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markitdown

markitdown

github.com/rysweetDocuments76115 installsUpdated 22w ago
Repository

About markitdown

markitdown is Microsoft's document-to-Markdown tool and skill for developers and LLM workflows that extract or analyze document content. It handles varied document, table, audio, and image formats while preserving structures such as headings, lists, tables, and links.

Key features

  1. 01Convert PDF, Office, HTML, and other formats
  2. 02Extract text from images or generate descriptions
  3. 03Transcribe audio content
  4. 04Preserve headings, lists, tables, and links
  5. 05Extend converters through a plugin system

Use cases

  1. 01Convert reports and presentations to Markdown
  2. 02Extract tabular data from spreadsheets
  3. 03Batch-convert documents in a directory
  4. 04Prepare text for LLM content analysis

MCP support

The project describes an MCP server integration for LLM applications; this skill input does not specify its MCP tools.

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/rysweet/amplihack --skill markitdown

After installing, type/markitdownin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

examples.md
patterns.md
reference.md

Details

From the project README

Document to Markdown Conversion

Overview

Convert various document formats to clean Markdown using Microsoft's MarkItDown tool. Optimized for LLM processing, content extraction, and document analysis workflows.

Supported Formats: PDF, Word (.docx), PowerPoint (.pptx), Excel (.xlsx/.xls), Images (with OCR/LLM), HTML, Audio (with transcription), CSV, JSON, XML, ZIP archives, EPubs

Quick Start

Basic Usage
from markitdown import MarkItDown

md = MarkItDown()
result = md.convert("document.pdf")
print(result.text_content)
Command Line
# Convert single file
markitdown document.pdf > output.md
markitdown document.pdf -o output.md

# Pipe input
cat document.pdf | markitdown

🔒 Security Considerations

Before using in production:

  • ✅ Validate file types (MIME, not extension)
  • ✅ Limit file sizes (prevent DoS)
  • ✅ Sanitize file paths (prevent traversal)
  • ✅ Protect API keys (never hardcode)
  • ✅ Consider data privacy (external services)

See patterns.md for implementation details.

API Key Security

❌ NEVER:

  • Hardcode keys in code
  • Commit .env files to git
  • Log environment variables

✅ ALWAYS:

  • Use environment variables: export OPENAI_API_KEY="sk-..." # pragma: allowlist secret
  • Use secret management (AWS Secrets Manager, Azure Key Vault)
  • Rotate keys regularly

Common Patterns

PDF Documents
# Basic PDF conversion
md = MarkItDown()
result = md.convert("report.pdf")

# With Azure Document Intelligence (better quality)
md = MarkItDown(docintel_endpoint="<your-endpoint>")
result = md.convert("report.pdf")
Office Documents
# Word documents - preserves structure
result = md.convert("document.docx")

# Excel - converts tables to markdown tables
result = md.convert("spreadsheet.xlsx")

# PowerPoint - extracts slide content
result = md.convert("presentation.pptx")
Images with Descriptions
# ✅ SECURE: Using environment variables for API keys
import os
from openai import OpenAI

api_key = os.getenv("OPENAI_API_KEY")
if not api_key:
    raise RuntimeError("OPENAI_API_KEY not set")

client = OpenAI(api_key=api_key)
md = MarkItDown(llm_client=client, llm_model="gpt-4o")
result = md.convert("diagram.jpg")  # Gets AI-generated description
Batch Processing
from pathlib import Path

md = MarkItDown()
documents = Path(".").glob("*.pdf")

for doc in documents:
    result = md.convert(str(doc))
    output_path = doc.with_suffix(".md")
    output_path.write_text(result.text_content)

Installation

# Full installation (all features)
pip install 'markitdown[all]'

# Selective features
pip install 'markitdown[pdf, docx, pptx]'

Requirements: Python 3.10 or higher

Key Features

  • Structure Preservation: Maintains headings, lists, tables, links
  • Plugin System: Extend with custom converters
  • Docker Support: Containerized deployments
  • MCP Integration: Model Context Protocol server for LLM apps

When to Read Supporting Files

  • reference.md - Read when you need:

    • Complete API reference and all configuration options
    • Azure Document Intelligence integration details
    • Plugin development guide
    • Docker and MCP server setup
    • Troubleshooting and error handling
  • examples.md - Read when you need:

    • Working examples for specific file types
    • Batch processing workflows
    • Error handling patterns
    • Integration with existing pipelines
  • patterns.md - Read when you need:

    • Production deployment patterns
    • Performance optimization strategies
    • Security considerations
    • Anti-patterns to avoid

Quick Reference

File TypeUse CaseCommand
PDFReports, papersmd.convert("file.pdf")
WordDocumentsmd.convert("file.docx")
ExcelData tablesmd.convert("file.xlsx")
PowerPointPresentationsmd.convert("file.pptx")
ImagesDiagrams with OCRmd = MarkItDown(llm_client=client); md.convert("img.jpg")
HTMLWeb pagesmd.convert("page.html")
ZIPArchivesmd.convert("archive.zip") - processes contents

⚠️ Common Mistakes to Avoid

Anti-Pattern 1: Hardcoded API Keys

# ❌ NEVER DO THIS
md = MarkItDown(llm_client=OpenAI(api_key="sk-hardcoded-key"))

# ✅ ALWAYS DO THIS
api_key = os.getenv("OPENAI_API_KEY")
md = MarkItDown(llm_client=OpenAI(api_key=api_key))

Anti-Pattern 2: Unvalidated File Paths

# ❌ Vulnerable to path traversal
user_input = "../../../etc/passwd"
md.convert(user_input)

# ✅ Validate and sanitize
from pathlib import Path
safe_path = Path(user_input).resolve()
if not safe_path.is_relative_to(allowed_dir):
    raise ValueError("Invalid path")
md.convert(str(safe_path))

Anti-Pattern 3: Ignoring File Size Limits

# ❌ Can cause DoS
md.convert("huge_file.pdf")  # No size check

# ✅ Check size first
max_size = 50 * 1024 * 1024  # 50MB
if Path("file.pdf").stat().st_size > max_size:
    raise ValueError("File too large")

Common Issues

Import Error: Ensure Python >= 3.10 and markitdown installed Missing Dependencies: Install with pip install 'markitdown[all]' Image Descriptions Not Working: Requires LLM client (OpenAI or compatible)

For detailed troubleshooting, see reference.md.

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