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Supports PDF, DOCX, PPTX, XLSX, images (with OCR), audio (with transcription), HTML, CSV, JSON, XML, ZIP, YouTube URLs, EPubs and more.","fadrienne\u002FMyGitHub","fadrienne","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F197874571?v=4","01M6X64V8VWCRSMFZ4EPS2BWXG","Francesca Adrienne","01M6X64VD5MW4F9PHDWQKS7ZAF",0,"MIT","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fdata-engineering\u002Fmarkitdown\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fdata-engineering\u002Fmarkitdown\u002FSKILL.md",[19,24,27,30],{"categoryNo":20,"categoryName":21,"taxonomyType":22,"taxonomyTypeDesc":23},"01JBSEEDCAT000000000000002","文档处理","SCENARIO","使用场景",{"categoryNo":25,"categoryName":26,"taxonomyType":22,"taxonomyTypeDesc":23},"01JBSEEDCAT000000000000003","演示文稿",{"categoryNo":28,"categoryName":29,"taxonomyType":22,"taxonomyTypeDesc":23},"01JBSEEDCAT000000000000004","电子表格",{"categoryNo":31,"categoryName":32,"taxonomyType":33,"taxonomyTypeDesc":34},"01JBSEEDCAT000000000000034","行政与办公人员","PROFESSION","适用职业",[5,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54],"markdown","zip","more","youtube","files","images","html","edit","write","read","docx","Bash","Audio","ocr","data","documents","xlsx","pptx","PDF","2026-11-10 10:12:30",false,"---\nname: markitdown\ndescription: \"Convert files and office documents to Markdown. Supports PDF, DOCX, PPTX, XLSX, images (with OCR), audio (with transcription), HTML, CSV, JSON, XML, ZIP, YouTube URLs, EPubs and more.\"\ncategory: data-engineering\nallowed-tools: [Read, Write, Edit, Bash]\nlicense: MIT\nsource: https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fmarkitdown\n---\n\n# MarkItDown - File to Markdown Conversion\n\n## Overview\n\nMarkItDown is a Python tool developed by Microsoft for converting various file formats to Markdown. It's particularly useful for converting documents into LLM-friendly text format, as Markdown is token-efficient and well-understood by modern language models.\n\n**Key Benefits**:\n- Convert documents to clean, structured Markdown\n- Token-efficient format for LLM processing\n- Supports 15+ file formats\n- Optional AI-enhanced image descriptions\n- OCR for images and scanned documents\n- Speech transcription for audio files\n\n## Visual Enhancement with Scientific Schematics\n\n**When creating documents with this skill, always consider adding scientific diagrams and schematics to enhance visual communication.**\n\nIf your document does not already contain schematics or diagrams:\n- Use the **scientific-schematics** skill to generate AI-powered publication-quality diagrams\n- Simply describe your desired diagram in natural language\n- Nano Banana Pro will automatically generate, review, and refine the schematic\n\n**For new documents:** Scientific schematics should be generated by default to visually represent key concepts, workflows, architectures, or relationships described in the text.\n\n**How to generate schematics:**\n```bash\npython scripts\u002Fgenerate_schematic.py \"your diagram description\" -o figures\u002Foutput.png\n```\n\nThe AI will automatically:\n- Create publication-quality images with proper formatting\n- Review and refine through multiple iterations\n- Ensure accessibility (colorblind-friendly, high contrast)\n- Save outputs in the figures\u002F directory\n\n**When to add schematics:**\n- Document conversion workflow diagrams\n- File format architecture illustrations\n- OCR processing pipeline diagrams\n- Integration workflow visualizations\n- System architecture diagrams\n- Data flow diagrams\n- Any complex concept that benefits from visualization\n\nFor detailed guidance on creating schematics, refer to the scientific-schematics skill documentation.\n\n---\n\n## Supported Formats\n\n| Format | Description | Notes |\n|--------|-------------|-------|\n| **PDF** | Portable Document Format | Full text extraction |\n| **DOCX** | Microsoft Word | Tables, formatting preserved |\n| **PPTX** | PowerPoint | Slides with notes |\n| **XLSX** | Excel spreadsheets | Tables and data |\n| **Images** | JPEG, PNG, GIF, WebP | EXIF metadata + OCR |\n| **Audio** | WAV, MP3 | Metadata + transcription |\n| **HTML** | Web pages | Clean conversion |\n| **CSV** | Comma-separated values | Table format |\n| **JSON** | JSON data | Structured representation |\n| **XML** | XML documents | Structured format |\n| **ZIP** | Archive files | Iterates contents |\n| **EPUB** | E-books | Full text extraction |\n| **YouTube** | Video URLs | Fetch transcriptions |\n\n## Quick Start\n\n### Installation\n\n```bash\n# Install with all features\npip install 'markitdown[all]'\n\n# Or from source\ngit clone https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fmarkitdown.git\ncd markitdown\npip install -e 'packages\u002Fmarkitdown[all]'\n```\n\n### Command-Line Usage\n\n```bash\n# Basic conversion\nmarkitdown document.pdf > output.md\n\n# Specify output file\nmarkitdown document.pdf -o output.md\n\n# Pipe content\ncat document.pdf | markitdown > output.md\n\n# Enable plugins\nmarkitdown --list-plugins  # List available plugins\nmarkitdown --use-plugins document.pdf -o output.md\n```\n\n### Python API\n\n```python\nfrom markitdown import MarkItDown\n\n# Basic usage\nmd = MarkItDown()\nresult = md.convert(\"document.pdf\")\nprint(result.text_content)\n\n# Convert from stream\nwith open(\"document.pdf\", \"rb\") as f:\n    result = md.convert_stream(f, file_extension=\".pdf\")\n    print(result.text_content)\n```\n\n## Advanced Features\n\n### 1. AI-Enhanced Image Descriptions\n\nUse LLMs via OpenRouter to generate detailed image descriptions (for PPTX and image files):\n\n```python\nfrom markitdown import MarkItDown\nfrom openai import OpenAI\n\n# Initialize OpenRouter client (OpenAI-compatible API)\nclient = OpenAI(\n    api_key=\"your-openrouter-api-key\",\n    base_url=\"https:\u002F\u002Fopenrouter.ai\u002Fapi\u002Fv1\"\n)\n\nmd = MarkItDown(\n    llm_client=client,\n    llm_model=\"anthropic\u002Fclaude-sonnet-4.5\",  # recommended for scientific vision\n    llm_prompt=\"Describe this image in detail for scientific documentation\"\n)\n\nresult = md.convert(\"presentation.pptx\")\nprint(result.text_content)\n```\n\n### 2. Azure Document Intelligence\n\nFor enhanced PDF conversion with Microsoft Document Intelligence:\n\n```bash\n# Command line\nmarkitdown document.pdf -o output.md -d -e \"\u003Cdocument_intelligence_endpoint>\"\n```\n\n```python\n# Python API\nfrom markitdown import MarkItDown\n\nmd = MarkItDown(docintel_endpoint=\"\u003Cdocument_intelligence_endpoint>\")\nresult = md.convert(\"complex_document.pdf\")\nprint(result.text_content)\n```\n\n### 3. Plugin System\n\nMarkItDown supports 3rd-party plugins for extending functionality:\n\n```bash\n# List installed plugins\nmarkitdown --list-plugins\n\n# Enable plugins\nmarkitdown --use-plugins file.pdf -o output.md\n```\n\nFind plugins on GitHub with hashtag: `#markitdown-plugin`\n\n## Optional Dependencies\n\nControl which file formats you support:\n\n```bash\n# Install specific formats\npip install 'markitdown[pdf, docx, pptx]'\n\n# All available options:\n# [all]                  - All optional dependencies\n# [pptx]                 - PowerPoint files\n# [docx]                 - Word documents\n# [xlsx]                 - Excel spreadsheets\n# [xls]                  - Older Excel files\n# [pdf]                  - PDF documents\n# [outlook]              - Outlook messages\n# [az-doc-intel]         - Azure Document Intelligence\n# [audio-transcription]  - WAV and MP3 transcription\n# [youtube-transcription] - YouTube video transcription\n```\n\n## Common Use Cases\n\n### 1. Convert Scientific Papers to Markdown\n\n```python\nfrom markitdown import MarkItDown\n\nmd = MarkItDown()\n\n# Convert PDF paper\nresult = md.convert(\"research_paper.pdf\")\nwith open(\"paper.md\", \"w\") as f:\n    f.write(result.text_content)\n```\n\n### 2. Extract Data from Excel for Analysis\n\n```python\nfrom markitdown import MarkItDown\n\nmd = MarkItDown()\nresult = md.convert(\"data.xlsx\")\n\n# Result will be in Markdown table format\nprint(result.text_content)\n```\n\n### 3. Process Multiple Documents\n\n```python\nfrom markitdown import MarkItDown\nimport os\nfrom pathlib import Path\n\nmd = MarkItDown()\n\n# Process all PDFs in a directory\npdf_dir = Path(\"papers\u002F\")\noutput_dir = Path(\"markdown_output\u002F\")\noutput_dir.mkdir(exist_ok=True)\n\nfor pdf_file in pdf_dir.glob(\"*.pdf\"):\n    result = md.convert(str(pdf_file))\n    output_file = output_dir \u002F f\"{pdf_file.stem}.md\"\n    output_file.write_text(result.text_content)\n    print(f\"Converted: {pdf_file.name}\")\n```\n\n### 4. Convert PowerPoint with AI Descriptions\n\n```python\nfrom markitdown import MarkItDown\nfrom openai import OpenAI\n\n# Use OpenRouter for access to multiple AI models\nclient = OpenAI(\n    api_key=\"your-openrouter-api-key\",\n    base_url=\"https:\u002F\u002Fopenrouter.ai\u002Fapi\u002Fv1\"\n)\n\nmd = MarkItDown(\n    llm_client=client,\n    llm_model=\"anthropic\u002Fclaude-sonnet-4.5\",  # recommended for presentations\n    llm_prompt=\"Describe this slide image in detail, focusing on key visual elements and data\"\n)\n\nresult = md.convert(\"presentation.pptx\")\nwith open(\"presentation.md\", \"w\") as f:\n    f.write(result.text_content)\n```\n\n### 5. Batch Convert with Different Formats\n\n```python\nfrom markitdown import MarkItDown\nfrom pathlib import Path\n\nmd = MarkItDown()\n\n# Files to convert\nfiles = [\n    \"document.pdf\",\n    \"spreadsheet.xlsx\",\n    \"presentation.pptx\",\n    \"notes.docx\"\n]\n\nfor file in files:\n    try:\n        result = md.convert(file)\n        output = Path(file).stem + \".md\"\n        with open(output, \"w\") as f:\n            f.write(result.text_content)\n        print(f\"✓ Converted {file}\")\n    except Exception as e:\n        print(f\"✗ Error converting {file}: {e}\")\n```\n\n### 6. Extract YouTube Video Transcription\n\n```python\nfrom markitdown import MarkItDown\n\nmd = MarkItDown()\n\n# Convert YouTube video to transcript\nresult = md.convert(\"https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=VIDEO_ID\")\nprint(result.text_content)\n```\n\n## Docker Usage\n\n```bash\n# Build image\ndocker build -t markitdown:latest .\n\n# Run conversion\ndocker run --rm -i markitdown:latest \u003C ~\u002Fdocument.pdf > output.md\n```\n\n## Best Practices\n\n### 1. Choose the Right Conversion Method\n\n- **Simple documents**: Use basic `MarkItDown()`\n- **Complex PDFs**: Use Azure Document Intelligence\n- **Visual content**: Enable AI image descriptions\n- **Scanned documents**: Ensure OCR dependencies are installed\n\n### 2. Handle Errors Gracefully\n\n```python\nfrom markitdown import MarkItDown\n\nmd = MarkItDown()\n\ntry:\n    result = md.convert(\"document.pdf\")\n    print(result.text_content)\nexcept FileNotFoundError:\n    print(\"File not found\")\nexcept Exception as e:\n    print(f\"Conversion error: {e}\")\n```\n\n### 3. Process Large Files Efficiently\n\n```python\nfrom markitdown import MarkItDown\n\nmd = MarkItDown()\n\n# For large files, use streaming\nwith open(\"large_file.pdf\", \"rb\") as f:\n    result = md.convert_stream(f, file_extension=\".pdf\")\n    \n    # Process in chunks or save directly\n    with open(\"output.md\", \"w\") as out:\n        out.write(result.text_content)\n```\n\n### 4. Optimize for Token Efficiency\n\nMarkdown output is already token-efficient, but you can:\n- Remove excessive whitespace\n- Consolidate similar sections\n- Strip metadata if not needed\n\n```python\nfrom markitdown import MarkItDown\nimport re\n\nmd = MarkItDown()\nresult = md.convert(\"document.pdf\")\n\n# Clean up extra whitespace\nclean_text = re.sub(r'\\n{3,}', '\\n\\n', result.text_content)\nclean_text = clean_text.strip()\n\nprint(clean_text)\n```\n\n## Integration with Scientific Workflows\n\n### Convert Literature for Review\n\n```python\nfrom markitdown import MarkItDown\nfrom pathlib import Path\n\nmd = MarkItDown()\n\n# Convert all papers in literature folder\npapers_dir = Path(\"literature\u002Fpdfs\")\noutput_dir = Path(\"literature\u002Fmarkdown\")\noutput_dir.mkdir(exist_ok=True)\n\nfor paper in papers_dir.glob(\"*.pdf\"):\n    result = md.convert(str(paper))\n    \n    # Save with metadata\n    output_file = output_dir \u002F f\"{paper.stem}.md\"\n    content = f\"# {paper.stem}\\n\\n\"\n    content += f\"**Source**: {paper.name}\\n\\n\"\n    content += \"---\\n\\n\"\n    content += result.text_content\n    \n    output_file.write_text(content)\n\n# For AI-enhanced conversion with figures\nfrom openai import OpenAI\n\nclient = OpenAI(\n    api_key=\"your-openrouter-api-key\",\n    base_url=\"https:\u002F\u002Fopenrouter.ai\u002Fapi\u002Fv1\"\n)\n\nmd_ai = MarkItDown(\n    llm_client=client,\n    llm_model=\"anthropic\u002Fclaude-sonnet-4.5\",\n    llm_prompt=\"Describe scientific figures with technical precision\"\n)\n```\n\n### Extract Tables for Analysis\n\n```python\nfrom markitdown import MarkItDown\nimport re\n\nmd = MarkItDown()\nresult = md.convert(\"data_tables.xlsx\")\n\n# Markdown tables can be parsed or used directly\nprint(result.text_content)\n```\n\n## Troubleshooting\n\n### Common Issues\n\n1. **Missing dependencies**: Install feature-specific packages\n   ```bash\n   pip install 'markitdown[pdf]'  # For PDF support\n   ```\n\n2. **Binary file errors**: Ensure files are opened in binary mode\n   ```python\n   with