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Nature Data

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YuanyuanMa03
nature-data

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.

Overview

PublisherYuanyuanMa03
Repositoryacademic-research-skills
Skill namenature-data
Stars
76
Forks
7
Bundled files
11
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 11 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by YuanyuanMa03 on GitHub. Read the source before you install it.

Installation

Install the Nature Data AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/YuanyuanMa03/academic-research-skills/tree/main/plugins/nature-data/skills/nature-data
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/YuanyuanMa03/academic-research-skills.git /tmp/academic-research-skills
mkdir -p .claude/skills
cp -r /tmp/academic-research-skills/plugins/nature-data/skills/nature-data .claude/skills/nature-data
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Nature Data in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Nature Data on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Nature Data is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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.

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

FileOpen when
references/policy-principles.mdYou need the governing Nature/Springer Nature data-sharing rules or edge-case policy logic
references/chinese-author-alignment.mdThe user writes in Chinese, needs bilingual wording, or provides Chinese availability notes
references/statement-patterns.mdYou need ready-to-adapt Data Availability statement patterns
references/repository-and-identifiers.mdYou need repository choice, accession, DOI, embargo, versioning, or dataset citation guidance
references/fair-metadata-checklist.mdYou need FAIR checks, README metadata, file organization, licences, provenance, or DataCite fields
references/source-basis.mdYou need to justify rules with official sources or check which source supports which rule

Source hierarchy

Use sources in this order:

  1. Target journal instructions and submission system requirements.
  2. Nature Portfolio / Springer Nature data, code, materials, and reporting policies.
  3. Repository-specific requirements and domain community standards.
  4. FAIR principles and DataCite metadata practice.

If a policy detail may have changed, verify the current journal page before giving final submission advice.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Nature Data AI skill do?

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.

Why use Nature Data on TypingMind?

Because you install it once and use it with any model. Nature Data is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Nature Data in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/YuanyuanMa03/academic-research-skills/tree/main/plugins/nature-data/skills/nature-data. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Nature Data?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Nature Data?

As many as you like. As long as a model supports skills, you can use Nature Data with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Nature Data AI skill free?

Yes. It is published on GitHub by YuanyuanMa03 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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