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nature-writing

by @yuan1z0825v
4.5(120)

此技能专为撰写或重构 Nature 风格的科学手稿而设计,能根据作者提供的论点、结果、图表、笔记或中文草稿,生成或优化摘要、引言、方法、讨论、结论等章节。它专注于构建严谨的论证结构和章节草稿,帮助科研人员高效地将研究成果转化为符合期刊要求的学术论文。

scientific-writingacademic-researchmanuscript-draftingnature-styleresearch-paperGitHub
安装方式
npx skills add https://github.com/yuan1z0825/nature-skills --skill nature-writing
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Before / After 效果对比

1 组
使用前

用户在手动撰写或重构 Nature 风格的科学手稿时,常面临耗时、结构不一致和难以准确把握期刊规范的痛点。从原始数据和笔记中构建逻辑严谨的论证链条,并确保各章节符合高标准,需要投入大量精力与时间。

使用后

此技能能高效地根据输入生成或优化 Nature 风格的科学手稿章节,确保论证结构严谨且符合期刊要求。它显著减少了科研人员将研究成果转化为高质量、可发表论文所需的时间和精力,提升了写作效率和质量。

SKILL.md

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 as nature.

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:

  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. Journal-specific framing and constraints.
  5. 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 Nature abstract opening or asks about a summary 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-polishing so 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:

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

DomainCore rule
Evidence firstDo not invent data, mechanisms, statistics, sample sizes or novelty
AbstractContext, gap, approach, key result, implication, boundary
IntroductionField scale, bottleneck, prior attempts, unresolved gap, present study; for Nature, use a staged summary-paragraph funnel
MethodExplain module motivation, design, forward process, and technical advantage
ResultsBuild an evidence ladder, not a chronological lab diary
ExperimentsTie every major claim to comparison, ablation, metric, or stress-test evidence
DiscussionExplain meaning, prior-work relation, constraints and future use
ReviewRun adversarial self-review before submission
Chinese notesTranslate intent and argument, not clause order

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统计数据

安装量15.4K
评分4.5 / 5.0
版本
更新日期2026年10月23日
对比案例1 组

用户评分

4.5(120)
5
37%
4
43%
3
13%
2
5%
1
2%

为此 Skill 评分

0.0

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时间线

创建2026年6月13日
最后更新2026年10月23日
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