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doc-to-markdown（DOC TO Markdown 控制）

Agent Skill

用于辅助文档、README、Markdown、说明文和内容稿件的整理与改写。它适合让 Agent 提炼结构、补齐章节、统一术语、检查链接或把零散材料整理成可读文档。使用时应保留项目已有事实、命令和路径，不要把未确认的信息写成确定结论；涉及对外文案时，还需要控制语气，避免过度营销或夸大能力。

总安装

3,216

周安装

134

GitHub Stars

956

下载量

1,072

CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息，不托管安装包，也不代用户安装。

GitHub

来源数

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手，先确认命令和权限，再让它执行。

请帮我安装这个 Agent Skill：doc-to-markdown（DOC TO Markdown 控制）
来源仓库：https://github.com/daymade/claude-code-skills
仓库路径：skills/doc-to-markdown
安装命令：
npx skills add https://github.com/daymade/claude-code-skills --skill doc-to-markdown
安装前请先检查当前环境是否支持对应 CLI，并向我确认将要执行的命令、安装目录、联网范围和文件读写权限；确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill；本站只展示命令，不托管安装包，也不自动执行。

skills.shnpx skills

npx skills add https://github.com/daymade/claude-code-skills --skill doc-to-markdown

简介

doc-to-markdown 实现 DOCX/PDF 到高质量 Markdown 的智能转换。

组合 Pandoc 提取引擎与八步后处理修正，解决表格、图片错位等问题。
支持 PDF 直转为 Markdown，保留目录结构与样式语义。
提供 Dual Mode 模式平衡速度（快速预览）与质量（精细还原）需求。
转换后建议人工复核数学公式与复杂布局，确保关键信息无损迁移。

SKILL.md

Doc to Markdown

Convert documents to high-quality markdown with intelligent multi-tool orchestration and automatic DOCX post-processing.

Architecture: Pandoc (best-in-class extraction) + 8 post-processing fixes (our value-add).

Quick Start

# DOCX → Markdown (one command, zero manual fixes)
uv run --with pymupdf4llm --with markitdown scripts/convert.py document.docx -o output.md --assets-dir ./media

# PDF → Markdown
uv run --with pymupdf4llm --with markitdown scripts/convert.py document.pdf -o output.md

# Run tests
uv run --with pytest pytest scripts/test_convert.py -v

Dual Mode

Mode	Speed	Quality	Use Case
Quick (default)	Fast	Good	Drafts, simple documents
Heavy	Slower	Best	Final documents, complex layouts

Tool Selection

Format	Quick Mode	Heavy Mode
PDF	pymupdf4llm	pymupdf4llm + markitdown
DOCX	pandoc + post-processing	pandoc + markitdown
PPTX	markitdown	markitdown + pandoc
XLSX	markitdown	markitdown

DOCX Post-Processing (automatic)

When converting DOCX via pandoc, 8 cleanups are applied automatically:

Problem	Fix	Test coverage
Grid tables (`+:---+`)	Single-column → blockquote, multi-column → pipe table	`TestPostprocessPipeline`
Simple tables (`---- ----`)	Multi-column images → pipe table with captions	`TestSimpleTable`
Image path nesting (`media/media/`)	Flatten to `media/`, absolute → relative	`test_stats_tracking`
Pandoc attributes (`{width="..."}`)	Removed	`test_pandoc_attributes_removed`
CJK bold spacing (`粗体中文`)	Add space around `**` for CJK bold spans	`TestCjkBoldSpacing` (15 cases)
Indented dashed code blocks	→ fenced ``` with language detection	`test_code_block_with_language`
Escaped brackets (`\[...\]`)	→ `[...]`	`test_escaped_brackets_fixed`
Double-bracket links (`[[text]](url)`)	→ `[text](url)`	`test_double_bracket_links_fixed`

CJK Bold Spacing — why and how

DOCX uses run-level styling (no spaces between bold/normal runs in CJK text). Markdown renderers need whitespace around ** to recognize bold boundaries.

Rule: if a **content** span contains any CJK character, ensure both sides have a space — unless already spaced or at line boundary. This handles CJK punctuation, emoji adjacency, and mixed content.

