markitdown

name	markitdown
description	Convert files and office documents to Markdown, including PDFs, Office files, images with OCR, audio with transcription, HTML, archives, and structured data. Use when the user needs markdown extraction or document-to-markdown conversion.
allowed-tools	Read Write Edit Bash
license	MIT license
metadata	{"skill-author":"K-Dense Inc."}

MarkItDown - File to Markdown Conversion

Overview

MarkItDown 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.

Key Benefits:

• Convert documents to clean, structured Markdown
• Token-efficient format for LLM processing
• Supports 15+ file formats
• Optional AI-enhanced image descriptions
• OCR for images and scanned documents
• Speech transcription for audio files

Visual Enhancement with Scientific Schematics

When creating documents with this skill, always consider adding scientific diagrams and schematics to enhance visual communication.

If your document does not already contain schematics or diagrams:

• Use the scientific-schematics skill to generate AI-powered publication-quality diagrams
• Simply describe your desired diagram in natural language
• Nano Banana Pro will automatically generate, review, and refine the schematic

For new documents: Scientific schematics should be generated by default to visually represent key concepts, workflows, architectures, or relationships described in the text.

How to generate schematics:

python scripts/generate_schematic.py "your diagram description" -o figures/output.png

The AI will automatically:

• Create publication-quality images with proper formatting
• Review and refine through multiple iterations
• Ensure accessibility (colorblind-friendly, high contrast)
• Save outputs in the figures/ directory

When to add schematics:

• Document conversion workflow diagrams
• File format architecture illustrations
• OCR processing pipeline diagrams
• Integration workflow visualizations
• System architecture diagrams
• Data flow diagrams
• Any complex concept that benefits from visualization

For detailed guidance on creating schematics, refer to the scientific-schematics skill documentation.

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Supported Formats

Format	Description	Notes
PDF	Portable Document Format	Full text extraction
DOCX	Microsoft Word	Tables, formatting preserved
PPTX	PowerPoint	Slides with notes
XLSX	Excel spreadsheets	Tables and data
Images	JPEG, PNG, GIF, WebP	EXIF metadata + OCR
Audio	WAV, MP3	Metadata + transcription
HTML	Web pages	Clean conversion
CSV	Comma-separated values	Table format
JSON	JSON data	Structured representation
XML	XML documents	Structured format
ZIP	Archive files	Iterates contents
EPUB	E-books	Full text extraction
YouTube	Video URLs	Fetch transcriptions

Quick Start

Installation

# Install with all features
pip install 'markitdown[all]'

# Or from source
git clone https://github.com/microsoft/markitdown.git
cd markitdown
pip install -e 'packages/markitdown[all]'

Command-Line Usage

# Basic conversion
markitdown document.pdf > output.md

# Specify output file
markitdown document.pdf -o output.md

# Pipe content
cat document.pdf | markitdown > output.md

# Enable plugins
markitdown --list-plugins  # List available plugins
markitdown --use-plugins document.pdf -o output.md

Python API

from markitdown import MarkItDown

# Basic usage
md = MarkItDown()
result = md.convert("document.pdf")
print(result.text_content)

# Convert from stream
with open("document.pdf", "rb") as f:
    result = md.convert_stream(f, file_extension=".pdf")
    print(result.text_content)

Advanced Features

1. AI-Enhanced Image Descriptions

Use LLMs via OpenRouter to generate detailed image descriptions (for PPTX and image files):

from markitdown import MarkItDown
from openai import OpenAI

# Initialize OpenRouter client (OpenAI-compatible API)
client = OpenAI(
    api_key="your-openrouter-api-key",
    base_url="https://openrouter.ai/api/v1"
)

md = MarkItDown(
    llm_client=client,
    llm_model="your-vision-model",  # use any vision-capable model ID supported by your gateway
    llm_prompt="Describe this image in detail for scientific documentation"
)

result = md.convert("presentation.pptx")
print(result.text_content)

2. Azure Document Intelligence

For enhanced PDF conversion with Microsoft Document Intelligence:

