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fastapi

Guide FastAPI and Pydantic conventions for building and refactoring APIs.

1|Updated Mar 7, 2026
Search Tags:#pydantic#fastapi#api design#streaming#async#dependency injection#response models
One-click install
npx skills add https://github.com/y0ncha/aptitude-client --skill fastapi-y0ncha
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: fastapi
Source: https://github.com/y0ncha/aptitude-client/tree/main/.agents/skills/fastapi
Command: npx skills add https://github.com/y0ncha/aptitude-client --skill fastapi-y0ncha

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

FastAPI projects can become inconsistent, expose sensitive data, or suffer performance and maintainability issues when patterns and newer framework features are not followed. This Skill provides clear conventions and practical guidance to keep FastAPI codebases idiomatic, secure, and performant as the framework and Pydantic evolve.

Core Features & Use Cases

  • Annotated parameter and dependency usage: Prefer Annotated for Path/Query/Header and dependency types to keep signatures explicit and reusable.
  • Response modeling and serialization: Recommend return types or response_model usage to filter sensitive fields and enable efficient serialization.
  • Router and dependency organization: Encourage router-level configuration, shared dependencies, and appropriate dependency scopes including yield-based cleanup.
  • Async vs sync guidance and streaming: Provide rules for when to use async, when to run blocking code safely, and patterns for JSON Lines, SSE, and byte streaming.
  • Tooling & libraries: Advise on using tooling like linters and Asyncer, and recommended libraries such as SQLModel and HTTPX.
  • Use Case: Refactoring a legacy endpoint to use Annotated parameters, a proper Pydantic response model, router-level dependencies, and streaming where appropriate to prevent data leaks and improve throughput.

Quick Start

Refactor an endpoint to use Annotated request parameters, a Pydantic response model, and router-level dependencies so the API validates inputs, hides internal fields, and follows async/sync best practices.

Frequently Asked Questions about fastapi

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I use Annotated for dependency injection in FastAPI?▼

FastAPI dependency injection uses Annotated to declare Path, Query, Header, and dependency types in function signatures. This keeps parameters explicit, reusable, and cleanly separated from default values.

What is the best way to prevent sensitive data exposure in FastAPI response models?▼

Preventing sensitive data exposure in FastAPI response models requires defining explicit Pydantic return types or using response_model to filter internal fields and enable safe serialization.

When should I use async versus sync endpoints in FastAPI?▼

Use async FastAPI endpoints for non-blocking operations, and sync endpoints for blocking code like standard database calls. Asyncer can safely run blocking code within async paths.

How do I implement streaming responses for JSON Lines or SSE in FastAPI?▼

Streaming JSON Lines, SSE, and byte streams in FastAPI involves returning async generators or streaming responses. This pattern improves throughput for large payloads and continuous data feeds.

Does FastAPI router-level configuration support shared dependencies and cleanup?▼

FastAPI router-level configuration supports shared dependencies and scopes. Yield-based dependencies provide necessary cleanup operations for resources across grouped endpoints.

What libraries integrate well with FastAPI for database and HTTP operations?▼

FastAPI integrates well with SQLModel for database operations and HTTPX for async HTTP requests. These align with Pydantic models and async patterns for clean backend services.

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