OpenParable
Skip to content
← Back to skills

Batch Processor

Documents
batch-processor

Batch Processor is an Agent Skill for documents published in the claude-office-skills/skills repository on GitHub: a SKILL.md instruction file an AI agent can load to do this task.

Process multiple documents in bulk with parallel execution

View on GitHub ↗
QUICK START

How to use this skill

Bring this guide into your coding agent with a prompt tailored to the tool you use.

  1. Open your project in Codex.
  2. Copy and paste the prompt below.
  3. Review files and risks before approving any action.
Prompt to paste
Inspect the Agent Skill at https://github.com/claude-office-skills/skills/blob/HEAD/batch-processor/SKILL.md and all supporting files. Report provenance, permissions, scripts, dependencies, and security risks. Propose an exact installation into .agents/skills/batch-processor, but wait for my approval before writing files or running scripts.

Copying this prompt does not install or run the skill. Review third-party files before use.

Batch Processor Skill

Overview

This skill enables efficient bulk processing of documents - convert, transform, extract, or analyze hundreds of files with parallel execution and progress tracking.

How to Use

  1. Describe what you want to accomplish
  2. Provide any required input data or files
  3. I'll execute the appropriate operations

Example prompts:

  • "Convert 100 PDFs to Word documents"
  • "Extract text from all images in a folder"
  • "Batch rename and organize files"
  • "Mass update document headers/footers"

Domain Knowledge

Batch Processing Patterns

Input: [file1, file2, ..., fileN]
         │
         ▼
    ┌─────────────┐
    │  Parallel   │  ← Process multiple files concurrently
    │  Workers    │
    └─────────────┘
         │
         ▼
Output: [result1, result2, ..., resultN]

Python Implementation

from concurrent.futures import ProcessPoolExecutor, as_completed
from pathlib import Path
from tqdm import tqdm

def process_file(file_path: Path) -> dict:
    """Process a single file."""
    # Your processing logic here
    return {"path": str(file_path), "status": "success"}

def batch_process(input_dir: str, pattern: str = "*.*", max_workers: int = 4):
    """Process all matching files in directory."""
    
    files = list(Path(input_dir).glob(pattern))
    results = []
    
    with ProcessPoolExecutor(max_workers=max_workers) as executor:
        futures = {executor.submit(process_file, f): f for f in files}
        
        for future in tqdm(as_completed(futures), total=len(files)):
            file = futures[future]
            try:
                result = future.result()
                results.append(result)
            except Exception as e:
                results.append({"path": str(file), "error": str(e)})
    
    return results

# Usage
results = batch_process("/documents/invoices", "*.pdf", max_workers=8)
print(f"Processed {len(results)} files")

Error Handling & Resume

import json
from pathlib import Path

class BatchProcessor:
    def __init__(self, checkpoint_file: str = "checkpoint.json"):
        self.checkpoint_file = checkpoint_file
        self.processed = self._load_checkpoint()
    
    def _load_checkpoint(self):
        if Path(self.checkpoint_file).exists():
            return json.load(open(self.checkpoint_file))
        return {}
    
    def _save_checkpoint(self):
        json.dump(self.processed, open(self.checkpoint_file, "w"))
    
    def process(self, files: list, processor_func):
        for file in files:
            if str(file) in self.processed:
                continue  # Skip already processed
            
            try:
                result = processor_func(file)
                self.processed[str(file)] = {"status": "success", **result}
            except Exception as e:
                self.processed[str(file)] = {"status": "error", "error": str(e)}
            
            self._save_checkpoint()  # Resume-safe

Best Practices

  1. Use progress bars (tqdm) for user feedback
  2. Implement checkpointing for long jobs
  3. Set reasonable worker counts (CPU cores)
  4. Log failures for later review

Installation

# Install required dependencies
pip install python-docx openpyxl python-pptx reportlab jinja2

Resources