Implementing API Patterns
API design and implementation across REST, GraphQL, gRPC, and tRPC patterns.
Install
npx skills add ancoleman/ai-design-components --skill implementing-api-patterns -g -a claude-code -y- Repository
- ancoleman/ai-design-components526
- Category
- Design
- Updated
- 11mo ago
- License
- MIT
- Context cost
- Mediumabout 2,807 tokens
- Runs scripts
- Yes, review before use
What it does
API design and implementation across REST, GraphQL, gRPC, and tRPC patterns. Use when building backend services, public APIs, or service-to-service communication. Covers REST frameworks (FastAPI, Axum, Gin, Hono), GraphQL libraries (Strawberry, async-graphql, gqlgen, Pothos), gRPC (Tonic, Connect-Go), tRPC for TypeScript, pagination strategies (cursor-based, offset-based), rate limiting, caching, versioning, and OpenAPI documentation generation. Includes frontend integration patterns for forms, tables, dashboards, and ai-chat skills.
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:
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()}
