Alterlab markitdown

Skill AlterLab-IEU/AlterLab-Academic-Skills/skills/document-tools/alterlab-markitdown

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Install

npx -y skills add AlterLab-IEU/AlterLab-Academic-Skills --skill alterlab-markitdown

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What its author says it does

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Convert files and Office documents to clean, LLM-friendly Markdown with Microsoft MarkItDown (markitdown CLI/Python), supporting PDF, DOCX, PPTX, XLSX, images (EXIF + OCR), audio (transcription), HTML, CSV, JSON, XML, ZIP archives, EPUB e-books, and YouTube transcript URLs, with optional AI image descriptions. Use when converting a document, PDF, slide deck, spreadsheet, scanned image, audio file, web page, or e-book into Markdown text for ingestion or LLM processing, extracting text via OCR, transcribing audio, or batch-converting mixed file formats to token-efficient Markdown. Part of the AlterLab Academic Skills suite.

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SKILL.md

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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

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 (uv-first; uv pip works inside an active venv)
uv pip install 'markitdown[all]'

# Or run the CLI ad hoc without installing into the project env
uvx --from 'markitdown[all]' markitdown document.pdf -o output.md

# Or from source
git clone https://github.com/microsoft/markitdown.git
cd markitdown
uv 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):

import os
from markitdown import MarkItDown
from openai import OpenAI

# Model ID follows the ALTERLAB_MODEL convention (skills/core/shared/model_env.md):
# read $ALTERLAB_MODEL, else the dated default (reviewed 2026-06-06). OpenRouter needs
# the "provider/" prefix, so the default is the dated Anthropic ID with that prefix.
model = os.environ.get("ALTERLAB_MODEL") or "anthropic/claude-opus-4-8"

# Initialize OpenRouter client (OpenAI-compatible API)
client = OpenAI(
    api_key=os.environ["OPENROUTER_API_KEY"],
    base_url="https://openrouter.ai/api/v1"
)

md = MarkItDown(
    llm_client=client,
    llm_model=model,  # 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:

# 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 (no spaces inside the bracket — pip parses 'pdf, docx' as bad names)
uv 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

import os
from markitdown import MarkItDown
from openai import OpenAI

# Model ID via the ALTERLAB_MODEL convention (skills/core/shared/model_env.md):
# $ALTERLAB_MODEL, else the dated default (reviewed 2026-06-06), with OpenRouter prefix.
model = os.environ.get("ALTERLAB_MODEL") or "anthropic/claude-opus-4-8"

# Use OpenRouter for access to multiple AI models
client = OpenAI(
    api_key=os.environ["OPENROUTER_API_KEY"],
    base_url="https://openrouter.ai/api/v1"
)

md = MarkItDown(
    llm_client=client,
    llm_model=model,  # 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

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
import os
from openai import OpenAI

# Model ID via the ALTERLAB_MODEL convention (skills/core/shared/model_env.md).
model = os.environ.get("ALTERLAB_MODEL") or "anthropic/claude-opus-4-8"

client = OpenAI(
    api_key=os.environ["OPENROUTER_API_KEY"],
    base_url="https://openrouter.ai/api/v1"
)

md_ai = MarkItDown(
    llm_client=client,
    llm_model=model,
    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

Missing dependencies: Install feature-specific packages
```
uv pip install 'markitdown[pdf]'  # For PDF support
```

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")

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