CORS .cursorrules Cursor AI Prompt File Examples

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FastAPI .cursorrules prompt file guide

Code Style and Structure:

Follow PEP 8 guidelines for Python code style
Use meaningful variable and function names (snake_case for variables and functions, PascalCase for classes)
Keep functions small and focused on a single task
Use docstrings for modules, classes, and functions
Organize code into logical modules and packages
Use virtual environments for project isolation

Python-specific Conventions:

Prefer list comprehensions and generator expressions over map() and filter()
Use context managers (with statements) for resource management
Utilize decorators for cross-cutting concerns (e.g., logging, timing)
Leverage type hints for improved code readability and tooling support
Use f-strings for string formatting

Framework and API Development:

Use FastAPI as the primary framework for building high-performance APIs
Leverage FastAPI's automatic OpenAPI (Swagger) documentation
Utilize dependency injection for clean, testable code
Implement RESTful API design principles
Use Pydantic for data validation, serialization, and settings management
Define request and response models using Pydantic
Utilize Pydantic's BaseSettings for configuration management

Database and Data Modeling:

Use SQLModel, which combines SQLAlchemy core with Pydantic models
Leverage SQLModel's ability to use the same models for database tables and API schemas
Utilize SQLModel's async support for high-performance database operations
Implement database migrations using Alembic
Use connection pooling for efficient database connections
Implement proper indexing for frequently queried fields

Authentication and Security:

Implement JWT for stateless authentication
Use FastAPI's built-in security utilities
Use bcrypt for password hashing
Apply CORS policies using FastAPI's CORS middleware
Implement rate limiting for API endpoints (consider using FastAPI's dependencies for this)

Error Handling and Logging:

Use FastAPI's exception handlers for consistent error responses
Implement custom exception classes for application-specific errors
Use a logging framework (e.g., Python's built-in logging module)
Structure log messages for easy parsing and analysis

Frontend Development:

For server-side rendering, consider using FastAPI with Jinja2 templates
Implement responsive design using CSS frameworks (e.g., Bootstrap)
For single-page applications, use a JavaScript framework (e.g., React, Vue.js) that consumes your FastAPI backend

Testing:

Write unit tests using pytest
Implement integration tests for API endpoints using FastAPI's TestClient
Use factories (e.g., Factory Boy) for test data generation
Aim for high test coverage, especially for critical paths
Utilize Pydantic's parse_obj_as for easy test data creation

Asynchronous Processing:

Leverage FastAPI's asynchronous capabilities for concurrent processing
For background tasks, use FastAPI's background tasks feature or Celery for more complex scenarios
Implement message queues (e.g., RabbitMQ, Redis) for task management if needed

Deployment and DevOps:

Use Docker for containerization
Create optimized Dockerfiles for your FastAPI applications
Implement CI/CD pipelines (e.g., GitLab CI, GitHub Actions)
Use environment variables for configuration management
Utilize Pydantic's BaseSettings to load and validate environment variables
Implement health check endpoints for monitoring

Performance Optimization:

Leverage FastAPI's asynchronous capabilities for I/O-bound operations
Use caching mechanisms (e.g., Redis) for frequently accessed data
Implement database query optimization techniques with SQLModel
Profile code to identify and resolve bottlenecks

API Documentation:

Utilize FastAPI's automatic interactive API documentation (Swagger UI and ReDoc)
Keep API documentation up-to-date by maintaining accurate Pydantic models and FastAPI path operation functions

Version Control:

Use Git for version control
Implement feature branching and pull request workflows
Write meaningful commit messages

Dependencies Management

Use pip and requirements.txt for managing project dependencies
Consider using Poetry for more advanced dependency management

Remember to keep your code DRY (Don't Repeat Yourself), follow SOLID principles, and prioritize readability and maintainability in your Python full-stack development projects. Leverage the power of FastAPI, SQLModel, and Pydantic to create robust, type-safe, and high-performance applications.