# Command line
markitdown document.pdf -o output.md -d -e "<document_intelligence_endpoint>"

# Python API
from markitdown import MarkItDown

md = MarkItDown(docintel_endpoint="<document_intelligence_endpoint>")
result = md.convert("complex_document.pdf")
print(result.text_content)

3. Plugin System

MarkItDown supports 3rd-party plugins for extending functionality:

# List installed plugins
markitdown --list-plugins

# Enable plugins
markitdown --use-plugins file.pdf -o output.md

Find plugins on GitHub with hashtag: #markitdown-plugin

Optional Dependencies

Control which file formats you support:

# Install specific formats
pip install 'markitdown[pdf, docx, pptx]'

# All available options:
# [all]                  - All optional dependencies
# [pptx]                 - PowerPoint files
# [docx]                 - Word documents
# [xlsx]                 - Excel spreadsheets
# [xls]                  - Older Excel files
# [pdf]                  - PDF documents
# [outlook]              - Outlook messages
# [az-doc-intel]         - Azure Document Intelligence
# [audio-transcription]  - WAV and MP3 transcription
# [youtube-transcription] - YouTube video transcription

Common Use Cases

1. Convert Scientific Papers to Markdown

from markitdown import MarkItDown

md = MarkItDown()

# Convert PDF paper
result = md.convert("research_paper.pdf")
with open("paper.md", "w") as f:
    f.write(result.text_content)

2. Extract Data from Excel for Analysis

from markitdown import MarkItDown

md = MarkItDown()
result = md.convert("data.xlsx")

# Result will be in Markdown table format
print(result.text_content)

3. Process Multiple Documents

from markitdown import MarkItDown
import os
from pathlib import Path

md = MarkItDown()

# Process all PDFs in a directory
pdf_dir = Path("papers/")
output_dir = Path("markdown_output/")
output_dir.mkdir(exist_ok=True)

for pdf_file in pdf_dir.glob("*.pdf"):
    result = md.convert(str(pdf_file))
    output_file = output_dir / f"{pdf_file.stem}.md"
    output_file.write_text(result.text_content)
    print(f"Converted: {pdf_file.name}")

4. Convert PowerPoint with AI Descriptions

from markitdown import MarkItDown
from openai import OpenAI

# Use OpenRouter for access to multiple AI models
client = OpenAI(
    api_key="your-openrouter-api-key",
    base_url="https://openrouter.ai/api/v1"
)

md = MarkItDown(
    llm_client=client,
    llm_model="your-vision-model",  # use any vision-capable model ID supported by your gateway
    llm_prompt="Describe this slide image in detail, focusing on key visual elements and data"
)

result = md.convert("presentation.pptx")
with open("presentation.md", "w") as f:
    f.write(result.text_content)

5. Batch Convert with Different Formats

from markitdown import MarkItDown
from pathlib import Path

md = MarkItDown()

# Files to convert
files = [
    "document.pdf",
    "spreadsheet.xlsx",
    "presentation.pptx",
    "notes.docx"
]

for file in files:
    try:
        result = md.convert(file)
        output = Path(file).stem + ".md"
        with open(output, "w") as f:
            f.write(result.text_content)
        print(f"✓ Converted {file}")
    except Exception as e:
        print(f"✗ Error converting {file}: {e}")

6. Extract YouTube Video Transcription

from markitdown import MarkItDown

md = MarkItDown()

# Convert YouTube video to transcript
result = md.convert("https://www.youtube.com/watch?v=VIDEO_ID")
print(result.text_content)

Docker Usage

# Build image
docker build -t markitdown:latest .

# Run conversion
docker run --rm -i markitdown:latest < ./document.pdf > output.md

Best Practices

1. Choose the Right Conversion Method

• Simple documents: Use basic MarkItDown()
• Complex PDFs: Use Azure Document Intelligence
• Visual content: Enable AI image descriptions
• Scanned documents: Ensure OCR dependencies are installed

