A professional ETL skill for converting unstructured construction PDFs into machine-readable Excel, CSV, and JSON formats.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install pdf-to-structured
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 pdf-to-structured 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 PDF to Structured Data Extraction?
This skill provides a robust framework for transforming static PDF documentation into actionable data. Based on the DDC methodology, it implements an ETL (Extract, Transform, Load) pattern specifically designed for the construction industry. By leveraging Openclaw Skills, users can bridge the gap between unstructured documents like specifications or reports and structured analysis tools. It combines the precision of pdfplumber for native files with the power of Tesseract OCR for scanned documents, ensuring that no data remains trapped in a static format.
PDF to Structured Data Extraction Use Cases
- Extracting Bill of Materials (BOM) from complex engineering specifications for procurement.
- Converting project schedules and Gantt chart tables into pandas-compatible DataFrames.
- Digitizing scanned site reports and legacy paper documentation into searchable databases.
- Automated parsing of technical section numbers and titles from master specifications.
- Batch processing entire directories of PDF documents into a single consolidated Excel workbook.
How PDF to Structured Data Extraction Works
- The Extraction phase initiates by loading the PDF using either native text extraction or by converting pages to images for OCR processing.
- During the Transformation phase, the skill parses the document structure, identifying tables, layout patterns, and specific regions defined by bounding boxes.
- Construction-specific logic is applied to recognize common headers like Task, Duration, or Item Quantity to categorize the data correctly.
- A data cleaning layer automatically strips whitespace, handles column misalignment, and converts numeric strings into usable data types.
- The Loading phase completes the workflow by exporting the structured results into the user's preferred format, such as Excel, CSV, or JSON.
PDF to Structured Data Extraction Setup
To begin using these PDF processing Openclaw Skills, install the required Python environment:
# Core libraries for native PDF and data handling
pip install pdfplumber pandas openpyxl
# OCR libraries for scanned document support
pip install pytesseract pdf2image pypdf
Note: For OCR functionality, you must also have the Tesseract OCR engine installed on your system.
PDF to Structured Data Extraction Data Schema & Taxonomy
The skill organizes extracted data into specialized schemas based on the document type:
| Feature | Extraction Logic | Data Output |
|---|---|---|
| Tables | Coordinate-based cell detection | Excel / Pandas DataFrame |
| BOMs | Keyword-aware header matching | CSV / JSON |
| Schedules | Date and Task parsing | JSON / Excel |
| Scanned OCR | Tesseract image-to-string | Plain Text / CSV |
| Batch | File-system globbing | Consolidated Excel |
PDF to Structured Data Extraction Advanced Features
- Position-aware extraction using bounding boxes (bbox) to target specific areas of engineering drawings.
- Visual debugging support to draw rectangles over detected table boundaries for validation.
- Multi-page table concatenation to handle long specifications spanning several sheets.
- Automated data validation pipelines to check for extraction quality and missing values.
- Support for multiple export formats including JSON Lines (JSONL) for high-volume data ingestion.
SKILL.md
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