nature-writing
この Skill は、Nature スタイルの科学論文の草稿作成や再構築のために設計されています。著者提供の主張、結果、図、メモ、または中国語の草稿に基づいて、要約、序論、方法、考察、結論などのセクションを生成または最適化できます。厳密な議論構造とセクションの草稿作成に焦点を当て、研究者が研究成果をジャーナル要件に合致する学術論文に効率的に変換するのを支援します。
npx skills add https://github.com/yuan1z0825/nature-skills --skill nature-writingBefore / After 効果比較
1 组ユーザーが Nature スタイルの科学論文を手動で執筆または再構築する際、時間のかかる作業、構造的な不整合、ジャーナルガイドラインの正確な把握の難しさといった課題に直面します。生データやメモから厳密な議論の連鎖を構築し、各セクションが高い基準を満たすようにするには、多大な労力と時間が必要です。
この Skill は、入力に基づいて Nature スタイルの科学論文のセクションを効率的に生成または最適化し、厳密な議論構造とジャーナル要件への準拠を保証します。これにより、研究者が研究成果を高品質で出版可能な論文に変換するために必要な時間と労力が大幅に削減され、執筆効率と品質が向上します。
Nature-Style Scientific Writing — Router
This skill is split into two layers:
- A static layer under
static/that holds versioned, reusable content fragments (core stance + workflow, paper-type playbooks, per-section drafting 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 drafting 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, 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:
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 / nat-comms / generic. Default: generic. If the user names a Nature subjournal, treat it asnature.
State the detected axis values in one short line to the user before drafting, 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 when the user has explicitly asked 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:
- Core stance + intake (
core/stance.md) — surface missing claim / evidence / boundary before drafting. - Paper-type playbook — argument chain, drafting order.
- Section-specific drafting rules and structure.
- Journal-specific framing and constraints.
- Language-specific sentence and paragraph rules (apply last).
Run the 8-step workflow in core/workflow.md end-to-end. Do not skip steps 1-3 (planning) just because the user asked for prose immediately — write the one-sentence argument first.
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
Natureabstract opening or asks about asummary paragraph→references/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.
Why this split
- The static layer is versioned and reviewable. Adding a new journal style, paper type, or section 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 multi-thousand-line reference set.
- The router itself is short on purpose. Update fragments, not this file, when adding scope.
- This structure mirrors
nature-polishingso shared content can later be lifted into a_shared/layer used by both skills.
nature-writing skill
A Nature-style manuscript writing skill for drafting or rebuilding sections from author-provided claims, figures, results, notes, or Chinese drafts.
What it does
nature-writing helps write:
- titles
- abstracts
- introductions
- results narratives
- discussions
- conclusions
- significance paragraphs
- manuscript outlines
It is for argument construction and section drafting. For sentence-level polish
of an existing draft, use nature-polishing.
Built from
Close reading of curated Nature and Nature Communications research articles across materials, energy systems, construction decarbonization and machine learning, combined with the existing writing-strategy rules in this repository.
Section-level writing and reviewer-facing self-review guidance is also adapted from Prof. Peng Sida's open research-writing notes:
- https://pengsida.notion.site/c1a22465a0fa4b15a12985223916048e
- https://github.com/pengsida/learning_research
File structure
nature-writing/
├── README.md
├── SKILL.md
└── references/
├── abstract.md
├── article-architecture.md
├── chinese-author-workflow.md
├── conclusion.md
├── experiments.md
├── introduction.md
├── method.md
├── nature-summary-paragraph.md
├── paper-review.md
├── paragraph-flow.md
├── related-work.md
└── examples/
Key rules
| Domain | Core rule |
|---|---|
| Evidence first | Do not invent data, mechanisms, statistics, sample sizes or novelty |
| Abstract | Context, gap, approach, key result, implication, boundary |
| Introduction | Field scale, bottleneck, prior attempts, unresolved gap, present study; for Nature, use a staged summary-paragraph funnel |
| Method | Explain module motivation, design, forward process, and technical advantage |
| Results | Build an evidence ladder, not a chronological lab diary |
| Experiments | Tie every major claim to comparison, ablation, metric, or stress-test evidence |
| Discussion | Explain meaning, prior-work relation, constraints and future use |
| Review | Run adversarial self-review before submission |
| Chinese notes | Translate intent and argument, not clause order |
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