DOIs, accession numbers, licences, embargo dates or access committees, and returns the statement text plus the fields you still need to resolve. Editing or auditing one existing statement keeps its identifiers and skips the full inventory. It carries exact rules for flagship Nature and for Nature Machine Intelligence, including NMI's separate Code availability section; the NMI rules come from the repo's nature-shared package. Chinese-speaking authors also get a Chinese check block. It is not for data cleaning or statistical analysis.

Summarizes the skill at d774add.

npx skills add https://github.com/yuan1z0825/nature-skills --skill nature-data

skills.sh page Source on GitHub All skills from yuan1z0825/nature-skills

Total Downloads: 16,100 on skills.sh, Nov 9, 2026
Repo stars: 52,794 yuan1z0825/nature-skills
First seen: May 1, 2026 on skills.sh

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Category	category:science category:writing Research reproduction, financial data, charts and diagrams. Articles, docs, internal comms, research write-ups and prose that doesn't read as generated.
Type	type:workflow A multi-step process the agent follows: interview, debug, run a TDD loop, review.
Effect	effect:none Adds no side effects of its own. It shapes how the agent thinks, writes or reviews. Guidelines and routers are always effect:none, even when the task they support edits code.
Needs	needs:none Instructions only. Works in any agent that supports skills.
Publisher	by:community Everyone else. Unverified vendor claims default to community.
Topics	research

SKILL.md

From yuan1z0825/nature-skills at d774add. Checked daily, last changed Nov 6, 2026.

Frontmatter

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

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.md and 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.