Before: 打开**飞书**，就可以    → some renderers fail to bold
After:  打开 **飞书** ，就可以  → universally renders correctly

Heavy Mode Workflow

Heavy Mode runs multiple tools in parallel and selects the best segments:

Parallel Execution: Run all applicable tools simultaneously
Segment Analysis: Parse each output into segments (tables, headings, images, paragraphs)
Quality Scoring: Score each segment based on completeness and structure
Intelligent Merge: Select best version of each segment across tools

Merge Criteria

Segment Type	Selection Criteria
Tables	More rows/columns, proper header separator
Images	Alt text present, local paths preferred
Headings	Proper hierarchy, appropriate length
Lists	More items, nested structure preserved
Paragraphs	Content completeness

Image Extraction

# Extract images with metadata
uv run --with pymupdf scripts/extract_pdf_images.py document.pdf -o ./extracted-images

# Generate markdown references file
uv run --with pymupdf scripts/extract_pdf_images.py document.pdf --markdown refs.md

Output:

Images: extracted-images/img_page1_1.png, extracted-images/img_page2_1.jpg
Metadata: extracted-images/images_metadata.json (page, position, dimensions)

Quality Validation

# Validate conversion quality
uv run --with pymupdf scripts/validate_output.py document.pdf output.md

# Generate HTML report
uv run --with pymupdf scripts/validate_output.py document.pdf output.md --report report.html

Quality Metrics

Metric	Pass	Warn	Fail
Text Retention	>95%	85-95%	<85%
Table Retention	100%	90-99%	<90%
Image Retention	100%	80-99%	<80%

Merge Outputs Manually

# Merge multiple markdown files
python scripts/merge_outputs.py output1.md output2.md -o merged.md

# Show segment attribution
python scripts/merge_outputs.py output1.md output2.md -o merged.md --verbose

Path Conversion (Windows/WSL)

# Windows to WSL conversion
python scripts/convert_path.py "C:\Users\<windows-user>\Documents\file.pdf"
# Output: /mnt/c/Users/<windows-user>/Documents/file.pdf

Common Issues

"No conversion tools available"

# Install all tools
pip install pymupdf4llm
uv tool install "markitdown[pdf]"
brew install pandoc

FontBBox warnings during PDF conversion

Harmless font parsing warnings, output is still correct

Images missing from output

Use Heavy Mode for better image preservation
Or extract separately with scripts/extract_pdf_images.py

Tables broken in output

Use Heavy Mode - it selects the most complete table version
Or validate with scripts/validate_output.py

Bundled Scripts

Script	Purpose
`convert.py`	Main orchestrator with Quick/Heavy mode + DOCX post-processing
`test_convert.py`	31 tests covering all post-processing functions
`merge_outputs.py`	Merge multiple markdown outputs
`validate_output.py`	Quality validation with HTML report
`extract_pdf_images.py`	PDF image extraction with metadata
`convert_path.py`	Windows to WSL path converter

References

references/benchmark-2026-03-22.md - 5-tool benchmark (Docling/MarkItDown/Pandoc/Mammoth/ours)
references/heavy-mode-guide.md - Detailed Heavy Mode documentation
references/tool-comparison.md - Tool capabilities comparison
references/conversion-examples.md - Batch operation examples

Next Step: Clean Up Converted Content

After converting documents to markdown, suggest cleanup:

Conversion complete: [N] files converted to markdown.

Options:
A) Clean up docs — run /docs-cleaner to consolidate redundant content (Recommended if multiple files)
B) Check facts — run /fact-checker to verify claims in the converted content
C) No thanks — the markdown conversion is sufficient

适合场景

用户想查找某类 Agent Skill 时

需要根据任务场景推荐可安装能力包时

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明，方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用；具体调用方式请以来源页面和 README 为准。

平台分布

Codex

35.85%

按下载量换算384

Claude

30.77%

按下载量换算330

Cursor

16.64%

按下载量换算178

Gemini CLI

9.96%

按下载量换算107

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容，本身偏只读；真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息，不托管安装包，不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源，正式发布前建议补源仓库或其他目录站核验。

来源信息

GitHub 仓库

github.com

/daymade/claude-code-skills

SKILL.md 来源

github.com

/daymade/claude-code-skills/tree/main/skills/doc-to-markdown

skills-sh

skills.sh

/daymade/claude-code-skills/doc-to-markdown

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