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FastAPI .cursorrules prompt file guide

Rohit Nair

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Optimize Python .cursorrules PEP 8 async await Dataclasses .cursorrules prompt file

Python:

Use Python 3.8+ features
Follow PEP 8 style guide with a line length of 120 characters
Use type hints consistently
Prefer functional programming patterns where appropriate
Use async/await for asynchronous operations
Structure code into modular, reusable functions

Python Specifics:

Use f-strings for string formatting
Do not use f-strings for log messages (use 'Message with %s', expensive_func1())')
Use list/dict/set comprehensions where they improve readability
Utilize dataclasses or Pydantic models for data structures
Use pytest for testing

Naming Conventions:

Use snake_case for function and variable names
Use PascalCase for class names
Use UPPER_CASE for constants

Error Handling:

Use specific exception types
Implement proper logging
Return meaningful error messages and appropriate HTTP status codes in API responses

General:

Write clear, concise comments and docstrings
Keep functions and methods small and focused
Follow the DRY (Don't Repeat Yourself) principle
Implement proper error logging and monitoring
Use environment variables for configuration
Implement proper security measures (e.g., input validation, CORS, authentication)

Version Control:

Write meaningful commit messages
Use feature branches and pull requests for new features or significant changes
Keep commits small and focused

Documentation:

Maintain up-to-date README files
Document API endpoints and their usage
Keep inline documentation current with code changes

stack:

Optimize Python .cursorrules PEP 8 async await Dataclasses .cursorrules prompt file

markusedgar

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TypeScript Python FastAPI React Best Practices .cursorrules prompt file

Frontend (TypeScript/React):

Use functional components with TypeScript interfaces
Prefer named exports for components
Use declarative programming patterns; avoid classes where possible
Structure files: exported component, subcomponents, helpers, static content, types
Use descriptive variable names (e.g., isLoading, hasError)
Implement responsive design with MUI (Material-UI)
Use dynamic imports for non-critical components
Optimize images: use appropriate formats, include size data, implement lazy loading
Minimize use of useEffect; favor React Server Components where applicable
Wrap client components in Suspense with fallback for better loading experience

TypeScript Usage:

Use TypeScript for all frontend code
Prefer interfaces over types for better extensibility
Avoid enums; use string literal unions or const objects instead
Enable strict mode in tsconfig.json

Naming Conventions:

Use PascalCase for component files (e.g., SubscriptionList.tsx)
Use camelCase for non-component files (e.g., urlUtils.ts)
Use lowercase with dashes for directories (e.g., components/add-subscription)

Syntax and Formatting:

Use arrow functions for component definitions and callbacks
Avoid unnecessary curly braces in conditionals; use concise syntax for simple statements
Use optional chaining (?.) and nullish coalescing (??) operators
Prefer template literals over string concatenation

Error Handling and Validation:

Handle errors and edge cases early in the function
Use try/catch blocks for async operations
Implement proper error logging and user-friendly messages
Use appropriate form validation (consider using a library like Formik or react-hook-form)

State Management:

Use React hooks (useState, useContext) for local and shared state
Consider using a state management library for complex state (e.g., Redux, Recoil)

Backend (Python):

Use Python 3.8+ features
Follow PEP 8 style guide with a line length of 120 characters
Use type hints consistently
Prefer functional programming patterns where appropriate
Use async/await for asynchronous operations
Structure code into modular, reusable functions

Python Specifics:

Use f-strings for string formatting
Do not use f-strings for log messages (use 'Message with %s', expensive_func1())')
Use list/dict/set comprehensions where they improve readability
Utilize dataclasses or Pydantic models for data structures
Use pytest for testing

Naming Conventions:

Use snake_case for function and variable names
Use PascalCase for class names
Use UPPER_CASE for constants

Error Handling:

Use specific exception types
Implement proper logging
Return meaningful error messages and appropriate HTTP status codes in API responses

API Design:

Follow RESTful principles
Use FastAPI for API development
Implement proper request validation and error handling
Use Pydantic models for request/response schemas

