From daymade-docs
Converts DOCX/PDF/PPTX to high-quality Markdown using Pandoc with automatic post-processing fixes for tables, images, CJK bold spacing, and code blocks.
How this skill is triggered — by the user, by Claude, or both
Slash command
/daymade-docs:doc-to-markdownThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Convert documents to high-quality markdown with intelligent multi-tool orchestration and automatic DOCX post-processing.
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).
# 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
| Mode | Speed | Quality | Use Case |
|---|---|---|---|
| Quick (default) | Fast | Good | Drafts, simple documents |
| Heavy | Slower | Best | Final documents, complex layouts |
| Format | Quick Mode | Heavy Mode |
|---|---|---|
| pymupdf4llm | pymupdf4llm + markitdown | |
| DOCX | pandoc + post-processing | pandoc + markitdown |
| PPTX | markitdown | markitdown + pandoc |
| XLSX | markitdown | markitdown |
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 |
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 runs multiple tools in parallel and selects the best segments:
| 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 |
# 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:
extracted-images/img_page1_1.png, extracted-images/img_page2_1.jpgextracted-images/images_metadata.json (page, position, dimensions)# 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
| Metric | Pass | Warn | Fail |
|---|---|---|---|
| Text Retention | >95% | 85-95% | <85% |
| Table Retention | 100% | 90-99% | <90% |
| Image Retention | 100% | 80-99% | <80% |
# 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
# 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
"No conversion tools available"
# Install all tools
pip install pymupdf4llm
uv tool install "markitdown[pdf]"
brew install pandoc
FontBBox warnings during PDF conversion
Images missing from output
scripts/extract_pdf_images.pyTables broken in output
scripts/validate_output.py| 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/benchmark-2026-03-22.md - 5-tool benchmark (Docling/MarkItDown/Pandoc/Mammoth/ours)references/heavy-mode-guide.md - Detailed Heavy Mode documentationreferences/tool-comparison.md - Tool capabilities comparisonreferences/conversion-examples.md - Batch operation examplesAfter 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
npx claudepluginhub p/daymade-daymade-docs-daymade-docsConverts PDFs, DOCX, PPTX, XLSX, HTML, images, URLs, CSV, JSON, and more to markdown via tiered fallbacks: MCP markitdown, native tools, or user notice. For ingesting non-plain-text files.
Converts PDF, Word, PPTX, PPT, and TXT documents to Markdown, preserving titles, lists, tables, structure, and PPT slide sections. Adds frontmatter, annotates images, suggests output path for archiving or import.
Converts files and URLs to clean Markdown using MarkItDown. Supports PDF, DOCX, XLSX, PPTX, HTML, images (OCR), audio, CSV, and YouTube transcripts. Optimized for LLM ingestion pipelines.