yuan1z0825/nature-skills

nature-polishing

Polish, restructure, or translate academic prose into concise Nature-leaning English while preserving facts, evidence boundaries, terminology, and citation intent.

ソースを見る
リポジトリの原文

見出し、例、コード、表、リンク、参照画像を含む原文を表示しています。

Nature-Style Academic Polishing — Router

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 / nat-mach-intell / generic. Default:

generic. Use nature only for flagship Nature, nat-comms for Nature Communications and nat-mach-intell for Nature Machine Intelligence (NMI). Do not route another Nature Portfolio title through flagship Nature rules.

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:

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

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

For Results, full-main-text compression, main-versus-SI allocation, or prose added during revision, load ../nature-shared/core/main-text-discipline.md before sentence polishing. Classify each result, retain the shortest sufficient evidence chain, and require every addition to trigger a deletion or replacement check across the affected paragraph.

For flagship Nature, Nature Communications, Nature Machine Intelligence, or another Nature Portfolio title, load the matching shared Nature-style corpus guidance:

  • 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

Preserve claim escalation, the fast question funnel, Introduction–Results alignment, discovery-centred abstract compression, evidence-bound local interpretation, and cross-Results synthesis. These defaults were initially distilled from published NMI papers; treat them as corpus-derived guidance, not official policy, and obey the target journal's current rules when they differ.

For any Discussion polish or restructuring job, also load ../nature-shared/core/discussion-argument-language.md. Use its function labels to remove Results replay, repair the movement from specific findings to bounded implications, calibrate modal and reporting verbs to evidence strength, and make limitations and future work resolve named claim boundaries. Treat it as general writing guidance, not journal policy.

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.

When the target is Nature Machine Intelligence and exact limits, availability sections, conference-extension disclosure or production checks affect the revision, load ../nature-shared/journal-formats/nature-machine-intelligence.md.

When the job is a whole manuscript rather than a passage, or the text has already been through more than one round of editing, also load ../nature-shared/core/consistency-sweep.md. Polishing passage by passage cannot see accumulated drift: one experimental factor under several names, the same quantity in two units, a metric at two precisions, or a superlative the paper's own table contradicts. Sweep for those before working on sentences, and repeat the sweep until a pass finds nothing new.

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.
同じリポジトリから

関連する Skills

すべての Skills
yuan1z0825
コミュニティ

nature-paper-card

Build a source-grounded deep-reading Paper Card for one scientific paper, preprint, PDF, DOI, arXiv page, publisher article, or pasted paper text. Use when the user asks for a Paper Card, deep-reading literature card, single-paper deep analysis, module-by-module analysis, experiment-to-claim evidence chain, conclusion-boundary audit, critical analysis, knowledge connections, or candidate research ideas. Produce the fixed Sections 01-16 covering bibliographic position, research question, background route, pain point, core insight, method and module logic, essential formulas, experiment-to-claim evidence, conclusion boundaries, author-stated limitations, critical analysis, learned knowledge, knowledge connections, and testable research ideas. Do not use for full-paper bilingual translation, formal peer-review reports, batch literature monitoring, academic-English collection, comprehension quizzes, or public-article writing.

導入数
9
GitHub Stars
4万
更新日
9月7日
yuan1z0825
コミュニティ

nature-statistics

- Audit, revise, or draft manuscript statistical reporting for Nature / high-impact journal submissions. Use when the user asks to check statistical analysis sections, p values, confidence intervals, sample size, biological versus technical replicates, randomization, blinding, multiple-comparison correction, model assumptions, figure legends, Results statistics wording, reviewer comments about statistics, or Chinese academic drafts needing publication-ready Statistical analysis text. Also trigger on general paper-statistics requests such as 统计审查、统计分析小节、统计方法、p值、样本量、重复数、多重比较、置信区间、效应量、图注统计、审稿人统计意见.

導入数
9
GitHub Stars
4万
更新日
9月7日
yuan1z0825
コミュニティ

nature-ref-verifier

- 对学术文献逐条执行多源交叉验证,逐字段对比作者、标题、年份、卷期、页码, 标记卷年/DOI年冲突、作者顺序异常、页码偏差等问题,输出结构化验证报告。 可批量处理整篇论文/开题报告的参考文献列表,也可单条校验,支持与 Zotero 同步修正。

導入数
13
GitHub Stars
3.8万
更新日
9月1日
yuan1z0825
コミュニティ

nature-citation

- Add strict Nature/CNS citations to manuscript text by splitting long passages into citable segments, searching only accepted flagship and subjournal titles from Nature Portfolio, the AAAS Science family, and Cell Press, filtering by publication time range, and exporting one reference-manager-ready output by default. Use this skill whenever the user asks to input text and automatically get references, add citations to a paragraph/manuscript, find Nature-series or CNS support for statements, create text-to-reference correspondence, "分段引用", "自动给出引用", "Nature系列引用", "CNS及子刊", "支撑文献", "补引用", "找引用", or export EndNote/RIS/ENW/Zotero RDF. Also trigger on general academic-writing citation needs even without the word "Nature", such as adding references while writing a paper, finding sources/literature for a claim, building a reference list, citation/referencing for academic writing, and Chinese phrasings like 学术写作引用、写论文加引用、写paper找文献、加参考文献、配文献、引用文献、文献支撑.

導入数
12
GitHub Stars
3.8万
更新日
9月1日