fastapi

A collection of patterns for building FastAPI applications, a Python framework for creating web APIs. It covers application structure, dependency injection, Pydantic v2 data schemas, authentication, middleware, background tasks, and route tests.

In plain words
What is it for?
Use it when structuring FastAPI services, defining request and response models, adding JWT or API-key authentication, handling errors and middleware, creating background tasks, or testing routes with httpx.
Why use it?
It gives the application a consistent way to organize web routes, business logic, data validation, authentication, and tests. This reduces ad hoc code as the API grows.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/kid-sid/codex-spellbook/fastapi
Any agent
npx skills add kid-sid/codex-spellbook --skill fastapi
Clone the repo
git clone --depth 1 https://github.com/kid-sid/codex-spellbook

Made for: Claude Code, Codex.

Per session 38 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,476 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00038 $0.03476
Opus 5 $0.00019 $0.01738
Sonnet 5 $0.00008 $0.00695
Haiku 4.5 $0.00004 $0.00348

Measured 2d ago against content hash a933f9a9344f, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

fastapi scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/fastapi/SKILL.md · 485 lines

How it starts

The opening of the file, as written. The whole thing — 485 lines — stays where its author put it; the contents beside it link to each section on GitHub.

FastAPI Patterns

Modern FastAPI (0.100+) with Pydantic v2, async-first, and typed throughout.

When to Activate

  • Structuring a FastAPI app with routers and layered architecture
  • Designing dependency injection chains with Depends
  • Defining Pydantic v2 request/response schemas
  • Handling errors, custom exception handlers, or middleware
  • Adding authentication (OAuth2, JWT, API keys)
  • Writing background tasks or startup/shutdown logic
  • Testing FastAPI routes with TestClient or async httpx

Project Structure

src/
├── api/
│   ├── app.py              # create_app(), register routers + middleware
│   ├── dependencies.py     # shared Depends (db session, current user, etc.)
│   ├── middleware.py        # CORS, logging, request ID
│   └── routes/
│       ├── users.py
│       └── orders.py
├── domain/
│   ├── entities/           # Pure Pydantic models — no ORM, no HTTP
│   ├── use_cases/          # Business logic — orchestrates services
│   └── repositories/       # Abstract interfaces (Protocol or ABC)
├── adapters/
│   ├── database/           # SQLAlchemy models + session factory
│   ├── crud/               # Concrete repository implementations
│   └── external/           # Third-party HTTP clients
├── config/
│   ├── settings.py         # Pydantic Settings (env vars)
│   └── dependencies.py     # App-wide singletons (DB engine, Redis, etc.)
└── main.py                 # uvicorn entry point

App Factory

# api/app.py
from contextlib import asynccontextmanager
from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware

from config.dependencies import GlobalDependencies
from api.routes import users, orders


@asynccontextmanager
async def lifespan(app: FastAPI):
    # Startup: initialize singletons, DB pools, caches
    await GlobalDependencies.initialize()
    yield
    # Shutdown: close connections cleanly
    await GlobalDependencies.close()


def create_app() -> FastAPI:
    app = FastAPI(
        title="My API",
        version="1.0.0",
        docs_url="/swagger",
        redoc_url="/api",
        lifespan=lifespan,
    )

    app.add_middleware(
        CORSMiddleware,
        allow_origins=["http://localhost:3000"],
        allow_credentials=True,
        allow_methods=["*"],
        allow_headers=["*"],
    )

    app.include_router(users.router, prefix="/api/v1/users", tags=["users"])
    app.include_router(orders.router, prefix="/api/v1/orders", tags=["orders"])

    return app

Read the full file on GitHub · 485 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 485 lines · 38 tokens per session scan A a933f9a9344f

Subscribe to this mod's changes

fastapi is a skill published in the GitHub repository kid-sid/codex-spellbook (21 stars, last pushed 3mo ago), licensed MIT. It adds 38 tokens to every session and 3,476 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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