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Nature Writing

CommunityPopular
Yuan1z0825
nature-writing

Draft or restructure scientific manuscript arguments, sections, and initial-submission materials from author-provided evidence. Use for 论文写作、章节起草、论证重构、正文压缩、首次投稿材料. Use nature-polishing for language-only edits to existing prose and nature-response for post-decision correspondence.

Overview

PublisherYuan1z0825
Repositorynature-skills
Skill namenature-writing
Stars
42.8K
Forks
2.3K
Bundled files
75
LicenseApache-2.0
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.

  • 75 bundled files

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

  • Open source

    Published by Yuan1z0825 on GitHub. Read the source before you install it.

Installation

Install the Nature Writing 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.
  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/Yuan1z0825/nature-skills.git /tmp/nature-skills
mkdir -p .claude/skills
cp -r /tmp/nature-skills/skills/nature-writing .claude/skills/nature-writing
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Nature Writing 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 Writing 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 Writing 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-Style Scientific Writing — Router

Routing protocol

For a new drafting task, follow the routing below. For follow-up edits, reuse established task choices and already loaded guidance; read additional fragments only when the requested scope changes.

1. Load the manifest and the core layer

Read manifest.yaml. It declares the axes (task, paper_type, section, language, journal), the allowed values, and the file paths each value maps to.

Also read every file listed under always_load. These hold the default stance, writing workflow, and output format that apply to every drafting job.

2. Detect the axis values for this request

For each axis in the manifest, decide the value using the manifest's detect: hint and the user's input:

  • task — manuscript / submission-package. Use submission-package for first-submission materials, never for revision correspondence.
  • paper_type — research / methods / hypothesis / algorithmic / review. Default: research.
  • section — abstract / intro / related-work / method / experiments / discussion / conclusion / title. May be multiple. Ask the user if it is ambiguous and matters for the draft.
  • language — en or zh-to-en. Detect from the user's notes themselves.
  • journal — nature / nature-family / nat-comms / nat-mach-intell / generic. Default: generic. Use nature only for the flagship journal Nature, nat-comms for Nature Communications, nat-mach-intell for Nature Machine Intelligence (NMI), and nature-family for other Nature Portfolio titles or an unspecified Nature-family request.

State the detected axis values in one short line to the user before drafting, so they can correct you cheaply. This is a progress update, not an approval gate; continue unless a necessary decision remains unresolved.

3. Load the matching fragments

For each axis value, Read the file mapped in the manifest. Skip the section axis when the task is submission-package or when the user explicitly asks for a free-floating argument paragraph with no section context.

Do not read every fragment in static/. Load only what step 2 selected.

4. Draft using the loaded material

Apply the loaded fragments in this priority order:

  1. Core stance + intake (core/stance.md) — surface missing claim / evidence / boundary before drafting.
  2. Paper-type playbook — argument chain, drafting order.
  3. Section-specific drafting rules and structure.
  4. Task-specific submission rules when task=submission-package.
  5. Journal-specific framing and constraints.
  6. Language-specific sentence and paragraph rules (apply last).

For task=manuscript, use core/workflow.md at the requested scale. Plan the argument for a new section or substantial restructuring; a title, single paragraph, or local follow-up needs only the applicable evidence, wording, and consistency checks. Complete the requested prose unless a material unresolved decision blocks it or the user requested an outline for approval first.

When drafting or restructuring Results, or compressing a full manuscript's main text, also load ../nature-shared/core/main-text-discipline.md before building the paragraph map. Classify every result by function, allocate it across main text, captions, Methods/source data, and SI, then draft the shortest sufficient evidence chain. Do not equate a complete analysis record with a complete main text.

When the target is flagship Nature, Nature Communications, Nature Machine Intelligence, or another Nature Portfolio title, load the matching shared Nature-style corpus guidance for the section being drafted:

  • Results or Discussion → ../nature-shared/core/nature-results-discussion.md
  • Introduction or whole-manuscript narrative → ../nature-shared/core/nature-introduction.md
  • Abstract → ../nature-shared/core/nature-abstract.md

Use these files for claim escalation, question-chain alignment, discovery-centred compression, and synthesis. They were initially distilled from published NMI papers and generalized as Nature-style defaults; do not present them as official policy, and let the target journal's current rules override them.