open(\"file.pdf\", \"rb\") as f:  # Note the \"rb\"\n       result = md.convert_stream(f, file_extension=\".pdf\")\n   ```\n\n3. **OCR not working**: Install tesseract\n   ```bash\n   # macOS\n   brew install tesseract\n   \n   # Ubuntu\n   sudo apt-get install tesseract-ocr\n   ```\n\n## Performance Considerations\n\n- **PDF files**: Large PDFs may take time; consider page ranges if supported\n- **Image OCR**: OCR processing is CPU-intensive\n- **Audio transcription**: Requires additional compute resources\n- **AI image descriptions**: Requires API calls (costs may apply)\n\n## Next Steps\n\n- See `references\u002Fapi_reference.md` for complete API documentation\n- Check `references\u002Ffile_formats.md` for format-specific details\n- Review `scripts\u002Fbatch_convert.py` for automation examples\n- Explore `scripts\u002Fconvert_with_ai.py` for AI-enhanced conversions\n\n## Resources\n\n- **MarkItDown GitHub**: https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fmarkitdown\n- **PyPI**: https:\u002F\u002Fpypi.org\u002Fproject\u002Fmarkitdown\u002F\n- **OpenRouter**: https:\u002F\u002Fopenrouter.ai (for AI-enhanced conversions)\n- **OpenRouter API Keys**: https:\u002F\u002Fopenrouter.ai\u002Fkeys\n- **OpenRouter Models**: https:\u002F\u002Fopenrouter.ai\u002Fmodels\n- **MCP Server**: markitdown-mcp (for Claude Desktop integration)\n- **Plugin Development**: See `packages\u002Fmarkitdown-sample-plugin`\n\n","# MarkItDown - File to Markdown Conversion\n\n## Overview\n\nMarkItDown is a Python tool developed by Microsoft for converting various file formats to Markdown. It's particularly useful for converting documents into LLM-friendly text format, as Markdown is token-efficient and well-understood by modern language models.\n\n**Key Benefits**:\n- Convert documents to clean, structured Markdown\n- Token-efficient format for LLM processing\n- Supports 15+ file formats\n- Optional AI-enhanced image descriptions\n- OCR for images and scanned documents\n- Speech transcription for audio files\n\n## Visual Enhancement with Scientific Schematics\n\n**When creating documents with this skill, always consider adding scientific diagrams and schematics to enhance visual communication.**\n\nIf your document does not already contain schematics or diagrams:\n- Use the **scientific-schematics** skill to generate AI-powered publication-quality diagrams\n- Simply describe your desired diagram in natural language\n- Nano Banana Pro will automatically generate, review, and refine the schematic\n\n**For new documents:** Scientific schematics should be generated by default to visually represent key concepts, workflows, architectures, or relationships described in the text.\n\n**How to generate schematics:**\n```bash\npython scripts\u002Fgenerate_schematic.py \"your diagram description\" -o figures\u002Foutput.png\n```\n\nThe AI will automatically:\n- Create publication-quality images with proper formatting\n- Review and refine through multiple iterations\n- Ensure accessibility (colorblind-friendly, high contrast)\n- Save outputs in the figures\u002F directory\n\n**When to add schematics:**\n- Document conversion workflow diagrams\n- File format architecture illustrations\n- OCR processing pipeline diagrams\n- Integration workflow visualizations\n- System architecture diagrams\n- Data flow diagrams\n- Any complex concept that benefits from visualization\n\nFor detailed guidance on creating schematics, refer to the scientific-schematics skill documentation.\n\n---\n\n## Supported Formats\n\n| Format | Description | Notes |\n|--------|-------------|-------|\n| **PDF** | Portable Document Format | Full text extraction |\n| **DOCX** | Microsoft Word | Tables, formatting preserved |\n| **PPTX** | PowerPoint | Slides with notes |\n| **XLSX** | Excel spreadsheets | Tables and data |\n| **Images** | JPEG, PNG, GIF, WebP | EXIF metadata + OCR |\n| **Audio** | WAV, MP3 | Metadata + transcription |\n| **HTML** | Web pages | Clean conversion |\n| **CSV** | Comma-separated values | Table format |\n| **JSON** | JSON data | Structured representation |\n| **XML** | XML documents | Structured format |\n| **ZIP** | Archive files | Iterates contents |\n| **EPUB** | E-books | Full text extraction |\n| **YouTube** | Video URLs | Fetch transcriptions |\n\n## Quick Start\n\n### Installation\n\n```bash\n# Install with all features\npip install 'markitdown[all]'\n\n# Or from source\ngit clone https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fmarkitdown.git\ncd markitdown\npip