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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.","davila7\u002Fclaude-code-templates","davila7","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6216945?v=4","01M6ADEWWFPS191NFM0Y1EX1JH","Daniel Avila","01M6ADJHGM78PHQRD3XZTFDVKK",33481,"MIT","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fmarkitdown\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fmarkitdown\u002FSKILL.md",[19,24,27,30,33],{"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":22,"taxonomyTypeDesc":23},"01JBSEEDCAT000000000000016","图像与多媒体",{"categoryNo":34,"categoryName":35,"taxonomyType":36,"taxonomyTypeDesc":37},"01JBSEEDCAT000000000000034","行政与办公人员","PROFESSION","适用职业",[5,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62],"zip","docx","youtube","files","images","edit","write","read","json","convert","markdown","Bash","Audio","ocr","office","csv","xml","https","documents","microsoft","github","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.\"\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 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markitdown","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fmarkitdown\u002FSKILL.md",true,[97,103,109,113,118,122,126,130,134,138,144,148,152],{"path":98,"resourceType":99,"resourceTypeDesc":100,"sizeBytes":101,"sourceHtmlUrl":102},"assets\u002Fexample_usage.md","ASSET","资产文件",10378,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fmarkitdown\u002Fassets\u002Fexample_usage.md",{"path":104,"resourceType":105,"resourceTypeDesc":106,"sizeBytes":107,"sourceHtmlUrl":108},"examples\u002Fexploration-patterns.md","REFERENCE","参考文档",7382,"https:\u002F\u002Fgithub.com\u002Fshengdabai\u002FTony-Claude-Code-Skills\u002Fblob\u002Fmain\u002Fskills\u002Frsmdt-the-startup\u002Fplugins\u002Fteam\u002Fskills\u002Fcross-cutting\u002Fcodebase-navigation\u002Fexamples\u002Fexploration-patterns.md",{"path":110,"resourceType":105,"resourceTypeDesc":106,"sizeBytes":111,"sourceHtmlUrl":112},"INSTALLATION_GUIDE.md",6722,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fmarkitdown\u002FINSTALLATION_GUIDE.md",{"path":114,"resourceType":115,"resourceTypeDesc":116,"sizeBytes":117,"sourceHtmlUrl":16},"LICENSE.txt","LICENSE","许可证",1075,{"path":119,"resourceType":105,"resourceTypeDesc":106,"sizeBytes":120,"sourceHtmlUrl":121},"OPENROUTER_INTEGRATION.md",9671,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fmarkitdown\u002FOPENROUTER_INTEGRATION.md",{"path":123,"resourceType":105,"resourceTypeDesc":106,"sizeBytes":124,"sourceHtmlUrl":125},"QUICK_REFERENCE.md",6823,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fmarkitdown\u002FQUICK_REFERENCE.md",{"path":127,"resourceType":105,"resourceTypeDesc":106,"sizeBytes":128,"sourceHtmlUrl":129},"README.md",5013,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fmarkitdown\u002FREADME.md",{"path":131,"resourceType":105,"resourceTypeDesc":106,"sizeBytes":132,"sourceHtmlUrl":133},"references\u002Fapi_reference.md",8980,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fmarkitdown\u002Freferences\u002Fapi_reference.md",{"path":135,"resourceType":105,"resourceTypeDesc":106,"sizeBytes":136,"sourceHtmlUrl":137},"references\u002Ffile_formats.md",8985,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fmarkitdown\u002Freferences\u002Ffile_formats.md",{"path":139,"resourceType":140,"resourceTypeDesc":141,"sizeBytes":142,"sourceHtmlUrl":143},"scripts\u002Fbatch_convert.py","SCRIPT","脚本",6650,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fmarkitdown\u002Fscripts\u002Fbatch_convert.py",{"path":145,"resourceType":140,"resourceTypeDesc":141,"sizeBytes":146,"sourceHtmlUrl":147},"scripts\u002Fconvert_literature.py",8915,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fmarkitdown\u002Fscripts\u002Fconvert_literature.py",{"path":149,"resourceType":140,"resourceTypeDesc":141,"sizeBytes":150,"sourceHtmlUrl":151},"scripts\u002Fconvert_with_ai.py",7629,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fmarkitdown\u002Fscripts\u002Fconvert_with_ai.py",{"path":153,"resourceType":105,"resourceTypeDesc":106,"sizeBytes":154,"sourceHtmlUrl":155},"SKILL_SUMMARY.md",8765,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fmarkitdown\u002FSKILL_SUMMARY.md",[157,187,214,245,280,304],{"skillNo":158,"name":159,"nameZh":6,"description":160,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":161,"categories":162,"keywords":166,"publishedAt":63},"01M70NHBTQ8BV0A2SCAZXEPHCX","wealthville-pool-scoring","Score