For any Discussion drafting, restructuring, or section audit, also load ../nature-shared/core/discussion-argument-language.md. Use it to select the opening anchor, control the reverse-funnel expansion, distinguish literature positioning from citation decoration, calibrate modal strength to evidence, and turn limitations and future work into claim-specific reasoning. This is general writing guidance rather than an official journal rule.

For task=submission-package, follow static/fragments/task/submission-package.md and references/submission-package.md instead. Build the deliverable matrix and readiness audit; do not force manuscript paragraph architecture onto administrative submission materials.

If essential evidence or boundary is missing, write a placeholder and list it under Assumptions or missing inputs: instead of inventing content.

5. Reach for references only when needed

The files under references/ are deep references and the example library, not defaults. Open them on demand per the references.on_demand table in the manifest. Typical triggers:

  • The user asks for a concrete example or template → references/examples/index.md.
  • A section's draft has structural problems that the section fragment alone does not explain → the matching references/<section>.md.
  • The user needs a broad-audience Nature abstract opening or asks about a summary paragraphreferences/nature-summary-paragraph.md.
  • The user asks "does this paragraph flow?" → references/paragraph-flow.md.
  • The user asks for a self-review or rejection-risk audit → references/paper-review.md.
  • The user asks what belongs in the main text, captions, or SI; wants a shorter Results section; or is adding reviewer-driven explanation → ../nature-shared/core/main-text-discipline.md.
  • The user requests a complete first-submission package, templates, or a submission-readiness audit → references/submission-package.md.
  • The target is the flagship journal Nature and exact submission or formatting requirements matter → ../nature-shared/journal-formats/nature.md.
  • The target is Nature Machine Intelligence and exact content-type, submission, data/code or production requirements matter → ../nature-shared/journal-formats/nature-machine-intelligence.md.
  • Any Nature / Nature Portfolio target needs Results claim progression, evidence-bound interpretation, robustness placement, or Discussion synthesis → ../nature-shared/core/nature-results-discussion.md.
  • Any target needs a Discussion function chain, evidence-calibrated modal language, claim-specific limitations, non-redundant literature positioning, or uncertainty-driven future work → ../nature-shared/core/discussion-argument-language.md.
  • Any Nature / Nature Portfolio target needs an Introduction funnel, exact gap, literature logic, question-first novelty, study roadmap, or alignment with Results → ../nature-shared/core/nature-introduction.md.
  • Any Nature / Nature Portfolio target needs abstract evidence-chain, main/supporting-claim, numeric-result, or final-payoff decisions → ../nature-shared/core/nature-abstract.md.
  • The work involves regulated or specialist research compliance → ../nature-shared/core/research-compliance.md.

Submission boundary

  • nature-writing owns initial submission materials prepared before peer review.
  • nature-response owns revision cover letters, rebuttals, point-by-point responses, marked manuscripts, appeals, and other post-decision correspondence.
  • Route graphical abstracts and TOC graphics to nature-figure; route simulated pre-submission peer review to nature-reviewer.

Bundled files

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

and 15 more files.

Frequently asked questions

What does the Nature Writing AI skill do?

Draft or restructure scientific manuscript arguments, sections, and initial-submission materials from author-provided evidence. Use for 论文写作、章节起草、论证重构、正文压缩、首次投稿材料. Use nature-polishing for language-only edits to existing prose and nature-response for post-decision correspondence.

Why use Nature Writing on TypingMind?

Because you install it once and use it with any model. Nature Writing 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 Writing in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Yuan1z0825/nature-skills/tree/main/skills/nature-writing. 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 Writing?

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 Writing?

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

Is the Nature Writing AI skill free?

Yes. It is published on GitHub by Yuan1z0825 under the Apache-2.0 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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