install -e 'packages\u002Fmarkitdown[all]'\n```\n\n### Command-Line Usage\n\n```bash\n# Basic conversion\nmarkitdown document.pdf > output.md\n\n# Specify output file\nmarkitdown document.pdf -o output.md\n\n# Pipe content\ncat document.pdf | markitdown > output.md\n\n# Enable plugins\nmarkitdown --list-plugins  # List available plugins\nmarkitdown --use-plugins document.pdf -o output.md\n```\n\n### Python API\n\n```python\nfrom markitdown import MarkItDown\n\n# Basic usage\nmd = MarkItDown()\nresult = md.convert(\"document.pdf\")\nprint(result.text_content)\n\n# Convert from stream\nwith open(\"document.pdf\", \"rb\") as f:\n    result = md.convert_stream(f, file_extension=\".pdf\")\n    print(result.text_content)\n```\n\n## Advanced Features\n\n### 1. AI-Enhanced Image Descriptions\n\nUse LLMs via OpenRouter to generate detailed image descriptions (for PPTX and image files):\n\n```python\nfrom markitdown import MarkItDown\nfrom openai import OpenAI\n\n# Initialize OpenRouter client (OpenAI-compatible API)\nclient = OpenAI(\n    api_key=\"your-openrouter-api-key\",\n    base_url=\"https:\u002F\u002Fopenrouter.ai\u002Fapi\u002Fv1\"\n)\n\nmd = MarkItDown(\n    llm_client=client,\n    llm_model=\"anthropic\u002Fclaude-sonnet-4.5\",  # recommended for scientific vision\n    llm_prompt=\"Describe this image in detail for scientific documentation\"\n)\n\nresult = md.convert(\"presentation.pptx\")\nprint(result.text_content)\n```\n\n### 2. Azure Document Intelligence\n\nFor enhanced PDF conversion with Microsoft Document Intelligence:\n\n```bash\n# Command line\nmarkitdown document.pdf -o output.md -d -e \"\u003Cdocument_intelligence_endpoint>\"\n```\n\n```python\n# Python API\nfrom markitdown import MarkItDown\n\nmd = MarkItDown(docintel_endpoint=\"\u003Cdocument_intelligence_endpoint>\")\nresult = md.convert(\"complex_document.pdf\")\nprint(result.text_content)\n```\n\n### 3. Plugin System\n\nMarkItDown supports 3rd-party plugins for extending functionality:\n\n```bash\n# List installed plugins\nmarkitdown --list-plugins\n\n# Enable plugins\nmarkitdown --use-plugins file.pdf -o output.md\n```\n\nFind plugins on GitHub with hashtag: `#markitdown-plugin`\n\n## Optional Dependencies\n\nControl which file formats you support:\n\n```bash\n# Install specific formats\npip install 'markitdown[pdf, docx, pptx]'\n\n# All available options:\n# [all]                  - All optional dependencies\n# [pptx]                 - PowerPoint files\n# [docx]                 - Word documents\n# [xlsx]                 - Excel spreadsheets\n# [xls]                  - Older Excel files\n# [pdf]                  - PDF documents\n# [outlook]              - Outlook messages\n# [az-doc-intel]         - Azure Document Intelligence\n# [audio-transcription]  - WAV and MP3 transcription\n# [youtube-transcription] - YouTube video transcription\n```\n\n## Common Use Cases\n\n### 1. Convert Scientific Papers to Markdown\n\n```python\nfrom markitdown import MarkItDown\n\nmd = MarkItDown()\n\n# Convert PDF paper\nresult = md.convert(\"research_paper.pdf\")\nwith open(\"paper.md\", \"w\") as f:\n    f.write(result.text_content)\n```\n\n### 2. Extract Data from Excel for Analysis\n\n```python\nfrom markitdown import MarkItDown\n\nmd = MarkItDown()\nresult = md.convert(\"data.xlsx\")\n\n# Result will be in Markdown table format\nprint(result.text_content)\n```\n\n### 3. Process Multiple Documents\n\n```python\nfrom markitdown import MarkItDown\nimport os\nfrom pathlib import Path\n\nmd = MarkItDown()\n\n# Process all PDFs in a directory\npdf_dir = Path(\"papers\u002F\")\noutput_dir = Path(\"markdown_output\u002F\")\noutput_dir.mkdir(exist_ok=True)\n\nfor pdf_file in pdf_dir.glob(\"*.pdf\"):\n    result = md.convert(str(pdf_file))\n    output_file = output_dir \u002F f\"{pdf_file.stem}.md\"\n    output_file.write_text(result.text_content)\n    print(f\"Converted: {pdf_file.name}\")\n```\n\n### 4. Convert PowerPoint with AI Descriptions\n\n```python\nfrom markitdown import MarkItDown\nfrom openai import OpenAI\n\n# Use OpenRouter for access to multiple AI models\nclient = OpenAI(\n    api_key=\"your-openrouter-api-key\",\n    base_url=\"https:\u002F\u002Fopenrouter.ai\u002Fapi\u002Fv1\"\n)\n\nmd = MarkItDown(\n    llm_client=client,\n    llm_model=\"anthropic\u002Fclaude-sonnet-4.5\",  # recommended for presentations\n    llm_prompt=\"Describe this slide image in detail, focusing on key visual elements and data\"\n)\n\nresult = md.convert(\"presentation.pptx\")\nwith