davila7/claude-code-templates/markitdown

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

# 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]'

# 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

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)

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)

# 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)

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

# 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

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)

from markitdown import MarkItDown
md = MarkItDown()
result = md.convert("data.xlsx")
# Result will be in Markdown table format
print(result.text_content)

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}")

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)

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}")

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)

# Build image
docker build -t markitdown:latest .
 
# Run conversion
docker run --rm -i markitdown:latest < ~/document.pdf > output.md

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}")

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)

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)

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"
)

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)