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 Describe what you want to accomplish Provide any required input data or files 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 Use progress bars (tqdm) for user feedback Implement checkpointing for long jobs Set reasonable worker counts (CPU cores) 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