This is an example of a .cursorrules file for a Python / Supabase / AWS Lambda function

.cursorrules

# Python Version: 3.9+
# Code Style: PEP 8
# Naming Conventions:
#   - Classes: PascalCase
#   - Functions and variables: snake_case
#   - Constants: UPPER_SNAKE_CASE
#   - Private attributes/methods: _leading_underscore

# Project Type:
#   - AWS Lambda Function

# File Structure:
#   - Main script: main.py (containing lambda_handler)
#   - Database operations: db_operations.py
#   - Configuration: config.py
#   - Utility functions: utils.py

# Dependencies:
#   - supabase-py
#   - python-dotenv
#   - asyncio
#   - boto3

# Environment Variables:
#   - SOURCE_SUPABASE_URL
#   - SOURCE_SUPABASE_KEY
#   - TARGET_SUPABASE_URL
#   - TARGET_SUPABASE_KEY

# Error Handling:
#   - Use try-except blocks for database operations and AWS SDK calls
#   - Log errors with appropriate level (info, warning, error)

# Asynchronous Programming:
#   - Use asyncio for concurrent database operations

# Data Validation:
#   - Validate data types and formats before writing to database
#   - Validate Lambda event and context objects

# Security:
#   - Store sensitive information in environment variables or AWS Secrets Manager
#   - Use parameterized queries to prevent SQL injection
#   - Follow AWS Lambda security best practices

# Testing:
#   - Write unit tests for each function, including the lambda_handler
#   - Use pytest for testing framework
#   - Implement integration tests with AWS SAM Local

# Documentation:
#   - Use docstrings for functions and classes
#   - Include inline comments for complex logic
#   - Document Lambda function configuration and deployment process

# Logging:
#   - Use Python's built-in logging module
#   - Log important events and errors
#   - Utilize AWS CloudWatch for log management

# Configuration:
#   - Use a config.py file for storing configuration variables
#   - Utilize AWS Lambda environment variables for runtime configuration

# Version Control:
#   - Use Git for version control
#   - Follow conventional commits for commit messages

# Code Organization:
#   - Group related functions and classes together
#   - Use type hints for function parameters and return values

# Performance:
#   - Use batch operations for database queries when possible
#   - Implement caching for frequently accessed data
#   - Optimize Lambda function for cold starts

# Error Messages:
#   - Provide clear and informative error messages
#   - Return appropriate HTTP status codes in Lambda responses

# Maintainability:
#   - Keep functions small and focused on a single task
#   - Use meaningful variable and function names

# Scalability:
#   - Design the system to handle increasing amounts of data
#   - Consider using pagination for large data sets
#   - Utilize AWS Lambda concurrency and provisioned concurrency features

# Monitoring:
#   - Implement basic monitoring for script execution and database operations
#   - Use AWS CloudWatch metrics and alarms for Lambda function monitoring

# Deployment:
#   - Use AWS SAM or Serverless Framework for Lambda deployment
#   - Implement CI/CD pipeline for automated testing and deployment

# AWS Lambda Specific:
#   - Implement proper error handling and status code returns
#   - Optimize function for AWS Lambda execution environment
#   - Consider Lambda layers for managing dependencies