scholarly-publishing

End-to-end scholarly publishing workflow: manuscript → figures → LaTeX/Word → submission → revision/rebuttal → camera-ready. Includes meta-rules, checklists, repo structure, and case-based guidance.

Best use case

scholarly-publishing is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

End-to-end scholarly publishing workflow: manuscript → figures → LaTeX/Word → submission → revision/rebuttal → camera-ready. Includes meta-rules, checklists, repo structure, and case-based guidance.

Teams using scholarly-publishing should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/scholarly-publishing/SKILL.md --create-dirs "https://raw.githubusercontent.com/foryourhealth111-pixel/Vibe-Skills/main/bundled/skills/scholarly-publishing/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/scholarly-publishing/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How scholarly-publishing Compares

Feature / Agentscholarly-publishingStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

End-to-end scholarly publishing workflow: manuscript → figures → LaTeX/Word → submission → revision/rebuttal → camera-ready. Includes meta-rules, checklists, repo structure, and case-based guidance.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# Scholarly Publishing (论文投稿全流程)

## 你会得到什么(输出契约)

当用户说“我要投稿/返修/顶刊作图/相机就绪/需要 LaTeX 工程化/写 rebuttal/写 cover letter/做组会汇报”时,本 skill 负责把目标拆成**可交付的出版资产包**:

- `manuscript/`:论文源文件(LaTeX / Word / Markdown 任一作为 *source-of-truth*)
- `figures/`:每张图的源代码/源数据/最终导出(PDF/EPS/SVG/TIFF)
- `supplement/`:补充材料(方法细节、附录、扩展实验、额外图表)
- `submission/`:投稿所需文件(cover letter、graphical abstract、highlights、checklist、打包 zip)
- `revision/`:返修资产(rebuttal、diff、逐条回应矩阵)
- `build/`:可复现构建产物(PDF、打包 zip、CI 日志)

> 目标不是“写一段文字/画一张图”,而是产出**能提交、能返修、能复用、能审计**的一套文件与规范。

---

## 何时使用(触发场景)

适用场景(中英混合均可):
- 投稿/返修:`投稿`、`submission`、`返修`、`revision`、`rebuttal`、`回复审稿意见`、`camera-ready`、`proof`
- 顶刊作图:`顶刊作图`、`投稿图`、`publication-quality`、`600dpi`、`tiff`、`多子图`、`panel`、`subplot`
- LaTeX 工程化:`latex template`、`latexmk`、`bibtex`、`biber`、`Overleaf`、`chktex`、`latexindent`
- 研究报告/技术报告:`科研报告`、`technical report`、`HTML + PDF`、`Quarto`
- 组会/答辩/汇报:`slides`、`Slidev`、`Marp`、`Reveal.js`、`Beamer`

不适用(应交给其它技能):
- 仅“从 PDF 提取文本/合并 PDF/批注回复”等纯文档处理 → `docs-media/pdf/docx/docx-comment-reply`
- 仅“画流程图/概念示意图(非数据图)” → `scientific-schematics` 或 `markdown-mermaid-writing`

---

## 输入信息(最小问询)

为了稳定落地,至少需要:
1) **目标投向**:期刊/会议/出版社(不知道也可以先用“类目”:Nature/IEEE/ACM/NeurIPS/PLOS)
2) **论文类型**:研究论文/方法论文/综述/短文/技术报告
3) **交付物**:只要“投稿包”?还是“报告 + 图 + slides”?(可多选)
4) **写作来源**:是否已有草稿/数据/图?(已有就以“改稿/补齐规范”为主)

---

## 工作流(可执行流程)

### Phase 0 — 选择“单一事实源”(Single Source of Truth)

在以下三者中选一个做源文件(强烈建议只选一个):
- **LaTeX**:适合期刊/会议、公式多、需要严格排版、可 CI 构建
- **Word**:适合部分医学生命科学期刊/协作者偏 Word 的团队
- **Markdown/Quarto**:适合技术报告/内部报告/可发布网页(可导出 PDF)

