Nature Polishing

Organization

ai4protein

nature_polishing

Polish, restructure, or translate academic prose into Nature-leaning English using writing-strategy principles, curated Nature/Nature Communications article patterns, and phrase-level support from Academic Phrasebank. Use whenever the user asks to polish a manuscript paragraph, abstract, introduction, results, discussion, conclusion, title, methods section, or Chinese academic draft for publication-quality English. Also covers LaTeX layout/typesetting (排版) fixes — loose or sparse pages, stranded section headings, figures that don't fill the page or split across pages, "Float too large", multi-panel figure arrangement, and Supplementary Information that looks empty — via references/latex-layout.md. Also trigger on general academic/scientific writing requests even without the word "Nature", including academic writing, scientific writing, SCI/paper writing, English manuscript polishing, language editing, proofreading, and Chinese phrasings such as 学术写作、科研写作、论文润色、写paper、SCI写作、英文论文润色、语言润色、润色、改写、学术英语、英文写作.

Overview

Publisher	ai4protein
Repository	`VenusFactory2`
Skill name	`nature_polishing`
Stars	255
Forks	37
Bundled files	27
License	Unknown
Links	Skill source Repository Homepage

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.
27 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by ai4protein on GitHub. Read the source before you install it.

Installation

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

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/ai4protein/VenusFactory2/tree/main/src/agent/skills/nature_polishing
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.

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/ai4protein/VenusFactory2.git /tmp/VenusFactory2
mkdir -p .claude/skills
cp -r /tmp/VenusFactory2/src/agent/skills/nature_polishing .claude/skills/nature_polishing

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Nature Polishing 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 Polishing 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 Polishing 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 Academic Polishing — Router

VenusFactory execution

skill_id: nature_polishing. Load manifest.yaml and static/ fragments via read_skill(..., relative_path=...).
For drafting new sections from claims/results, prefer nature_writing.

This skill is split into two layers:

A static layer under static/ that holds versioned, reusable content fragments (core principles, paper-type playbooks, per-section guidance, language-specific rules, per-journal style).
A dynamic layer (this file plus manifest.yaml) that detects the request's axes and loads only the fragments needed for the current job.

Do not try to apply the polishing logic from memory or from this router. Always load fragments from disk as described below.

Routing protocol

Follow these five steps every time the skill is invoked.

1. Load the manifest and the core layer

Read manifest.yaml. It declares the axes (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, failure-mode diagnosis, ethics, and output format that apply to every polish 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:

paper_type — research / methods / hypothesis / algorithmic / review. Default: research.
section — abstract / intro / results / discussion / conclusion / title / methods. May be multiple. Ask the user if it is ambiguous and matters for the polish.
language — en or zh-to-en. Detect from the draft itself.
journal — nature / nat-comms / generic. Default: generic. If the user names a Nature subjournal, treat it as nature.

State the detected axis values in one short line to the user before proceeding, so they can correct you cheaply.

3. Load the matching fragments

For each axis value, Read the file mapped in the manifest. Skip the section axis only if the user has supplied free-floating prose with no section context.

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

4. Polish using the loaded material

Apply the loaded fragments in this priority order, matching the paper type -> section job -> paragraph logic -> claim/evidence/boundary -> sentence polish rule from core/failure-modes.md:

Paper-type playbook (architecture, writing order).
Section-specific job and failure modes.
Journal-specific framing and constraints.
Language-specific sentence and paragraph rules (apply last).
Core stance and ethics throughout.

If a paragraph's structural problem cannot be fixed without inventing content, flag it instead of papering over it.

5. 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 when the user explicitly asks for phrasebank-style alternatives or a stricter style audit.

Layout/typesetting (排版) requests are different. If the user asks to fix placement rather than wording — loose/sparse pages, stranded headings, figures that don't fill the page or split across pages, "Float too large", multi-panel arrangement, sparse Supplementary Information — skip the prose axes (paper_type, section, language, journal) and load references/latex-layout.md directly. That file is self-contained: it carries the diagnosis workflow (render → contact-sheet → read the log), the float-glue and [H]/\clearpage/placeins patterns, and the "regenerate wide figures taller at the source" rule. Always compile and visually inspect rendered pages before and after — never judge layout from the .tex alone.

Why this split

The static layer is versioned and reviewable. Adding a new journal style or paper type is one new file plus one manifest line.
The dynamic layer keeps each invocation cheap: only the fragments relevant to this draft enter context, instead of the full 1000-line monolith.
The router itself is short on purpose. Update fragments, not this file, when adding scope.

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 Polishing AI skill do?

Why use Nature Polishing on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ai4protein/VenusFactory2/tree/main/src/agent/skills/nature_polishing. 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 Polishing?

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 Polishing?

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

Is the Nature Polishing AI skill free?

Yes. It is published on GitHub by ai4protein under the Unknown 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Nature-Style Academic Polishing — Router

VenusFactory execution

Routing protocol

1. Load the manifest and the core layer

2. Detect the axis values for this request

3. Load the matching fragments

4. Polish using the loaded material

5. Reach for references only when needed

Why this split

Bundled files

Frequently asked questions

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