batch-processor

name	batch-processor
description	Process multiple documents in bulk with parallel execution
version	1.0
author	claude-office-skills
license	MIT
category	workflow
tags	["batch","processor","bulk","automation"]
department	All
models	{"recommended":["claude-sonnet-4","claude-opus-4"],"compatible":["claude-3-5-sonnet","gpt-4","gpt-4o"]}
mcp	{"server":"office-mcp","tools":["batch_convert"]}
capabilities	["batch_processing","automation"]
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  f  files}
        
         future  tqdm(as_completed(futures), total=(files)):
            file = futures[future]
            :
                result = future.result()
                results.append(result)
             Exception  e:
                results.append({: (file), : (e)})
    
     results


results = batch_process(, , max_workers=)
()