A high-performance automation skill for bulk document transformation, extraction, and analysis using parallel execution.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install batch-processor-1-0-0
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 batch-processor-1-0-0 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 Batch Processor?
The Batch Processor is a specialized tool within the Openclaw Skills ecosystem designed to handle high-volume document tasks with efficiency and reliability. It leverages parallel execution and worker pools to process hundreds of files concurrently, significantly reducing the latency associated with repetitive data operations.
Whether you are dealing with file conversions, text extraction from image libraries, or bulk metadata updates, this skill provides the infrastructure needed for scalable automation. It is particularly valuable for developers and power users who need to transform large datasets or automate complex office-related file workflows using advanced Python-based execution patterns.
Batch Processor Use Cases
- Converting hundreds of PDFs into editable Microsoft Word documents or text files.
- Extracting text data from a folder containing numerous image files via automated OCR processes.
- Mass renaming and organizing files based on specific naming conventions or internal metadata.
- Updating headers, footers, or branding elements across a large library of office documents.
- Processing bulk invoice data for extraction and generation of structured analytical reports.
How Batch Processor Works
- The user provides a high-level description of the batch task and identifies the source directory or file set.
- The skill initializes a parallel processing worker pool based on the system's CPU capabilities to maximize throughput.
- Input files are mapped to specific worker processes, executing the logic for conversion, extraction, or transformation concurrently.
- A real-time progress bar (tqdm) provides constant feedback to the user regarding the operation status.
- The skill implements a checkpointing system to handle errors gracefully and ensure tasks can be resumed from the last successful file if interrupted.
Batch Processor Setup
To get started with this powerful entry in the Openclaw Skills collection, you must install the necessary Python dependencies for document manipulation and parallel execution. Run the following command in your terminal:
pip install python-docx openpyxl python-pptx reportlab jinja2 tqdm
Batch Processor Data Schema & Taxonomy
The Batch Processor organizes its workflow using a structured metadata approach to ensure data integrity during bulk operations:
| Component | Description | Format |
|---|---|---|
| Input Directory | The source folder containing files to be processed. | Filesystem Path |
| Checkpoint File | A JSON file tracking processed files to enable job resumption. | checkpoint.json |
| Processed Data | The resulting structured data from individual file operations. | JSON/Dictionary |
| Output Directory | The destination folder for transformed or extracted files. | Filesystem Path |
Batch Processor Advanced Features
- Multi-core parallel execution using Python's ProcessPoolExecutor for maximum hardware utilization.
- Robust checkpointing system that allows long-running jobs to resume without re-processing already completed files.
- Integrated progress tracking via tqdm to provide granular visibility into bulk processing tasks.
- Extensible worker logic that allows users to define custom processing functions within the Openclaw Skills framework for specialized data extraction.
SKILL.md
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