---
name: batch-processor-2ecd420a1976
description: Process multiple documents in bulk with parallel execution
author: claude-office-skills
version: "1.0"
tags: ['batch', 'bulk', 'parallel', 'automation']
models: [claude-sonnet-4, claude-opus-4]
tools: [computer, code_execution, file_operations]
library:
  name: Custom
  url: https://github.com/claude-office-skills/skills
  stars: N/A
---

# 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

```python
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

```python
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

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

## Resources

- [Custom Repository](https://github.com/claude-office-skills/skills)
- [Claude Office Skills Hub](https://github.com/claude-office-skills/skills)


---
Source attribution: modbender
Original project: https://github.com/modbender/skill-library-mcp/blob/d9c8fe903b5b4e82237dbe8d0df8567783022e18/data/batch-processor/SKILL.md
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Dave AI.

## Upstream license notice

MIT License

Copyright (c) 2025 skill-library-mcp contributors

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Dave AI resource metadata

Creator / publisher: modbender
Original project: https://github.com/modbender/skill-library-mcp/blob/d9c8fe903b5b4e82237dbe8d0df8567783022e18/data/batch-processor/SKILL.md
Attribution basis: Source repository record

Fields of work: Content & Media, Data & Analytics, Development & Engineering
Roles: Backend Developer, Data Analyst, Technical Writer
Specialties: API, Backend
Tools: Jinja2, Openpyxl, Python Docx, Python Pptx, Reportlab
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: 5c34e129240abf8ddc97b6835990cf879103433a7ab08528b4e0541a00affe3c