An intelligent PDF data extraction tool that transforms complex product labels into structured Excel spreadsheets with high precision.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install my-pdf-extract-skill
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 my-pdf-extract-skill 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 Label Data Extractor?
This skill is designed to automate the tedious process of manual data entry from PDF documents. By leveraging powerful libraries like pdfplumber and pandas, it intelligently identifies data blocks within PDF files to extract critical information such as product codes, multi-line product names, batches, and quantities. It is particularly effective for high-precision scenarios where exact matching of product nomenclature is required. This utility is a part of the Openclaw Skills ecosystem, providing developers and data analysts with a reliable way to bridge the gap between unstructured PDF reports and structured Excel data.
PDF Label Data Extractor Use Cases
- Converting PDF-based product labels into structured digital datasets.
- Processing complex PDF documents containing multi-line product descriptions.
- Generating consolidated Excel reports from various PDF sources.
- Ensuring high-precision matching of product codes and names against predefined lists.
How PDF Label Data Extractor Works
- The script loads the target PDF file using the pdfplumber engine to ensure text-layer access.
- It scans the document text to identify specific data blocks based on predefined patterns and regular expressions.
- Multi-line product names are intelligently concatenated to ensure data integrity and readability.
- Extracted data is validated against a list of known product identifiers to ensure accuracy and prevent noise.
- The final structured dataset is compiled into a pandas DataFrame and exported as a professional Excel file for business use.
PDF Label Data Extractor Setup
To get started with this component of Openclaw Skills, ensure you have Python 3.8 or higher installed. Follow these steps to prepare your environment:
# Install the necessary Python dependencies
pip install pdfplumber pandas openpyxl
# Navigate to your project directory and activate the virtual environment
source ../venv/bin/activate
# Configure the file paths in extract_exact.py and execute the extraction
python extract_exact.py
PDF Label Data Extractor Data Schema & Taxonomy
The skill processes input PDFs and generates structured Excel outputs with the following metadata taxonomy:
| Field | Description | Type |
|---|---|---|
| CODIGO | Unique product identification code | String |
| NAME | Full product name, including reconstructed multi-line text | String |
| BATCH | Specific production batch or lot number | String |
| QUANTITY | Numerical count of the items extracted from the label | Integer |
PDF Label Data Extractor Advanced Features
- Custom Regex Support: Easily modify regular expressions within the extraction function to adapt to different PDF layouts and formatting.
- Dynamic Name Matching: Update the internal lookup list to handle evolving product catalogs and specific nomenclature requirements.
- Multi-Agent Compatibility: Specifically designed to function within a larger Openclaw Skills pipeline for automated document processing and ingestion.
- Automated Data Validation: Includes built-in checks to verify row formatting, ensuring that items like product codes and quantities are captured correctly.
SKILL.md
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