Agent Skills
› debpalash/VoiceStudio
› fastapi-python
fastapi-python
GitHub提供FastAPI和Python后端开发的最佳实践指南,涵盖异步操作、Pydantic模型验证、RORO模式及性能优化,辅助构建高性能、模块化的RESTful API。
Trigger Scenarios
需要编写或优化FastAPI接口代码
询问Python后端架构与最佳实践
Install
npx skills add debpalash/VoiceStudio --skill fastapi-python -g -y
SKILL.md
Frontmatter
{
"name": "fastapi-python",
"description": "Expert in FastAPI Python development with best practices for APIs and async operations"
}
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)
- Use ordinary Python control flow; prefer readability over compressed one-line conditionals
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. In
async defhandlers, use awaitable database/API clients; put synchronous SQLite or other blocking work in synchronous routes or explicitly offload it - 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
Version History
- 032c5ab Current 2026-08-27 20:11


