Nature DataSAFE
Semi-automated research assistant for academic research and software development. Supports Claude Code, Codex CLI, Kimi Code CLI, and OpenCode across ideation, coding, experiments, writing, and publication.
Overview
From the repository's own README, as read at the audited commit. Badges and raw HTML are left out.
A data-availability skill for preparing manuscript data statements, repository plans, dataset citations, and FAIR metadata checks in a Nature / Springer Nature publication style.
This skill is bilingual-aware. It accepts Chinese author notes covering data availability statements, data requests to the corresponding author, raw data, restricted data, or public databases, then converts them into submission-ready English with Chinese action notes for the author.
What it does
- drafts ready-to-paste Data Availability statements
- audits weak or incomplete data statements before submission
- maps each supporting dataset to a repository, accession, DOI, or access route
- distinguishes public, controlled-access, third-party, supplementary, and not-applicable cases
- prepares FAIR metadata and DataCite-style dataset citation checks
- flags missing repository records, licences, provenance, embargo details, and access conditions
- aligns Chinese author intent with Nature-style English availability wording
Source hierarchy
- Nature Portfolio and Springer Nature research data policies
- Nature Portfolio reporting standards for availability of data, code, materials, and protocols
- Scientific Data data policies for repository, rawness, preservation, and data citation practice
- FAIR Guiding Principles and DataCite metadata schema
File structure
nature-data/ ├── SKILL.md ├── README.md ├── agents/ │ └── openai.yaml └── references/ ├── fair-metadata-checklist.md ├── chinese-author-alignment.md ├── policy-principles.md ├── repository-and-identifiers.md ├── source-basis.md └── statement-patterns.md
When to use
- preparing a Data Availability statement for a Nature-family or Springer Nature journal
- deciding where to deposit data before submission
- revising "available on request" language
- handling controlled-access, human-participant, proprietary, or third-party data
- citing datasets with DOI, access
29ad4d4206fbOBSERVED · 2026-10-07What it tells the agent
The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.
--- name: nature-data description: >- 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, restricted or sensitive data, source data, supplementary datasets, DataCite-style dataset references, FAIR metadata for academic publication, or Chinese-to-English data availability wording for Chinese-speaking authors preparing Nature-family submissions. --- # Nature Data Availability Skill Use this skill to turn a manuscript's supporting data into a transparent, Nature-ready data availability package: statement text, repository plan, dataset citations, and missing-information flags. The governing policy layer is Springer Nature / Nature Portfolio data policy. The implementation layer is FAIR data practice and DataCite-style citation metadata. For academic wording only, the active installed `skills/ml-paper-writing/references/knowledge/paper-miner-writing-memory.md` under the current client's skill home may offer relevant phrasing examples. Use it only when it contains source-attributed availability language. Journal policy and the author's actual data-access facts take precedence. If the memory is absent or irrelevant, continue without it. ## Chinese-user operating mode When the user writes in Chinese, provides a Chinese manuscript note, or asks for "中文对应", "中英对照", "数据可用性声明", "数据获取声明", "原始数据", "数据存储库", or "受限数据": - Accept Chinese input naturally, but draft the final submission-ready statement in English unless the user explicitly asks for Chinese only. - Preserve a short Chinese explanation of unresolved decisions when it helps the author act. - Translate intent, not wording. Chinese phrases such as "可向通讯作者索取" are usually too vague for Nature-style English unless the restriction and access process are specified. - Convert Chinese repository/status descriptions into precise publication terms: `数据可用性声明` -> `Data Availability`; `原始数据` -> `raw data`; `处理后数据` -> `processed data`; `源数据` -> `source data`; `补充材料` -> `Supplementary Information`; `受限数据` -> `restricted data`; `合理请求` -> `reasonable request`, only with reason and review route. - Use `references/chinese-author-alignment.md` for Chinese terminology, common CN-to-EN failure modes, and bilingual intake questions. ## Default stance - Treat the Data Availability statement as a link between the paper's claims and the evidence needed to inspect, reproduce, or reuse them. - Do not invent DOIs, accession numbers, repository names, licences, embargo dates, ethics approvals, access committees, or data-use conditions. - Prefer public, discipline-specific