open(\"presentation.md\", \"w\") as f:\n    f.write(result.text_content)\n```\n\n### 5. Batch Convert with Different Formats\n\n```python\nfrom markitdown import MarkItDown\nfrom pathlib import Path\n\nmd = MarkItDown()\n\n# Files to convert\nfiles = [\n    \"document.pdf\",\n    \"spreadsheet.xlsx\",\n    \"presentation.pptx\",\n    \"notes.docx\"\n]\n\nfor file in files:\n    try:\n        result = md.convert(file)\n        output = Path(file).stem + \".md\"\n        with open(output, \"w\") as f:\n            f.write(result.text_content)\n        print(f\"✓ Converted {file}\")\n    except Exception as e:\n        print(f\"✗ Error converting {file}: {e}\")\n```\n\n### 6. Extract YouTube Video Transcription\n\n```python\nfrom markitdown import MarkItDown\n\nmd = MarkItDown()\n\n# Convert YouTube video to transcript\nresult = md.convert(\"https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=VIDEO_ID\")\nprint(result.text_content)\n```\n\n## Docker Usage\n\n```bash\n# Build image\ndocker build -t markitdown:latest .\n\n# Run conversion\ndocker run --rm -i markitdown:latest \u003C ~\u002Fdocument.pdf > output.md\n```\n\n## Best Practices\n\n### 1. Choose the Right Conversion Method\n\n- **Simple documents**: Use basic `MarkItDown()`\n- **Complex PDFs**: Use Azure Document Intelligence\n- **Visual content**: Enable AI image descriptions\n- **Scanned documents**: Ensure OCR dependencies are installed\n\n### 2. Handle Errors Gracefully\n\n```python\nfrom markitdown import MarkItDown\n\nmd = MarkItDown()\n\ntry:\n    result = md.convert(\"document.pdf\")\n    print(result.text_content)\nexcept FileNotFoundError:\n    print(\"File not found\")\nexcept Exception as e:\n    print(f\"Conversion error: {e}\")\n```\n\n### 3. Process Large Files Efficiently\n\n```python\nfrom markitdown import MarkItDown\n\nmd = MarkItDown()\n\n# For large files, use streaming\nwith open(\"large_file.pdf\", \"rb\") as f:\n    result = md.convert_stream(f, file_extension=\".pdf\")\n    \n    # Process in chunks or save directly\n    with open(\"output.md\", \"w\") as out:\n        out.write(result.text_content)\n```\n\n### 4. Optimize for Token Efficiency\n\nMarkdown output is already token-efficient, but you can:\n- Remove excessive whitespace\n- Consolidate similar sections\n- Strip metadata if not needed\n\n```python\nfrom markitdown import MarkItDown\nimport re\n\nmd = MarkItDown()\nresult = md.convert(\"document.pdf\")\n\n# Clean up extra whitespace\nclean_text = re.sub(r'\\n{3,}', '\\n\\n', result.text_content)\nclean_text = clean_text.strip()\n\nprint(clean_text)\n```\n\n## Integration with Scientific Workflows\n\n### Convert Literature for Review\n\n```python\nfrom markitdown import MarkItDown\nfrom pathlib import Path\n\nmd = MarkItDown()\n\n# Convert all papers in literature folder\npapers_dir = Path(\"literature\u002Fpdfs\")\noutput_dir = Path(\"literature\u002Fmarkdown\")\noutput_dir.mkdir(exist_ok=True)\n\nfor paper in papers_dir.glob(\"*.pdf\"):\n    result = md.convert(str(paper))\n    \n    # Save with metadata\n    output_file = output_dir \u002F f\"{paper.stem}.md\"\n    content = f\"# {paper.stem}\\n\\n\"\n    content += f\"**Source**: {paper.name}\\n\\n\"\n    content += \"---\\n\\n\"\n    content += result.text_content\n    \n    output_file.write_text(content)\n\n# For AI-enhanced conversion with figures\nfrom openai import OpenAI\n\nclient = OpenAI(\n    api_key=\"your-openrouter-api-key\",\n    base_url=\"https:\u002F\u002Fopenrouter.ai\u002Fapi\u002Fv1\"\n)\n\nmd_ai = MarkItDown(\n    llm_client=client,\n    llm_model=\"anthropic\u002Fclaude-sonnet-4.5\",\n    llm_prompt=\"Describe scientific figures with technical precision\"\n)\n```\n\n### Extract Tables for Analysis\n\n```python\nfrom markitdown import MarkItDown\nimport re\n\nmd = MarkItDown()\nresult = md.convert(\"data_tables.xlsx\")\n\n# Markdown tables can be parsed or used directly\nprint(result.text_content)\n```\n\n## Troubleshooting\n\n### Common Issues\n\n1. **Missing dependencies**: Install feature-specific packages\n   ```bash\n   pip install 'markitdown[pdf]'  # For PDF support\n   ```\n\n2. **Binary file errors**: Ensure files are opened in binary mode\n   ```python\n   with open(\"file.pdf\", \"rb\") as f:  # Note the \"rb\"\n       result = md.convert_stream(f, file_extension=\".pdf\")\n   ```\n\n3. **OCR not working**: Install tesseract\n   ```bash\n   # macOS\n   brew install tesseract\n   \n   # Ubuntu\n   sudo apt-get install tesseract-ocr\n   ```\n\n## Performance