DeFi liquidity pools before providing liquidity. Returns a 0-100 pool score and an Enter\u002FHold\u002FExit\u002FReduce\u002FAvoid verdict with calibrated confidence for ~68,800 Solana pools and 575 EVM pools, plus a public miss-inclusive track record. Use when asked whether a pool is worth LPing into, which pools look best right now, or how accurate the scores have actually been.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fweb-data\u002Fwealthville-pool-scoring\u002FSKILL.md",[163],{"categoryNo":164,"categoryName":165,"taxonomyType":22,"taxonomyTypeDesc":23},"01JBSEEDCAT000000000000023","Skill与智能体开发",[167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186],"scoring","pool","wealthville pool scoring","liquidity","which","for","exit","now","use","when","reduce","with","record","track","verdict","evm","solana","how","public","look",{"skillNo":188,"name":189,"nameZh":6,"description":190,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":191,"categories":192,"keywords":194,"publishedAt":63},"01M70MB6VR8ERJ4EM8D5KHBMEV","technical-debt-auditor","Scans a codebase and produces a quantified technical debt report with a prioritized remediation plan. Analyzes dead code, TODO\u002FFIXME\u002FHACK markers, dependency staleness, complexity hotspots, code duplication signals, test coverage gaps, unused exports, and outdated patterns. Use this skill when the user asks to audit their codebase for tech debt, wants a health check of their code quality, says things like \"where should I focus refactoring\", \"find code smells\", \"audit my codebase\", \"what technical debt do we have\", \"prioritize cleanup work\", or asks about code quality metrics. Also trigger when the user mentions \"code rot\", \"legacy cleanup\", \"refactoring priorities\", or wants to understand the overall health of a project before a major effort. Trigger even if the user doesn't say \"technical debt\" explicitly — any request to systematically assess code quality and identify improvement priorities belongs here.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Ftechnical-debt-auditor\u002FSKILL.md",[193],{"categoryNo":164,"categoryName":165,"taxonomyType":22,"taxonomyTypeDesc":23},[195,196,197,198,199,200,201,202,203,204,205,176,206,207,208,209,210,211,212,213],"technical debt","debt","technical","technical debt auditor","auditor","coverage","complexity","markers","project","cleanup","unused","code","todo","work","plan","focus","signals","audit","dependency",{"skillNo":215,"name":216,"nameZh":6,"description":217,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":218,"categories":219,"keywords":227,"publishedAt":63},"01M70KPYH9QNHCNME5GJQGE22K","readonly-sql-guard","Make an agent's database access provably read-only. Use when an agent runs caller-supplied SQL against a production\u002FERP\u002Fanalytics database (a \"query\" MCP tool, text-to-SQL, an admin console), or when reviewing DB-access code for write\u002FDDL\u002Fside-effect\u002Ftransaction-escape risks. Checks what a statement CALLS, not just its shape.