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Doc To Markdown

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daymade
doc-to-markdown

Converts DOCX/PDF/PPTX to high-quality Markdown with automatic post-processing. Fixes pandoc grid tables, simple tables, image paths, CJK bold spacing, attribute noise, and code blocks; for PDFs also strips OCR garbage blocks, repeated headers/footers/watermarks, and absolute image paths from pymupdf4llm output. Benchmarked best-in-class (7.6/10) against Docling, MarkItDown, Pandoc raw, and Mammoth. Trigger on "convert document", "docx to markdown", "parse word", "doc to markdown", "解析word", "转换文档".

Overview

Publisherdaymade
Repositoryclaude-code-skills
Skill namedoc-to-markdown
Stars
1.4K
Forks
219
Bundled files
10
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 10 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by daymade on GitHub. Read the source before you install it.

Installation

Install the Doc To Markdown AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/daymade/claude-code-skills.git /tmp/claude-code-skills
mkdir -p .claude/skills
cp -r /tmp/claude-code-skills/daymade-docs/doc-to-markdown .claude/skills/doc-to-markdown
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Doc To Markdown in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Doc To Markdown on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Doc To Markdown is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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

bash
# 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

ModeSpeedQualityUse Case
Quick (default)FastGoodDrafts, simple documents
HeavySlowerBestFinal documents, complex layouts

Tool Selection

FormatQuick ModeHeavy Mode
PDFpymupdf4llmpymupdf4llm + markitdown
DOCXpandoc + post-processingpandoc + markitdown
PPTXmarkitdownmarkitdown + pandoc
XLSXmarkitdownmarkitdown

DOCX Post-Processing (automatic)

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

ProblemFixTest coverage
Grid tables (+:---+)Single-column → blockquote, multi-column → pipe tableTestPostprocessPipeline
Simple tables ( ---- ----)Multi-column images → pipe table with captionsTestSimpleTable
Image path nesting (media/media/)Flatten to media/, absolute → relativetest_stats_tracking
Pandoc attributes ({width="..."})Removedtest_pandoc_attributes_removed
CJK bold spacing (**粗体**中文)Add space around ** for CJK bold spansTestCjkBoldSpacing (15 cases)
Indented dashed code blocks→ fenced ``` with language detectiontest_code_block_with_language
Escaped brackets (\[...\])[...]test_escaped_brackets_fixed
Double-bracket links ([[text]](url))[text](url)test_double_bracket_links_fixed

PDF Post-Processing (automatic, 2026-08-30 起)

When converting PDF via pymupdf4llm, 3 cleanups are applied automatically (skip with --no-postprocess):

ProblemFixTest coverage
Tesseract OCR garbage on image regions (<!-- Start of picture text -->...)Block removed; images themselves keptTestStripOcrPictureText
Repeated header/footer/watermark lines (same normalized line on ≥60% of pages, incl. diagonal watermarks)Detected via pymupdf cross-page scan, removed from markdown; bold-wrapped and merged-with-page-number variants also caughtTestRepeatingLines
Absolute image paths (![](/abs/tmp/assets/...))Rewritten relative to the output markdown file (portable output)TestImagePathsRelative

Heavy mode additionally prints a loud ⚠️ HEAVY MODE DEGRADED warning on stderr when one engine fails and the merge would otherwise silently degrade to single-engine output.

Known limits (learned from a 62-page Chinese research-report conversion, 2026-08-30):

  • pymupdf4llm may emit duplicated paragraphs (source text layer has only one copy) — not auto-fixed; spot-check.
  • Dotted TOC pages get detected as tables — rewrite the TOC manually if it matters.
  • Cross-page tables are NOT merged (each page's fragment keeps its own header row) — merge manually.
  • Table cells overlapped by diagonal watermarks can contain watermark character shards (dn, uFE, ...); the repeating-line stripper removes full lines only, not intra-cell shards. Watermark-heavy PDFs need cell-level rebuild (collect non-watermark spans per cell bbox).
  • Complex infographics (dense in-image text) come out as images only; transcribing in-image text needs a VLM pass, not this tool.

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:

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

Merge Criteria

Segment TypeSelection Criteria
TablesMore rows/columns, proper header separator
ImagesAlt text present, local paths preferred
HeadingsProper hierarchy, appropriate length
ListsMore items, nested structure preserved
ParagraphsContent completeness

Image Extraction

bash
# 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

bash
# 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

MetricPassWarnFail
Text Retention>95%85-95%<85%
Table Retention100%90-99%<90%
Image Retention100%80-99%<80%

Merge Outputs Manually

bash
# 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)

bash
# 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"

bash
# 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

ScriptPurpose
convert.pyMain orchestrator with Quick/Heavy mode + DOCX post-processing
test_convert.py31 tests covering all post-processing functions
merge_outputs.pyMerge multiple markdown outputs
validate_output.pyQuality validation with HTML report
extract_pdf_images.pyPDF image extraction with metadata
convert_path.pyWindows 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 /daymade-docs: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

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Doc To Markdown AI skill do?

Converts DOCX/PDF/PPTX to high-quality Markdown with automatic post-processing. Fixes pandoc grid tables, simple tables, image paths, CJK bold spacing, attribute noise, and code blocks; for PDFs also strips OCR garbage blocks, repeated headers/footers/watermarks, and absolute image paths from pymupdf4llm output. Benchmarked best-in-class (7.6/10) against Docling, MarkItDown, Pandoc raw, and Mammoth. Trigger on "convert document", "docx to markdown", "parse word", "doc to markdown", "解析word", "转换文档".

Why use Doc To Markdown on TypingMind?

Because you install it once and use it with any model. Doc To Markdown is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Doc To Markdown in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/daymade/claude-code-skills/tree/main/daymade-docs/doc-to-markdown. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Doc To Markdown?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Doc To Markdown?

As many as you like. As long as a model supports skills, you can use Doc To Markdown with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Doc To Markdown AI skill free?

Yes. It is published on GitHub by daymade under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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