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