--- name: office-to-md-2704d5120d0d description: Convert Office documents (Word, Excel, PowerPoint, PDF) to Markdown using Microsoft's markitdown author: claude-office-skills version: "1.0" tags: [markdown, conversion, markitdown, microsoft, office] models: [claude-sonnet-4, claude-opus-4] tools: [computer, code_execution, file_operations] library: name: markitdown url: https://github.com/microsoft/markitdown stars: 86k --- # Office to Markdown Skill ## Overview This skill enables conversion from various Office formats to Markdown using **markitdown** - Microsoft's open-source tool for converting documents to Markdown. Perfect for making Office content searchable, version-controllable, and AI-friendly. ## How to Use 1. Provide the Office file (Word, Excel, PowerPoint, PDF, etc.) 2. Optionally specify conversion options 3. I'll convert it to clean Markdown **Example prompts:** - "Convert this Word document to Markdown" - "Turn this PowerPoint into Markdown notes" - "Extract content from this PDF as Markdown" - "Convert this Excel file to Markdown tables" ## Domain Knowledge ### markitdown Fundamentals ```python from markitdown import MarkItDown # Initialize converter md = MarkItDown() # Convert file result = md.convert("document.docx") print(result.text_content) # Save to file with open("output.md", "w") as f: f.write(result.text_content) ``` ### Supported Formats | Format | Extension | Notes | |--------|-----------|-------| | Word | .docx | Full text, tables, basic formatting | | Excel | .xlsx | Converts to Markdown tables | | PowerPoint | .pptx | Slides as sections | | PDF | .pdf | Text extraction | | HTML | .html | Clean markdown | | Images | .jpg, .png | OCR with vision model | | Audio | .mp3, .wav | Transcription | | ZIP | .zip | Processes contained files | ### Basic Usage #### Python API ```python from markitdown import MarkItDown # Simple conversion md = MarkItDown() result = md.convert("document.docx") # Access content markdown_text = result.text_content # With options md = MarkItDown( llm_client=None, # Optional LLM for enhanced processing llm_model=None # Model name if using LLM ) ``` #### Command Line ```bash # Install pip install markitdown # Convert file markitdown document.docx > output.md # Or with output file markitdown document.docx -o output.md ``` ### Word Document Conversion ```python from markitdown import MarkItDown md = MarkItDown() # Convert Word document result = md.convert("report.docx") # Output preserves: # - Headings (as # headers) # - Bold/italic formatting # - Lists (bulleted and numbered) # - Tables (as markdown tables) # - Hyperlinks print(result.text_content) ``` **Example Output:** ```markdown # Annual Report 2024 ## Executive Summary This report summarizes the key achievements and challenges... ### Key Metrics | Metric | 2023 | 2024 | Change | |--------|------|------|--------| | Revenue | $10M | $12M | +20% | | Users | 50K | 75K | +50% | ## Detailed Analysis The following sections provide... ``` ### Excel Conversion ```python from markitdown import MarkItDown md = MarkItDown() result = md.convert("data.xlsx") # Each sheet becomes a section # Data becomes markdown tables print(result.text_content) ``` **Example Output:** ```markdown ## Sheet1 | Name | Department | Salary | |------|------------|--------| | John | Engineering | $80,000 | | Jane | Marketing | $75,000 | ## Sheet2 | Product | Q1 | Q2 | Q3 | Q4 | |---------|----|----|----|----| | Widget A | 100 | 120 | 150 | 180 | ``` ### PowerPoint Conversion ```python from markitdown import MarkItDown md = MarkItDown() result = md.convert("presentation.pptx") # Each slide becomes a section # Speaker notes included if present print(result.text_content) ``` **Example Output:** ```markdown # Slide 1: Company Overview Our mission is to... ## Key Points - Innovation first - Customer focused - Global reach --- # Slide 2: Market Analysis The market opportunity is significant... **Notes:** Mention the competitor