Considerations\n\n- **PDF files**: Large PDFs may take time; consider page ranges if supported\n- **Image OCR**: OCR processing is CPU-intensive\n- **Audio transcription**: Requires additional compute resources\n- **AI image descriptions**: Requires API calls (costs may apply)\n\n## Next Steps\n\n- See `references\u002Fapi_reference.md` for complete API documentation\n- Check `references\u002Ffile_formats.md` for format-specific details\n- Review `scripts\u002Fbatch_convert.py` for automation examples\n- Explore `scripts\u002Fconvert_with_ai.py` for AI-enhanced conversions\n\n## Resources\n\n- **MarkItDown GitHub**: https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fmarkitdown\n- **PyPI**: https:\u002F\u002Fpypi.org\u002Fproject\u002Fmarkitdown\u002F\n- **OpenRouter**: https:\u002F\u002Fopenrouter.ai (for AI-enhanced conversions)\n- **OpenRouter API Keys**: https:\u002F\u002Fopenrouter.ai\u002Fkeys\n- **OpenRouter Models**: https:\u002F\u002Fopenrouter.ai\u002Fmodels\n- **MCP Server**: markitdown-mcp (for Claude Desktop integration)\n- **Plugin Development**: See `packages\u002Fmarkitdown-sample-plugin`\n\n",{"name":5,"source":60,"license":15,"category":61,"description":7,"allowed-tools":62},"https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fmarkitdown","data-engineering",[63,64,65,47],"Read","Write","Edit","main","4aa39efd1cdab0b4657ecee16c92a92011a5b10f","openscience\u002Fbackend\u002Fcli\u002Fskills\u002Fdata-engineering\u002Fmarkitdown","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub","https:\u002F\u002Fgithub.com\u002Ffadrienne","2026-10-28 00:43:09",{"source":8,"skillName":5,"commands":73,"prompt":83,"zipAvailable":84},[74,77,80],{"tool":75,"command":76},"npx","npx skills add fadrienne\u002FMyGitHub --skill markitdown",{"tool":78,"command":79},"bunx","bunx skills add fadrienne\u002FMyGitHub --skill markitdown",{"tool":81,"command":82},"pnpm","pnpm dlx skills add fadrienne\u002FMyGitHub --skill 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size calculation, power analysis, and effect size conversion for scientific studies. Use BEFORE data collection (a priori) to determine required N, or after (post-hoc) to compute achieved power. Covers parametric tests (t-test, ANOVA, chi-square, proportions, correlation, regression), simulation-based power for complex\u002Fclustered\u002Fnested designs, and power curves. Use this skill when someone asks 'how many samples do I need', 'what is my power', 'what effect size can I detect', or any variant. Pairs with \u002Fexperimental-design (choose design first, then compute power) and \u002Fstatistical-analysis (analyze data after collection).","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fstatistical-power\u002FSKILL.md",[148],{"categoryNo":149,"categoryName":150,"taxonomyType":22,"taxonomyTypeDesc":23},"01JBSEEDCAT000000000000023","Skill与智能体开发",[152,153,154,155,156,157,158,159,160,47,161,162,163,164,165,166,167,168,169,170],"power","statistical power","effect","scipy","regression","choose","design","uv","when","collection","analysis","calculation","ANOVA","conversion","analyze","t-test","chi-square","simulation","sample",{"skillNo":172,"name":173,"nameZh":6,"description":174,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":175,"categories":176,"keywords":180,"publishedAt":55},"01M71677EBCM8EPCE9BZCK8505","statistical-analysis","Guided statistical analysis with test selection and reporting. Use when you need help choosing appropriate tests for your data, assumption checking, power analysis, and APA-formatted results. Best for academic research reporting, test selection guidance. For implementing specific models programmatically use statsmodels.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fstatistical-analysis\u002FSKILL.md",[177],{"categoryNo":178,"categoryName":179,"taxonomyType":22,"taxonomyTypeDesc":23},"01JBSEEDCAT000000000000008","测试与质量",[162,181,152,182,183,184,160,185,186,50,187,188,189,190,191,192,193,194,195,196],"statistical analysis","academic research","implementing","selection","you","reporting","for","tests","use","with","test","research","results","statsmodels","models","help",{"skillNo":198,"name":199,"nameZh":6,"description":200,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":201,"categories":202,"keywords":210,"publishedAt":55},"01M71673R93XAHFH81DBNVNG7H","scientific-writing","Core skill for the deep research and writing tool. Write scientific manuscripts in full paragraphs (never bullet points). Use two-stage process with (1) section outlines with key points using research-lookup then (2) convert to flowing prose. IMRAD structure, citations (APA\u002FAMA\u002FVancouver), figures\u002Ftables, reporting guidelines (CONSORT\u002FSTROBE\u002FPRISMA), for research papers and journal submissions.