> 元规则:同一论文不要在多个格式里并行编辑。其它格式只能是“导出物”。

### Phase 1 — 先建“投稿约束”再写正文

1) 目标投向与模板:用 `venue-templates` 获取模板/版式约束  
2) 投稿清单:用 `submission-checklist` 拉一份对应 stage 的 checklist  
3) 明确图的规格:列出每张图的用途、类型(line art / raster / combination)与导出格式(PDF/TIFF)  

### Phase 2 — 论文主线(写作)

用 `scientific-writing` 执行两段式写作:
- 先写“结构大纲(允许 bullet)”
- 再写“最终正文(必须段落,禁止 bullet)”

元规则(顶级期刊通用):
- **先图后文**:Results 的主线由 Figures 驱动
- **句子不超载**:每句一个主张;每段一个中心句;每节一个问题
- **让审稿人省力**:方法可复现、统计可追溯、图注自解释

### Phase 3 — 图表管线(顶刊作图)

用 `scientific-visualization` 作为默认图表技能,必要时补 `scientific-schematics`(流程/机制示意):
- 对 data figure:统一字体、字号、线宽、配色、子图间距、panel label(A/B/C)
- 导出:优先 `PDF/EPS/SVG`(矢量),必要时 `TIFF 600dpi`(栅格)
- 可访问性:色盲友好(Okabe-Ito / colorcet / cmcrameri)

### Phase 4 — LaTeX/构建/打包

如果 source-of-truth 是 LaTeX:
- 用 `latex-submission-pipeline` 完成:本地编译 → lint/format → CI 编译 → submission zip

如果 source-of-truth 是 Word:
- 仍然遵循“图表输出标准 + 引用一致性 + 文件命名规范”,并准备投稿系统所需附件

### Phase 5 — 投稿与返修

- 投稿前:`submission-checklist/templates/pre-submission-checklist.md`
- 返修:用 `submission-checklist/templates/rebuttal-response-matrix.md` 逐条回应
- 相机就绪:`submission-checklist/templates/camera-ready-checklist.md`

### Phase 6 — 汇报与传播(可选)

用 `slides-as-code` 或 `scientific-slides` 把论文变成可讲的故事:
- 1 张图 = 1 个结论点
- Slides 的图直接复用 `figures/` 的最终导出,不要二次截图

---

## 规范(Meta Rules → Skills)

### A. 资产命名规范

- 图文件:`fig-01-overview.pdf`、`fig-02-results.tiff`
- 子图:`fig-02A-...`、`fig-02B-...`
- 统一用 `kebab-case`;避免空格与中文;避免“final_v7_reallyfinal”

### B. 可复现性最小集(Reproducibility Minimum)

至少提供:
- 构建命令(`make pdf` / `latexmk` / `quarto render`)
- 环境说明(Python 版本、依赖,或 lockfile)
- 图表源(代码/参数)与导出脚本(自动化优先)

### C. 质量门禁(提交前自检)

- 字体一致、字号可读、线宽统一
- 统计图包含不确定性(CI/SEM/SD)并在 caption 解释
- 图注自包含:看 caption 能理解图表达什么、样本量、统计检验
- 引用准确:每个关键主张可追溯到数据或引用

---

## 案例库(GitHub 高信号仓库)

见:`references/case-library.md`(按“写作清单/论文工程化/顶刊作图/LaTeX pipeline/Slides-as-code”分类)

---

## 快速调用示例(给 VCO 路由用)

- “我要投稿 Nature 风格论文:请给出投稿资产包目录结构 + checklist + 图表导出标准”
- “顶刊作图:matplotlib 多子图 + 色盲友好 + 导出 PDF + TIFF 600dpi(每张图的规范写清楚)”
- “我要回复审稿意见:请生成 rebuttal 矩阵,并给出逐条回应的写作规范”
- “请把当前 LaTeX 项目接入 GitHub Actions 自动编译并生成 submission zip”

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