fastcampus-ai-agent-vibecoding

```python
# FastCampus AI Agent & Vibe Coding - Weekly Sales Report Automation
# Generated: 2024-05-20 | Version: 1.2
# Author: AI Agent

import os
import json
import logging
from datetime import datetime, timedelta
from typing import Dict, List
import requests
from fastcampus_ai_agent import FastCampusAgent

# Configure logging
logging.basicConfig(
    filename='/var/log/fastcampus/weekly_sales_report.log',
    level=logging.INFO,
    format='%(asctime)s - %(levelname)s - %(message)s'
)

class SalesReportAutomation(FastCampusAgent):
    def __init__(self):
        super().__init__()
        self.api_endpoint = os.getenv('SALES_API_ENDPOINT', 'https://api.fastcampus.com/sales')
        self.api_key = os.getenv('SALES_API_KEY')
        self.report_date = (datetime.now() - timedelta(days=7)).strftime('%Y-%m-%d')
        
    def fetch_sales_data(self) -> Dict:
        """Fetch sales data from FastCampus API with error handling"""
        try:
            headers = {'Authorization': f'Bearer {self.api_key}'}
            params = {'start_date': self.report_date, 'end_date': datetime.now().strftime('%Y-%m-%d')}
            response = requests.get(f'{self.api_endpoint}/weekly', headers=headers, params=params, timeout=30)
            response.raise_for_status()
            return response.json()
        except requests.exceptions.Timeout:
            logging.error('API timeout occurred while fetching sales data')
            raise
        except requests.exceptions.RequestException as e:
            logging.error(f'API request failed: {str(e)}')
            raise
    
    def format_report(self, data: Dict) -> str:
        """Format sales data into a readable report"""
        report_lines = [
            f"FastCampus Weekly Sales Report - {self.report_date} to {datetime.now().strftime('%Y-%m-%d')}",
            "="*60,
            f"Total Revenue: ${data['total_revenue']:,.2f}",
            f"Total Orders: {data['total_orders']:,}",
            f"Average Order Value: ${data['avg_order_value']:,.2f}",
            "\nTop 5 Products by Revenue:"
        ]
        for product in data['top_products'][:5]:
            report_lines.append(f"- {product['name']}: ${product['revenue']:,.2f} ({product['quantity']} units)")
        return '\n'.join(report_lines)
    
    def send_email_report(self, report: str) -> bool:
        """Send formatted report via email using FastCampus email service"""
        try:
            email_payload = {
                'to': os.getenv('REPORT_EMAIL_RECIPIENTS', '[email protected]'),
                'subject': f'Weekly Sales Report - {self.report_date}',
                'body': report
            }
            response = requests.post(
                f'{self.api_endpoint}/email',
                json=email_payload,
                headers={'Authorization': f'Bearer {self.api_key}'},
                timeout=15
            )
            response.raise_for_status()
            logging.info('Email report sent successfully')
            return True
        except Exception as e:
            logging.error(f'Failed to send email report: {str(e)}')
            return False

if __name__ == '__main__':
    try:
        agent = SalesReportAutomation()
        sales_data = agent.fetch_sales_data()
        report = agent.format_report(sales_data)
        success = agent.send_email_report(report)
        if success:
            print(f"Report generated and sent successfully. Check logs at /var/log/fastcampus/weekly_sales_report.log")
        else:
            print("Report generated but failed to send. Check error logs.")
    except Exception as e:
        print(f"Automation failed: {str(e)}. Check /var/log/fastcampus/weekly_sales_report.log for details")
```

This Python script demonstrates the FastCampus AI Agent & Vibe Coding methodology by:
1. Creating a modular class that inherits from FastCampusAgent
2. Implementing three core functions (data fetching, formatting, email distribution)
3. Including comprehensive error handling and logging
4. Using environment variables for configuration
5. Following Python best practices with type hints and docstrings

The script can be scheduled to run weekly using cron or FastCampus's automation scheduler. The logging ensures any issues are immediately visible, and the modular structure allows for easy extension to include additional metrics or recipients.