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fscientific-writing\u002FSKILL.md",[203,206,207],{"categoryNo":204,"categoryName":205,"taxonomyType":22,"taxonomyTypeDesc":23},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":149,"categoryName":150,"taxonomyType":22,"taxonomyTypeDesc":23},{"categoryNo":208,"categoryName":209,"taxonomyType":33,"taxonomyTypeDesc":34},"01JBSEEDCAT000000000000036","内容创作者",[211,212,213,214,215,216,217,218,219,220,47,221,222,223,224,225,226,45,186,227],"writing","scientific","scientific writing","Citations","deep","deep research","never","papers","journal","false","api","prisma","structure","openrouter","Outlines","core skill","features",{"skillNo":229,"name":230,"nameZh":6,"description":231,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":232,"categories":233,"keywords":238,"publishedAt":55},"01M7166ZPKXBF4EBPHVTJPQBB1","scientific-visualization","Meta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib\u002Fseaborn\u002Fplotly with publication styles. For quick exploration use seaborn or plotly directly.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fscientific-visualization\u002FSKILL.md",[234,237],{"categoryNo":235,"categoryName":236,"taxonomyType":22,"taxonomyTypeDesc":23},"01JBSEEDCAT000000000000013","分析与可视化",{"categoryNo":149,"categoryName":150,"taxonomyType":22,"taxonomyTypeDesc":23},[239,212,240,241,242,243,244,245,246,219,160,247,248,249,250,251,252,253,187,254],"visualization","scientific visualization","error","ready","quick","exploration","meta-skill","Meta","safe","plotly","annotations","formatting","Publication","figures","panel","submission",{"skillNo":256,"name":257,"nameZh":6,"description":258,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":259,"categories":260,"keywords":267,"publishedAt":55},"01M7166VXJWQSSC3RQZP60WBGR","research-code","Debug, profile, review, and restructure Python research and analysis code. Use when analysis scripts crash, produce wrong results, run too slowly, or need a correctness review. Covers: reading tracebacks and diagnosing root causes, hypothesis-driven debugging, reviewing code for common statistical\u002Fnumerical errors, profiling data pipelines, improving reproducibility (seeds, environments, determinism), and structuring analysis scripts for sharing. Distinct from \u002Fexploratory-data-analysis (which understands data) and \u002Fstatistical-analysis (which chooses and runs tests) — this skill focuses on the code itself.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fresearch-code\u002FSKILL.md",[261,264],{"categoryNo":262,"categoryName":263,"taxonomyType":22,"taxonomyTypeDesc":23},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":265,"categoryName":266,"taxonomyType":33,"taxonomyTypeDesc":34},"01JBSEEDCAT000000000000030","开发工程师",[268,269,192,270,271,272,273,159,274,160,47,162,275,276,277,278,279,280,45,44],"code","research code","which","memory","data-analysis","errors","debugging","common","review","data pipelines","run","scripts","line",{"skillNo":282,"name":283,"nameZh":6,"description":284,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":285,"licenseFileUrl":6,"sourceHtmlUrl":286,"categories":287,"keywords":292,"publishedAt":55},"01M7166CF5HSR7SVZYZFWTDX55","markdown-mermaid-writing","Comprehensive markdown and Mermaid diagram writing skill. Use when creating any scientific document, report, analysis, or visualization. Establishes text-based diagrams as the default documentation standard with full style guides (markdown + mermaid), 24 diagram type references, and 9 document templates.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fmarkdown-mermaid-writing\u002FSKILL.md",[288,289,290,291],{"categoryNo":20,"categoryName":21,"taxonomyType":22,"taxonomyTypeDesc":23},{"categoryNo":204,"categoryName":205,"taxonomyType":22,"taxonomyTypeDesc":23},{"categoryNo":149,"categoryName":150,"taxonomyType":22,"taxonomyTypeDesc":23},{"categoryNo":208,"categoryName":209,"taxonomyType":33,"taxonomyTypeDesc":34},[211,293,294,36,295,296,239,297,298,160,47,162,299,300,301,302,303,304,212,45],"markdown mermaid writing","mermaid","community","diagrams","project","default","team","guides","text","documentation","references","diagram"]