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdatabase\u002Freadonly-sql-guard\u002FSKILL.md",[220,223,224],{"categoryNo":221,"categoryName":222,"taxonomyType":22,"taxonomyTypeDesc":23},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":164,"categoryName":165,"taxonomyType":22,"taxonomyTypeDesc":23},{"categoryNo":225,"categoryName":226,"taxonomyType":36,"taxonomyTypeDesc":37},"01JBSEEDCAT000000000000033","数据分析师",[228,229,230,231,232,233,234,172,235,236,237,238,239,240,241,175,242,243,176,244],"readonly","guard","sql guard","sql","readonly sql guard","erp","effect","read-only","db","just","tool","calls","only","MCP","access","database","query",{"skillNo":246,"name":247,"nameZh":6,"description":248,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":249,"licenseFileUrl":250,"sourceHtmlUrl":251,"categories":252,"keywords":256,"publishedAt":63},"01M6AFY6F7CC2BBZP1QD6VTNVC","web-artifacts-builder","Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn\u002Fui). Use for complex artifacts requiring state management, routing, or shadcn\u002Fui components - not for simple single-file HTML\u002FJSX artifacts.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FSKILL.md",[253],{"categoryNo":254,"categoryName":255,"taxonomyType":22,"taxonomyTypeDesc":23},"01JBSEEDCAT000000000000009","前端开发",[257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,172,279],"artifacts builder","web","web artifacts builder","artifacts","builder","state","routing","state management","component","css","frontend","ui","Tools","file","components","shadcn","react","Tailwind","suite","jsx","html","single-file","ui components",{"skillNo":281,"name":282,"nameZh":6,"description":283,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":249,"licenseFileUrl":284,"sourceHtmlUrl":285,"categories":286,"keywords":291,"publishedAt":63},"01M6AFWF33SFV9Z3SF4PEEE4BF","webapp-testing","Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FSKILL.md",[287,290],{"categoryNo":288,"categoryName":289,"taxonomyType":22,"taxonomyTypeDesc":23},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":254,"categoryName":255,"taxonomyType":22,"taxonomyTypeDesc":23},[292,293,294,295,296,258,297,267,268,298,299,300,301,172,302,178,303],"webapp testing","testing","webapp","browser","playwright","applications","debugging","toolkit","logs","screenshots","local","behavior",{"skillNo":305,"name":306,"nameZh":6,"description":307,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":249,"licenseFileUrl":308,"sourceHtmlUrl":309,"categories":310,"keywords":318,"publishedAt":63},"01M6AF67Q67JJJ5V51JR2P52KA","discernment-nudge","After you give a substantive answer or draft that the user may act on — advice or recommendations, drafted artifacts such as goals, plans, pitches, proposals, or emails, estimates or projections, analysis or interpretation of data, factual claims they may rely on, or a multi-step argument — invoke this skill BEFORE finalizing your reply and then, if it applies, append 2-3 short follow-up questions, each tied to something specific in what you just produced, that help the user check key facts, probe the reasoning or assumptions, and notice missing context. Do this at most once per conversation. Skip it when the user asked a trivial how-to or simple lookup, wants a purely educational explanation, asked you only to format, convert, or assemble a file from content they provided, is writing code they will run, is doing creative writing or casual chat, or already asked you to double-check, cite, or review — the skill file explains these boundaries and the exact output format.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FSKILL.md",[311,314,315],{"categoryNo":312,"categoryName":313,"taxonomyType":22,"taxonomyTypeDesc":23},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":164,"categoryName":165,"taxonomyType":22,"taxonomyTypeDesc":23},{"categoryNo":316,"categoryName":317,"taxonomyType":36,"taxonomyTypeDesc":37},"01JBSEEDCAT000000000000036","内容创作者",[319,320,321,322,323,324,325,326,260,327,270,206,328,329,330,331,332,240,333,176,334,335,336,337,338,184,339],"discernment nudge","nudge","interpretation","help","run","reasoning","data","writing","lookup","content","review","conversation","short","up","invoke","you","analysis","claims","argument","assemble","recommendations"]