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Nature Response

CommunityPopular
Yuan1z0825
nature-response

Draft, audit, or revise responses to peer review, revision cover letters, and marked-manuscript or LaTeX revision packages. Use for 审稿意见回复、逐点回复、返修信、 rebuttals and edits to existing response drafts. Initial-submission materials belong to nature-writing; simulated peer review belongs to nature-reviewer.

Overview

PublisherYuan1z0825
Repositorynature-skills
Skill namenature-response
Stars
42.8K
Forks
2.3K
Bundled files
36
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.

  • 36 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 Response 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-response .claude/skills/nature-response
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Nature Response 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 Response 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 Response 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 Reviewer Response — 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 — the editor-facing purpose, the default stance, the red lines, and the source hierarchy that apply to every response job.
  • static/core/workflow.md — accepted inputs, the revision correspondence workflow, and the output package format.

2. No content axis — identify mode and language inline

Unlike nature-writing or nature-figure, nature-response has no fragment axis. Its variation is identified at runtime, not by loading different content bodies:

  • task modedraft / audit / revise / triage-only / cover-letter / revision-package / latex-template / appeal-like.
  • decision type — minor revision, major revision, revise-and-resubmit, transfer after review, or unclear.
  • user language — if the user writes Chinese, also produce the 中文核对 block.

Decision type is required for a new package-level revision strategy or complete response package. First extract it from the editor letter or task context. If it remains unclear, ask whether this is a Major Revision or Minor Revision and pause only decision-dependent work. A local wording edit, audit of an existing reply, or comment-level triage may proceed with decision type marked unknown. Do not infer the decision from the number, tone, or difficulty of reviewer comments.

Use references/intake-and-routing.md to fix the task mode, minimum inputs, and readiness state before drafting. Route appeal-like cases separately; do not draft an appeal as the default path.

For a local edit or bounded audit, return the revised passage or findings and relevant missing facts. Do not expand it into a master tracker, cover letter, or complete revision package unless requested. Apply the package workflow below only to the parts needed for the requested output.

3. Run the workflow

Follow the workflow in core/workflow.md: if the user pasted a journal email, first parse manuscript metadata, decision type, editor instructions, reviewer reports, required files, deadlines, and reviewer-visibility rules from the email; identify mode and pass the decision-type gate; apply the Major- or Minor-Revision strategy without downgrading the severity of individual comments; extract editor instructions (IDs E.1) then reviewer comments (R1.1, R2.1) when present; classify each item by response action and independently verified work status; build an internal/editor master strategy and tracker; draft a standalone privacy-filtered response for each mutually blind reviewer; when a reviewer missed material already present in the manuscript, treat that as a clarity signal and revise the presentation instead of replying that the point was already stated; draft a revision cover letter when required; map every claimed change to a manuscript location or explicit placeholder; mark changed manuscript text in red on a backed-up copy when editing; format quoted revised manuscript text in the response letter in italics; flag missing author input; run QA; and derive package readiness from the per-item statuses and blocking state.

Whenever a response proposes or performs a manuscript main-text edit, also load ../nature-shared/core/main-text-discipline.md. Answer the reviewer completely in the letter, but keep the manuscript change to the shortest text needed for the reader. Prefer replacement or compression over appending, and route non-central robustness or reconciliation detail to SI unless it changes the central interpretation.

Never invent experiments, citations, line numbers, figure panels, supplementary items, editor instructions, or manuscript changes. Mark anything the author must supply as AUTHOR_INPUT_NEEDED.

4. Reach for references only when needed

The files under references/ and templates/ are deep resources, not defaults. Open them on demand per the references.on_demand table in the manifest — for example references/comment-taxonomy.md to classify comments, references/action-mapping.md for tracker fields, references/tone-and-stance.md for disagreement wording, references/difficult-cases.md for impossible experiments / conflicting reviewers / appeal-like cases, references/chinese-author-alignment.md for Chinese author notes, references/latex-templates.md for .tex cover/response/redline outputs, ../nature-shared/core/main-text-discipline.md for reviewer-driven manuscript additions and evidence relocation, references/package-consistency-audit.md whenever the manuscript is edited alongside the letter or the package is about to be compiled and delivered, and references/qa-checklist.md before finalizing.

qa-checklist.md and package-consistency-audit.md are complementary and both apply to a final package: the first asks whether the response is complete, honest, and well-toned; the second asks whether the marked manuscript, the clean manuscript, and the letter actually agree with each other after editing. For a LaTeX package, run scripts/check_package_consistency.py after the first complete draft, after every manuscript edit, and immediately before delivery. Any manuscript edit invalidates the letter's verbatim quotes and page references, so re-run the audit rather than treating it as a one-time final check.

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 Response AI skill do?

Draft, audit, or revise responses to peer review, revision cover letters, and marked-manuscript or LaTeX revision packages. Use for 审稿意见回复、逐点回复、返修信、 rebuttals and edits to existing response drafts. Initial-submission materials belong to nature-writing; simulated peer review belongs to nature-reviewer.

Why use Nature Response on TypingMind?

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

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

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 Response?

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

Is the Nature Response 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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