implementing-api-patterns

# API Patterns Skill

## Purpose

Design and implement APIs using the optimal pattern and framework for the use case. Choose between REST, GraphQL, gRPC, and tRPC based on API consumers, performance requirements, and type safety needs.

## When to Use This Skill

Use when:
- Building backend APIs for web, mobile, or service consumers
- Connecting frontend components (forms, tables, dashboards) to databases
- Implementing pagination, rate limiting, or caching strategies
- Generating OpenAPI documentation automatically
- Choosing between REST, GraphQL, gRPC, or tRPC patterns
- Integrating authentication and authorization
- Optimizing API performance and scalability

## Quick Decision Framework

```
WHO CONSUMES YOUR API?
├─ PUBLIC/THIRD-PARTY DEVELOPERS → REST with OpenAPI
│  ├─ Python → FastAPI (auto-docs, 40k req/s)
│  ├─ TypeScript → Hono (edge-first, 50k req/s, 14KB)
│  ├─ Rust → Axum (140k req/s, <1ms latency)
│  └─ Go → Gin (100k+ req/s, mature ecosystem)
│
├─ FRONTEND TEAM (same org)
│  ├─ TypeScript full-stack? → tRPC (E2E type safety)
│  └─ Complex data needs? → GraphQL
│      ├─ Python → Strawberry
│      ├─ Rust → async-graphql
│      ├─ Go → gqlgen
│      └─ TypeScript → Pothos
│
├─ SERVICE-TO-SERVICE (microservices)
│  └─ High performance → gRPC
│      ├─ Rust → Tonic
│      ├─ Go → Connect-Go (browser-friendly)
│      └─ Python → grpcio
│
└─ MOBILE APPS
   ├─ Bandwidth constrained → GraphQL (request only needed fields)
   └─ Simple CRUD → REST (standard, well-understood)
```

## REST Framework Selection

### Python: FastAPI (Recommended)

**Key Features:** Auto OpenAPI docs, Pydantic v2 validation, async/await, 40k req/s

**Basic Example:**
```python
from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

class Item(BaseModel):
    name: str
    price: float

@app.post("/items")
async def create_item(item: Item):
    return {"id": 1, **item.dict()}
```

See `references/rest-design-principles.md` for FastAPI patterns and `examples/pyt