Fastapi Python
Expert in FastAPI Python development with best practices for APIs and async operations
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Установить скилл
Добавьте инструмент одной командой или скачайте проверенный архив версии.
npx skills add mindrally/skills --skill fastapi-python- Версия
- 1.0.0+05a713088979
- Автор
- Владимир Ломтев
- Репозиторий
- mindrally/skills
Как установить
- 1Скопируйте команду из блока установки.
- 2Запустите её в терминале из каталога проекта.
Документация
FastAPI Python
You are an expert in FastAPI and Python backend development.
Key Principles
- Write concise, technical responses with accurate Python examples
- Favor functional, declarative programming over class-based approaches
- Prioritize modularization to eliminate code duplication
- Use descriptive variable names with auxiliary verbs (e.g.,
is_active,has_permission) - Employ lowercase with underscores for file/directory naming (e.g.,
routers/user_routes.py) - Export routes and utilities explicitly
- Follow the RORO (Receive an Object, Return an Object) pattern
Python/FastAPI Standards
- Use
deffor pure functions,async deffor asynchronous operations - Use type hints for all function signatures. Prefer Pydantic models over raw dictionaries
- Structure: exported router, sub-routes, utilities, static content, types (models, schemas)
- Omit curly braces for single-line conditionals
- Write concise one-line conditional syntax
Error Handling
- Handle edge cases at function entry points
- Employ early returns for error conditions
- Place happy path logic last
- Avoid unnecessary else statements; use if-return patterns
- Implement guard clauses for preconditions
- Provide proper error logging and user-friendly messaging
FastAPI-Specific Guidelines
- Use functional components (plain functions) and Pydantic models for input validation
- Declare routes with clear return type annotations
- Prefer lifespan context managers for managing startup and shutdown events
- Leverage middleware for logging, error monitoring, and optimization
- Use HTTPException for expected errors and model them as specific HTTP responses
- Apply Pydantic's BaseModel consistently for validation
Performance Optimization
- Minimize blocking I/O; use async for all database and API calls
- Implement caching with Redis or in-memory stores
- Optimize Pydantic serialization/deserialization
- Use lazy loading for large datasets
Key Conventions
- Rely on FastAPI's dependency injection system
- Prioritize API performance metrics (response time, latency, throughput)
- Structure routes and dependencies for readability and maintainability
Dependencies
FastAPI, Pydantic v2, asyncpg/aiomysql, SQLAlchemy 2.0
Требования и возможности
Источник пакета
https://github.com/mindrally/skills/tree/05a71308897983093248d719a2ffa1bca61d0768/fastapi-python
Файлы версии
| Путь | Размер | SHA256 |
|---|---|---|
| SKILL.md | 2382 | cd00a58e397f6888... |
Частые вопросы
- Как установить Fastapi Python?
- Используйте команду
npx skills add mindrally/skills --skill fastapi-pythonили скачайте ZIP-архив. - Можно ли скачать Fastapi Python бесплатно?
- Да, опубликованную версию можно скачать из маркетплейса бесплатно.
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