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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.
Basierend auf der SOC-Berufsklassifikation
Deterministically query 78 public scientific, biomedical, materials science, regulatory, finance, and demographics databases through documented REST APIs. Use for reproducible lookups of compounds, genes, proteins, pathways, variants, clinical trials, patents, economic indicators, structures, astronomy objects, environmental records, or database-backed scientific facts when endpoints, filters, pagination, and provenance need to be explicit.
Hugging Face Transformers for loading Hub models, running pipeline inference, text generation, and Trainer fine-tuning on NLP, vision, audio, and multimodal tasks. Use when working with AutoModel, pipelines, tokenizers, or TrainingArguments—not for general ML outside the Transformers library.
Autonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting — e.g. "get my model's eval score up", "improve this agent/harness", "tune this pipeline", "beat the baseline on this benchmark", "run a search over approaches and keep the best", "do an MLE-bench / Kaggle-style optimization", or any long-horizon "make this artifact better and don't just memorize the dev set" task. Trigger it even when the user doesn't say "Arbor" or "hypothesis tree" but describes repeated experiment-and-evaluate loops, branching exploration of competing ideas, or worries about a dev/test gap. Runs Claude itself as the coordinator with subagent executors in isolated git worktrees; for the standalone `arbor` CLI tool see references/arbor-upstream.md.
Standard single-cell RNA-seq analysis pipeline. Use for QC, normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression, visualization, and converting R-friendly single-cell formats such as Seurat or SingleCellExperiment RDS files into h5ad for Scanpy. Best for exploratory scRNA-seq analysis with established workflows. For deep learning models use scvi-tools; for data format questions use anndata.
Complete mass spectrometry analysis platform. Use for proteomics and metabolomics workflows—feature detection, peptide/protein identification, label-free and isobaric quantification, adduct/accurate-mass annotation, and complex LC-MS/MS pipelines. Supports extensive file formats and algorithms. For simple spectral comparison and small-molecule library matching use matchms.
Design experiments and studies BEFORE data is collected — choosing a design, randomizing, blocking, and laying out treatment combinations so the results will actually be interpretable. Use whenever someone is planning a study, asks how to assign subjects/samples to groups, mentions randomization, blocking, stratification, controls, factorial or fractional-factorial designs, design of experiments (DOE), screening many factors, response-surface optimization, crossover or repeated-measures or split-plot designs, cluster/group randomization, Latin squares, plate layouts, batch/run-order effects, replication vs. pseudoreplication, or sequential/adaptive/group-sequential designs. Trigger this even for informal phrasings like "how should I set up this experiment", "how do I avoid confounding", "what's the best way to test these 6 factors", or "assign these mice to conditions". For computing the sample size or power once the design is chosen, use statistical-power; for analyzing data already collected, use statistica
| 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 license |
| required_environment_variables | [{"name":"OPENROUTER_API_KEY","prompt":"OpenRouter API key for the skill's LLM-powered steps.","required_for":"optional features"}] |
| metadata | {"version":"1.1","skill-author":"K-Dense Inc.","openclaw":{"primaryEnv":"OPENROUTER_API_KEY","envVars":[{"name":"OPENROUTER_API_KEY","required":false,"description":"OpenRouter API key for the skill's LLM-powered steps."}]}} |
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:
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:
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:
When to add schematics:
For detailed guidance on creating schematics, refer to the scientific-schematics skill documentation.
| Format | Description | Notes |
|---|---|---|
| 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 |
# 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)
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="anthropic/claude-opus-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)
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)
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
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
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-opus-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
MarkItDown()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)
Markdown output is already token-efficient, but you can:
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-opus-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)
Missing dependencies: Install feature-specific packages
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
references/api_reference.md for complete API documentationreferences/file_formats.md for format-specific detailsscripts/batch_convert.py for automation examplesscripts/convert_with_ai.py for AI-enhanced conversionspackages/markitdown-sample-plugin