Skill: leowing/batch-processor-1-0-0 (version 1.0.0)

--- name: batch-processor 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)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)