2. Handle Errors Gracefully

from markitdown import MarkItDown

md = MarkItDown()

try:
    result = md.convert("document.pdf")
    print(result.text_content)
except FileNotFoundError:
    print("File not found")
except Exception as e:
    print(f"Conversion error: {e}")

3. Process Large Files Efficiently

from markitdown import MarkItDown

md = MarkItDown()

# For large files, use streaming
with open("large_file.pdf", "rb") as f:
    result = md.convert_stream(f, file_extension=".pdf")
    
    # Process in chunks or save directly
    with open("output.md", "w") as out:
        out.write(result.text_content)

4. Optimize for Token Efficiency

Markdown output is already token-efficient, but you can:

• Remove excessive whitespace
• Consolidate similar sections
• Strip metadata if not needed

from markitdown import MarkItDown
import re

md = MarkItDown()
result = md.convert("document.pdf")

# Clean up extra whitespace
clean_text = re.sub(r'\n{3,}', '\n\n', result.text_content)
clean_text = clean_text.strip()

print(clean_text)

Integration with Scientific Workflows

Convert Literature for Review

from markitdown import MarkItDown
from pathlib import Path

md = MarkItDown()

# Convert all papers in literature folder
papers_dir = Path("literature/pdfs")
output_dir = Path("literature/markdown")
output_dir.mkdir(exist_ok=True)

for paper in papers_dir.glob("*.pdf"):
    result = md.convert(str(paper))
    
    # Save with metadata
    output_file = output_dir / f"{paper.stem}.md"
    content = f"# {paper.stem}\n\n"
    content += f"**Source**: {paper.name}\n\n"
    content += "---\n\n"
    content += result.text_content
    
    output_file.write_text(content)

# For AI-enhanced conversion with figures
from openai import OpenAI

client = OpenAI(
    api_key="your-openrouter-api-key",
    base_url="https://openrouter.ai/api/v1"
)

md_ai = MarkItDown(
    llm_client=client,
    llm_model="your-vision-model",  # use any vision-capable model ID supported by your gateway
    llm_prompt="Describe scientific figures with technical precision"
)

Extract Tables for Analysis

from markitdown import MarkItDown
import re

md = MarkItDown()
result = md.convert("data_tables.xlsx")

# Markdown tables can be parsed or used directly
print(result.text_content)

Troubleshooting

Common Issues

1. Missing dependencies: Install feature-specific packages

   pip install 'markitdown[pdf]'  # For PDF support
   

2. Binary file errors: Ensure files are opened in binary mode

   with open("file.pdf", "rb") as f:  # Note the "rb"
       result = md.convert_stream(f, file_extension=".pdf")
   

3. OCR not working: Install tesseract

   # macOS
   brew install tesseract
   
   # Ubuntu
   sudo apt-get install tesseract-ocr
   

Performance Considerations

• PDF files: Large PDFs may take time; consider page ranges if supported
• Image OCR: OCR processing is CPU-intensive
• Audio transcription: Requires additional compute resources
• AI image descriptions: Requires API calls (costs may apply)

Next Steps

• See references/api_reference.md for complete API documentation
• Check references/file_formats.md for format-specific details
• Review scripts/batch_convert.py for automation examples
• Explore scripts/convert_with_ai.py for AI-enhanced conversions

Resources

• MarkItDown GitHub: https://github.com/microsoft/markitdown
• PyPI: https://pypi.org/project/markitdown/
• OpenRouter: https://openrouter.ai (for AI-enhanced conversions)
• OpenRouter API Keys: https://openrouter.ai/keys
• OpenRouter Models: https://openrouter.ai/models
• MCP Server: markitdown-mcp (for AI assistant desktop integration)
• Plugin Development: See packages/markitdown-sample-plugin

When to Use

Use this skill when the request matches the capability, constraints, and activation cues described below.

Core Workflow

Follow the primary workflow, commands, and decision points documented in the sections below.

Examples

Use the examples and snippets already present in this document whenever they apply to the task.

References

Use any linked scripts, assets, reference files, and companion resources mentioned in this document.