A high-performance document parsing skill that converts URLs, PDFs, and office files into clean, structured Markdown or text using GPU-accelerated ML models.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install docling
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install docling using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Docling Document & Web Content Extraction?
Docling is a specialized tool within the Openclaw Skills ecosystem designed for deep content extraction and document understanding. Unlike standard web scrapers that may miss context, Docling utilizes machine learning and OCR to handle complex layouts in PDFs, PowerPoint presentations, and images. It provides a superior alternative to basic fetching tools when you need clean, structured data for LLM consumption.
By leveraging GPU acceleration via CUDA, Docling ensures that even the most document-heavy tasks are processed rapidly. This makes it a cornerstone for sophisticated Openclaw Skills workflows where accuracy and speed in document parsing are critical for downstream reasoning and data analysis.
Docling Document & Web Content Extraction Use Cases
- Extracting clean text from a specific URL to feed into an LLM context.
- Converting complex PDF reports or academic papers into searchable Markdown.
- Parsing Excel or CSV data into structured formats for automated data analysis.
- Performing high-accuracy OCR on scanned images or document screenshots.
- Replacing standard web fetching with structured extraction for better RAG performance within Openclaw Skills.
How Docling Document & Web Content Extraction Works
- The AI agent receives a URL or local file path and identifies the source format.
- The Docling CLI is invoked with specific flags for the desired output format, such as Markdown, JSON, or plain text.
- Optional GPU acceleration via CUDA is engaged for heavy OCR or document layout analysis tasks.
- The skill saves the parsed content into a designated output directory or temporary file path.
- The agent reads the structured output to perform further reasoning, synthesis, or storage tasks.
Docling Document & Web Content Extraction Setup
To integrate this into your environment for Openclaw Skills, ensure you have the Docling CLI installed via pipx or a similar manager.
pipx install docling
# Optional: Verify GPU support for CUDA acceleration
python -c "import torch; print(torch.cuda.is_available())"
Basic command usage for extracting web content:
docling "<URL>" --from html --to md --output /tmp/docling_out
Docling Document & Web Content Extraction Data Schema & Taxonomy
Docling organizes data based on source and destination formats, providing high-fidelity metadata and structural preservation. This is a core advantage for users of Openclaw Skills.
| Feature | Details |
|---|---|
| Source Formats | URL, PDF, DOCX, PPTX, XLSX, Images, Markdown, CSV |
| Output Formats | Markdown (md), Text (text), JSON, YAML, HTML |
| Accelerators | Auto, CPU, or CUDA (NVIDIA GPU) |
| Table Handling | Automatic extraction and formatting of complex tables |
| Metadata | Extraction of document structure and headers |
Docling Document & Web Content Extraction Advanced Features
- Multi-format conversion including CSV and XLSX into readable Markdown tables.
- Full OCR capabilities for scanned documents and images using the --ocr flag.
- Customizable hardware acceleration settings to optimize performance based on available hardware.
- Detailed table extraction specifically designed to handle complex grid layouts and merged cells.
- Seamless integration with wider Openclaw Skills for automated document-to-knowledge base workflows.
SKILL.md
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