repositories. Use generalist or institutional repositories only when no suitable community repository exists. - Describe both newly generated data and reused third-party data. - If data cannot be openly shared, state why, who controls access, how requests are evaluated, and what metadata or representative data can still be public. - Separate data, code, materials, and protocols unless the journal asks for a combined availability section. - Keep this skill focused on availability and metadata. Do not rewrite methods, analyze statistics, or polish the manuscript unless the user asks for those tasks separately. - Flag "available upon request" as weak unless there is a specific legal, ethical, commercial, or third-party restriction. ## Workflow 1. Identify the target journal and article type. If journal-specific instructions conflict with this skill, follow the journal. 2. Inventory every dataset needed to support the main and supplementary results: generated raw data, processed data, figure source data, secondary data, software outputs, models, tables, images, and files underlying statistical analysis. 3. Classify each dataset into one access route: `public repository`, `controlled access repository`, `within paper or supplement`, `reused public source`, `third-party restricted`, `available on justified request`, or `not applicable`. 4. Choose repository and identifier strategy before drafting text. Prefer DOI, accession number, Handle, ARK, or stable repository record over personal websites and temporary cloud links. 5. Draft the Data Availability statement using explicit dataset-to-location mapping. 6. Add formal dataset citations for public data that support conclusions. 7. Run the FAIR and metadata audit before finalizing. 8. Return ready-to-paste statement text plus any unresolved fields the author must confirm. ## Output format Unless the user asks for another format, return: ```text Data Availability [ready-to-paste statement] Repository and citation actions - [specific actions or "None"] Missing information / risk flags - [specific flags or "None"] 中文核对 - [用中文列出作者需要确认的字段或 "无"] ``` When auditing an existing statement, lead with blocking issues first, then provide a revised version. ## Related files | File | Open when | |---|---| | [references/policy-principles.md](references/policy-principles.md) | You need the governing Nature/Springer Nature data-sharing rules or edge-case policy logic | | [references/chinese-author-alignment.md](references/chinese-author-alignment.md) | The user writes in Chinese, needs bilingual wording, or provides Chinese availability notes | | [references/statement-patterns.md](references/statement-patterns.md) | You need ready-to-adapt Data Availability statement patterns | | [references/repository-and-identifiers.md](references/repository-and-identifiers.md) | You need repository choice, accession, DOI, embargo, versioning, or dataset citation guidance | | [references/fair-metadata-checklist.md](references/fair-metadata-checklist.md) | You need FAIR checks, README metadata, file organization, licences, provenance, or DataCite fields | | [references/source-basis.md](references/s
Trust audit
SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.
| Layer | What it checks | Result |
|---|---|---|
| L0 | Provenance & inventory | PASS |
| L1 | Static analysis of the code | NA |
| L2 | Instruction surface (what it tells the agent) | PASS |
| L3 | Class-specific surface | PASS |
| L4 | Behavioural (sandbox) | SKIPPED |
What the source does
- Filesystem
- none-observed
- Network
- none-observed
- Shell
- none-observed
- Dependencies
- pinned
- Secrets in source
- none-found
Findings (0)
No findings outside the package's declared scope.
Gates applied: no_behavioural_pass.
29ad4d4206fbfull audit observations/trust-audit/skill/galaxy-dawn__nature-data.json · Report an issue / request a re-scanAudit history
Every audit this skill has had.
| Date | Source | Verdict | Grade | Score | Change |
|---|---|---|---|---|---|
| 2026-10-07 | 29ad4d4206fb | SAFE | B | 89 | first audit |
Questions
What does the Nature Data skill do?
Semi-automated research assistant for academic research and software development. Supports Claude Code, Codex CLI, Kimi Code CLI, and OpenCode across ideation, coding, experiments, writing, and publication.
Is Nature Data safe to install?
The audit found nothing in the source that contradicts what it says it does, and graded it B (89/100). Grade A is held back for packages that have also passed a sandboxed behavioural run, which is why a clean skill reads B.
What can Nature Data access on my machine?
The audit observed no filesystem, network or shell use at all in its source.
How current is this page?
The grade is for one exact copy of the source (29ad4d4206fb), read on 2026-10-07. The repository is watched, and a new audit runs when it changes — this is the first audit.