analysis here ``` ### PDF Conversion ```python from markitdown import MarkItDown md = MarkItDown() result = md.convert("document.pdf") # Extracts text content # Tables converted where detected print(result.text_content) ``` ### Image Conversion (with Vision Model) ```python from markitdown import MarkItDown import anthropic # Use Claude for image description client = anthropic.Anthropic() md = MarkItDown( llm_client=client, llm_model="claude-sonnet-4-20250514" ) result = md.convert("diagram.png") print(result.text_content) # Output: Description of the image content ``` ### Batch Conversion ```python from markitdown import MarkItDown from pathlib import Path def batch_convert(input_dir, output_dir): """Convert all Office files to Markdown.""" md = MarkItDown() input_path = Path(input_dir) output_path = Path(output_dir) output_path.mkdir(exist_ok=True) extensions = ['.docx', '.xlsx', '.pptx', '.pdf'] for ext in extensions: for file in input_path.glob(f'*{ext}'): try: result = md.convert(str(file)) output_file = output_path / f"{file.stem}.md" with open(output_file, 'w') as f: f.write(result.text_content) print(f"Converted: {file.name}") except Exception as e: print(f"Error converting {file.name}: {e}") batch_convert('./documents', './markdown') ``` ## Best Practices 1. **Check Output Quality**: Review converted Markdown for accuracy 2. **Handle Tables**: Complex tables may need manual adjustment 3. **Preserve Structure**: Use consistent heading levels in source docs 4. **Image Handling**: Consider using vision models for important images 5. **Version Control**: Store converted Markdown in Git for tracking ## Common Patterns ### Document Archive ```python import os from datetime import datetime from markitdown import MarkItDown def archive_document(doc_path, archive_dir): """Convert and archive Office document to Markdown.""" md = MarkItDown() result = md.convert(doc_path) # Create archive structure date_str = datetime.now().strftime('%Y-%m-%d') filename = os.path.basename(doc_path) base_name = os.path.splitext(filename)[0] # Save with metadata output_content = f"""--- source: {filename} converted: {date_str} --- {result.text_content} """ output_path = os.path.join(archive_dir, f"{base_name}.md") with open(output_path, 'w') as f: f.write(output_content) return output_path ``` ### AI-Ready Corpus ```python from markitdown import MarkItDown from pathlib import Path import json def create_ai_corpus(doc_folder, output_file): """Convert documents to JSON corpus for AI training/RAG.""" md = MarkItDown() corpus = [] for doc in Path(doc_folder).glob('**/*'): if doc.suffix in ['.docx', '.pdf', '.pptx', '.xlsx']: try: result = md.convert(str(doc)) corpus.append({ 'source': str(doc), 'filename': doc.name, 'content': result.text_content, 'type': doc.suffix[1:] }) except Exception as e: print(f"Skipped {doc.name}: {e}") with open(output_file, 'w') as f: json.dump(corpus, f, indent=2) print(f"Created corpus with {len(corpus)} documents") return corpus ``` ## Examples ### Example 1: Convert Documentation Suite ```python from markitdown import MarkItDown from pathlib import Path def convert_docs_to_wiki(docs_folder, wiki_folder): """Convert all Office docs to markdown wiki structure.""" md = MarkItDown() docs_path = Path(docs_folder) wiki_path = Path(wiki_folder) # Create wiki structure wiki_path.mkdir(exist_ok=True) # Create index index_content = "# Documentation Index\n\n" for doc in sorted(docs_path.glob('**/*.docx')): try: result = md.convert(str(doc)) # Create relative path in wiki rel_path = doc.relative_to(docs_path) output_file = wiki_path / rel_path.with_suffix('.md') output_file.parent.mkdir(parents=True, exist_ok=True) # Write markdown with open(output_file, 'w') as f: f.write(result.text_content) # Add to index link = str(rel_path.with_suffix('.md')).replace('\\', '/') index_content += f"- [{doc.stem}]({link})\n" print(f"Converted: {doc.name}") except Exception as e: print(f"Error: {doc.name} - {e}") # Write index with open(wiki_path / 'index.md', 'w') as f: f.write(index_content) convert_docs_to_wiki('./company_docs', './wiki') ``` ### Example 2: Meeting Notes Processor ```python from markitdown import MarkItDown import re from datetime import datetime def process_meeting_notes(pptx_path): """Extract and structure meeting notes from PowerPoint.""" md = MarkItDown() result = md.convert(pptx_path) # Parse the markdown content = result.text_content # Extract sections sections = { 'attendees': [], 'agenda': [], 'decisions': [], 'action_items': [] } current_section = None for line in content.split('\n'): line_lower = line.lower() if 'attendee' in line_lower or 'participant' in line_lower: current_section = 'attendees' elif 'agenda' in line_lower: current_section = 'agenda' elif 'decision' in line_lower: current_section = 'decisions' elif 'action' in line_lower: current_section = 'action_items' elif line.strip().startswith(('-', '*', '•')) and current_section: sections[current_section].append(line.strip()[1:].strip()) # Generate structured output output = f"""# Meeting Notes **Date:** {datetime.now().strftime('%Y-%m-%d')} **Source:** {pptx_path} ## Attendees {chr(10).join('- ' + a for a in sections['attendees'])} ## Agenda {chr(10).join('- ' + a for a in sections['agenda'])} ## Decisions Made {chr(10).join('- ' + d for d in sections['decisions'])} ## Action Items {chr(10).join('- [ ] ' + a for a in sections['action_items'])} """ return output notes = process_meeting_notes('team_meeting.pptx') print(notes) ``` ### Example 3: Excel to Documentation ```python from markitdown import MarkItDown def excel_to_data_dictionary(xlsx_path): """Convert Excel data model to data dictionary documentation.""" md = MarkItDown() result = md.convert(xlsx_path) # Add documentation structure doc = f"""# Data Dictionary Generated from: `{xlsx_path}` {result.text_content} ## Usage Notes - All tables are derived from the source Excel file - Review data types and constraints before use - Contact data team for clarifications ## Change Log | Date | Change | Author | |------|--------|--------| | {datetime.now().strftime('%Y-%m-%d')} | Initial generation | Auto | """ return doc documentation = excel_to_data_dictionary('data_model.xlsx') with open('data_dictionary.md', 'w') as f: f.write(documentation) ``` ## Limitations - Complex formatting may be simplified - Images are not embedded (use vision model for descriptions) - Some table structures may not convert perfectly - Track changes in Word are not preserved - Comments may not be extracted ## Installation ```bash pip install markitdown # For image/audio processing pip install markitdown[all] # For specific features pip install markitdown[images] # Image OCR pip install markitdown[audio] # Audio transcription ``` ## Resources - [GitHub Repository](https://github.com/microsoft/markitdown) - [PyPI Package](https://pypi.org/project/markitdown/) - [Supported Formats](https://github.com/microsoft/markitdown#supported-formats) --- Source attribution: claude-office-skills Original project: https://github.com/kbarbel640-del/skills/blob/e7e336ac628c3e3bd771aff1aaa469c93778f94a/skills/lijie420461340/office-to-md/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2026 clawdbot Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: claude-office-skills Original project: https://github.com/kbarbel640-del/skills/blob/e7e336ac628c3e3bd771aff1aaa469c93778f94a/skills/lijie420461340/office-to-md/SKILL.md Attribution basis: Source repository record Fields of work: Marketing, Content & Media, Design, Product Management Roles: Content Marketer, Technical Writer, UX Writer, Copywriter, Product Manager Specialties: Word Documents, Excel, PowerPoint Tools: Markitdown Country applicability: Country not specified Classification basis: Source category, role and specialization labels Instruction SHA-256: ef6ac9cb44aca0ee6172f3d3ae3020ada511b60a9158d2e8d02e62dc13a09301