Browse

Dockerfiles .cursorrules prompt files

Dockerfiles are text files that define the instructions to build a Docker image, enabling consistent and automated deployment of applications across various environments.

Filters

All

React

Tailwind CSS

Filters

Tech

Clear

Chainlink VRF

Drizzle

Zustand

Prisma

SWR

Ruff

virtual environments

Langchain

SQLite

dask

matplotlib

tqdm

Flask

seaborn

joblib

Tauri

python-dotenv

pip

Azure

Django REST Framework

MySQL

unittest

WhiteNoise

pytest-django

Go

Kafka

Rust

GraphQL

Kubernetes

Cypress

Axios

Next.js SSR.

Angular

React Server Components (RSC)

Rell

Kotlin

Tailwind CSS.

Cairo

Biome

Composer

yarn

Nuxt 3

Wagmi

Viem

autoprefixer

@types/react

Next.Js

@types/node

postcss

react-dom

Supabase Auth

Next.js 14

Daisy UI

MongoDB

React.js

Mongoose ODM

Express.js

JSON Web Tokens

Dusk

PHPUnit

Paraglide.js

WordPress

Sass

MDX

Less

Netlify

Linux

Vercel AI SDK

Framer Motion

Firebase

Formik

poetry

node-gyp

uuid

mime-types

js-yaml

Istio

SOLID

CSS frameworks

Expo Router

React Query

Sentry

Clerk

OpenAI

SSR

RSC

fetch

React Native Testing Library

Detox

HTTPS

Flexbox

Prometheus

Memcached

ELK Stack

Grafana

dataclasses

SQLModel

async/await

API

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.

stack: