back
loading skill details...
>
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
Provide the Office file (Word, Excel, PowerPoint, PDF, etc.)
Optionally specify conversion options
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
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
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
# Install
pip install markitdown
# Convert file
markitdown document.docx > output.md
# Or with output file
markitdown document.docx -o output.md
Word Document Conversion
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:
# 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
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:
## 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
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:
# 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
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)
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
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
Check Output Quality: Review converted Markdown for accuracy
Handle Tables: Complex tables may need manual adjustment
Preserve Structure: Use consistent heading levels in source docs
Image Handling: Consider using vision models for important images
Version Control: Store converted Markdown in Git for tracking
Common Patterns
Document Archive
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
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
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
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
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
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
PyPI Package
Supported Formats
1fdon't have the plugin yet? install it then click "run inline in claude" again.