batch-processor

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CLAUDE OFFICE SKILL - Enhanced Metadata v2.0

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Basic Information

name: batch-processor description: "Process multiple documents in bulk with parallel execution" version: "1.0" author: claude-office-skills license: MIT

Categorization

category: workflow tags:

• batch
• processor
• bulk
• automation department: All

AI Model Compatibility

models: recommended: - claude-sonnet-4 - claude-opus-4 compatible: - claude-3-5-sonnet - gpt-4 - gpt-4o

MCP Tools Integration

mcp: server: office-mcp tools: - batch_convert

Skill Capabilities

capabilities:

• batch_processing
• automation

Language Support

languages: - en - zh

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

• Custom Repository
• Claude Office Skills Hub