superpowers-lab

## Experimental Feature: Batch File Processing with Parallel Execution

**Scenario**: You need to process 1,000 large CSV files, each containing 50,000 rows of sales data, to extract specific metrics.

**Solution**: Utilize Claude's Code Superpowers to implement parallel processing with the following steps:

1. **Initial Setup**:
   ```python
   import pandas as pd
   from concurrent.futures import ThreadPoolExecutor
   
   def process_file(file_path):
       df = pd.read_csv(file_path)
       # Add your processing logic here
       return processed_data
   
   file_paths = [f'data/file_{i}.csv' for i in range(1000)]
   ```

2. **Parallel Execution**:
   ```python
   with ThreadPoolExecutor(max_workers=10) as executor:
       results = list(executor.map(process_file, file_paths))
   ```

3. **Output Handling**:
   - Combine results into a single DataFrame
   - Generate summary statistics
   - Save final output to a new CSV file

**Note**: Adjust the number of workers based on your system's capabilities to avoid overloading resources.