Database:

Use SQLModel for ORM (https://sqlmodel.tiangolo.com/)
Use SQLAlchemy for database operations when needed but prefer to use SQLModel
Implement database migrations for schema changes
Use async database operations where possible

General:

Write clear, concise comments and docstrings
Keep functions and methods small and focused
Follow the DRY (Don't Repeat Yourself) principle
Implement proper error logging and monitoring
Use environment variables for configuration
Implement proper security measures (e.g., input validation, CORS, authentication)

Version Control:

Write meaningful commit messages
Use feature branches and pull requests for new features or significant changes
Keep commits small and focused

Testing:

Write unit tests for both frontend and backend code
Aim for high test coverage, especially for critical paths
Use mocking for external dependencies in tests

Performance:

Optimize database queries
Implement caching where appropriate
Use pagination for large datasets

Accessibility:

Ensure proper keyboard navigation
Use semantic HTML elements
Provide alternative text for images

Documentation:

Maintain up-to-date README files
Document API endpoints and their usage
Keep inline documentation current with code changes

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TypeScript Python FastAPI React Best Practices .cursorrules prompt file

Bryan Thode

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Python FastAPI Guide .cursorrules prompt file

You are an expert in Python, FastAPI, microservices architecture, and serverless environments.

Advanced Principles

Design services to be stateless; leverage external storage and caches (e.g., Redis) for state persistence.
Implement API gateways and reverse proxies (e.g., NGINX, Traefik) for handling traffic to microservices.
Use circuit breakers and retries for resilient service communication.
Favor serverless deployment for reduced infrastructure overhead in scalable environments.
Use asynchronous workers (e.g., Celery, RQ) for handling background tasks efficiently.
Microservices and API Gateway Integration
Integrate FastAPI services with API Gateway solutions like Kong or AWS API Gateway.
Use API Gateway for rate limiting, request transformation, and security filtering.
Design APIs with clear separation of concerns to align with microservices principles.
Implement inter-service communication using message brokers (e.g., RabbitMQ, Kafka) for event-driven architectures.
Serverless and Cloud-Native Patterns
Optimize FastAPI apps for serverless environments (e.g., AWS Lambda, Azure Functions) by minimizing cold start times.
Package FastAPI applications using lightweight containers or as a standalone binary for deployment in serverless setups.
Use managed services (e.g., AWS DynamoDB, Azure Cosmos DB) for scaling databases without operational overhead.
Implement automatic scaling with serverless functions to handle variable loads effectively.
Advanced Middleware and Security
Implement custom middleware for detailed logging, tracing, and monitoring of API requests.
Use OpenTelemetry or similar libraries for distributed tracing in microservices architectures.
Apply security best practices: OAuth2 for secure API access, rate limiting, and DDoS protection.
Use security headers (e.g., CORS, CSP) and implement content validation using tools like OWASP Zap.
Optimizing for Performance and Scalability
Leverage FastAPI’s async capabilities for handling large volumes of simultaneous connections efficiently.
Optimize backend services for high throughput and low latency; use databases optimized for read-heavy workloads (e.g., Elasticsearch).
Use caching layers (e.g., Redis, Memcached) to reduce load on primary databases and improve API response times.
Apply load balancing and service mesh technologies (e.g., Istio, Linkerd) for better service-to-service communication and fault tolerance.
Monitoring and Logging
Use Prometheus and Grafana for monitoring FastAPI applications and setting up alerts.
Implement structured logging for better log analysis and observability.
Integrate with centralized logging systems (e.g., ELK Stack, AWS CloudWatch) for aggregated logging and monitoring.
Key Conventions

Follow microservices principles for building scalable and maintainable services.
Optimize FastAPI applications for serverless and cloud-native deployments.
Apply advanced security, monitoring, and optimization techniques to ensure robust, performant APIs.

Refer to FastAPI, microservices, and serverless documentation for best practices and advanced usage patterns.

stack:

Python

FastAPI

microservices architecture

Redis

NGINX