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k-dense-ai/claude-scientific-writer/markitdown

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version: "1.0.2" name: markitdown description: "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." allowed-tools: [Read, Write, Edit, Bash] license: MIT source: https://github.com/microsoft/markitdown

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:

bash

python <path-to-scientific-schematics-skill>/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.

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

bash

# 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

bash

# 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

python

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):

python

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="anthropic/claude-sonnet-4.5",  # recommended for scientific vision
    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:

bash

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

python

# 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:

bash

# 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:

bash

# 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

python

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

python

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

python

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

python

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="anthropic/claude-sonnet-4.5",  # recommended for presentations
    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

python

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

python

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

bash

# 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

python

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

python

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

python

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

python

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="anthropic/claude-sonnet-4.5",
    llm_prompt="Describe scientific figures with technical precision"
)

Extract Tables for Analysis

python

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

Missing dependencies: Install feature-specific packages

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

Binary file errors: Ensure files are opened in binary mode

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

OCR not working: Install tesseract

```bash # 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 Claude Desktop integration)
Plugin Development: See packages/markitdown-sample-plugin

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