fastapi

Q: How do I install fastapi?

Run npx skills add https://github.com/martinholovsky/claude-skills-generator --skill fastapi in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/fastapi/ (personal) or .claude/skills/fastapi/ (project).

Q: Which AI agents can use fastapi?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Q: Is fastapi safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Q: Who maintains fastapi?

It is published by martinholovsky in martinholovsky/claude-skills-generator. It has 263 recorded installs.

by martinholovskyBackend & APIs

REST API and WebSocket development with FastAPI emphasizing security, performance, and async patterns

Installs: 263
Last 24h: —
Trending rank: —
Security: Not audited

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/martinholovsky/claude-skills-generator --skill fastapi

01
Install
Run the command in your project. The CLI asks which agents to install for.
02
Describe the task
Ask your agent for something this skill covers. It loads fastapi automatically when relevant.
03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

FastAPI Development Skill

File Organization

SKILL.md: Core principles, patterns, essential security (this file)
references/security-examples.md: CVE details and OWASP implementations
references/advanced-patterns.md: Advanced FastAPI patterns
references/threat-model.md: Attack scenarios and STRIDE analysis

Validation Gates

Gate 0.2: Vulnerability Research (BLOCKING for HIGH-RISK)

Status: PASSED (5+ CVEs documented)
Research Date: 2025-11-20
CVEs: CVE-2024-47874, CVE-2024-12868, CVE-2023-30798, Starlette DoS variants

1. Overview

Risk Level: HIGH

Justification: FastAPI applications handle authentication, database access, file uploads, and external API communication. DoS vulnerabilities in Starlette, injection risks, and improper validation can compromise availability and security.

You are an expert FastAPI developer creating secure, performant REST APIs and WebSocket services. You configure proper validation, authentication, and security headers.

Core Expertise Areas

Pydantic validation and dependency injection
Authentication: OAuth2, JWT, API keys
Security headers and CORS configuration
Rate limiting and DoS protection
Database integration with async ORMs
WebSocket security

2. Core Responsibilities

Fundamental Principles

TDD First: Write tests before implementation code
Performance Aware: Connection pooling, caching, async patterns
Validate Everything: Use Pydantic models for all inputs
Secure by Default: HTTPS, security headers, strict CORS
Rate Limit: Protect all endpoints from abuse
Authenticate & Authorize: Verify identity and permissions
Handle Errors Safely: Never leak internal details

3. Technical Foundation

Version Recommendations

Component	Version	Notes
FastAPI	0.115.3+	CVE-2024-47874 fix
Starlette	0.40.0+	DoS vulnerability fix
Pydantic	2.0+	Better validation
Python	3.11+	Performance

Security Dependencies

[project]
dependencies = [
    "fastapi>=0.115.3",
    "starlette>=0.40.0",
    "pydantic>=2.5",
    "python-jose[cryptography]>=3.3",
    "passlib[argon2]>=1.7",
    "python-multipart>=0.0.6",
    "slowapi>=0.1.9",
    "secure>=0.3",
]

4. Implementation Patterns

Pattern 1: Secure Application Setup

from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from secure import SecureHeaders

app = FastAPI(
    title="Secure API",
    docs_url=None if PRODUCTION else "/docs",  # Disable in prod
    redoc_url=None,
)

# Security headers
secure_headers = SecureHeaders()

@app.middleware("http")
async def add_security_headers(request, call_next):
    response = await call_next(request)
    secure_headers.framework.fastapi(response)
    return response

# Restrictive CORS
app.add_middleware(
    CORSMiddleware,
    allow_origins=["https://app.example.com"],  # Never ["*"]
    allow_credentials=True,
    allow_methods=["GET", "POST", "PUT", "DELETE"],
    allow_headers=["Authorization", "Content-Type"],
)

Pattern 2: Input Validation

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel, Field, field_validator, EmailStr

class UserCreate(BaseModel):
    username: str = Field(min_length=3, max_length=50, pattern=r'^[a-zA-Z0-9_-]+$')
    email: EmailStr
    password: str = Field(min_length=12)

    @field_validator('password')
    @classmethod
    def validate_password(cls, v):
        if not any(c.isupper() for c in v):
            raise ValueError('Must contain uppercase')
        if not any(c.isdigit() for c in v):
            raise ValueError('Must contain digit')
        return v

@app.post("/users")
async def create_user(user: UserCreate):
    # Input already validated by Pydantic
    return await user_service.create(user)

Pattern 3: JWT Authentication

from fastapi import Depends, HTTPException, status
from fastapi.security import OAuth2PasswordBearer
from jose import JWTError, jwt
from datetime import datetime, timedelta

oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token")

SECRET_KEY = os.environ["JWT_SECRET"]
ALGORITHM = "HS256"
ACCESS_TOKEN_EXPIRE_MINUTES = 30

def create_access_token(data: dict) -> str:
    to_encode = data.copy()
    expire = datetime.utcnow() + timedelta(minutes=ACCESS_TOKEN_EXPIRE_MINUTES)
    to_encode.update({"exp": expire})
    return jwt.encode(to_encode, SECRET_KEY, algorithm=ALGORITHM)

async def get_current_user(token: str = Depends(oauth2_scheme)) -> User:
    credentials_exception = HTTPException(
        status_code=status.HTTP_401_UNAUTHORIZED,
        detail="Could not validate credentials",
        headers={"WWW-Authenticate": "Bearer"},
    )
    try:
        payload = jwt.decode(token, SECRET_KEY, algorithms=[ALGORITHM])
        user_id: str = payload.get("sub")
        if user_id is None:
            raise credentials_exception
    except JWTError:
        raise credentials_exception

    user = await user_service.get(user_id)
    if user is None:
        raise credentials_exception
    return user

Pattern 4: Rate Limiting

from slowapi import Limiter, _rate_limit_exceeded_handler
from slowapi.util import get_remote_address
from slowapi.errors import RateLimitExceeded

limiter = Limiter(key_func=get_remote_address)
app.state.limiter = limiter
app.add_exception_handler(RateLimitExceeded, _rate_limit_exceeded_handler)

@app.post("/login")
@limiter.limit("5/minute")  # Strict for auth endpoints
async def login(request: Request, credentials: LoginRequest):
    return await auth_service.login(credentials)

@app.get("/data")
@limiter.limit("100/minute")
async def get_data(request: Request):
    return await data_service.get_all()

Pattern 5: Safe File Upload

from fastapi import UploadFile, File, HTTPException
import magic

ALLOWED_TYPES = {"image/jpeg", "image/png", "application/pdf"}
MAX_SIZE = 10 * 1024 * 1024  # 10MB

@app.post("/upload")
async def upload_file(file: UploadFile = File(...)):
    # Check size
    content = await file.read()
    if len(content) > MAX_SIZE:
        raise HTTPException(400, "File too large")

    # Check magic bytes, not just extension
    mime_type = magic.from_buffer(content, mime=True)
    if mime_type not in ALLOWED_TYPES:
        raise HTTPException(400, f"File type not allowed: {mime_type}")

    # Generate safe filename
    safe_name = f"{uuid4()}{Path(file.filename).suffix}"

    # Store outside webroot
    file_path = UPLOAD_DIR / safe_name
    file_path.write_bytes(content)

    return {"filename": safe_name}

5. Implementation Workflow (TDD)

Step 1: Write Failing Test First

Always start with tests that define expected behavior:

import pytest
from httpx import AsyncClient, ASGITransport
from app.main import app

@pytest.mark.asyncio
async def test_create_item_success():
    """Test successful item creation with valid data."""
    async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
        response = await client.post(
            "/items",
            json={"name": "Test Item", "price": 29.99},
            headers={"Authorization": "Bearer valid_token"}
        )
        assert response.status_code == 201
        data = response.json()
        assert data["name"] == "Test Item"
        assert "id" in data

@pytest.mark.asyncio
async def test_create_item_validation_error():
    """Test validation rejects invalid price."""
    async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
        response = await client.post(
            "/items",
            json={"name": "Test", "price": -10},
            headers={"Authorization": "Bearer valid_token"}
        )
        assert response.status_code == 422

@pytest.mark.asyncio
async def test_create_item_unauthorized():
    """Test endpoint requires authentication."""
    async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
        response = await client.post("/items", json={"name": "Test", "price": 10})
        assert response.status_code == 401

Step 2: Implement Minimum to Pass

Write only the code needed to make tests pass:

@app.post("/items", status_code=201)
async def create_item(
    item: ItemCreate,
    user: User = Depends(get_current_user)
) -> ItemResponse:
    created = await item_service.create(item, user.id)
    return ItemResponse.from_orm(created)

Step 3: Refactor if Needed

Improve code quality while keeping tests green. Extract common patterns, improve naming, optimize queries.

Step 4: Run Full Verification

# Run all tests with coverage
pytest --cov=app --cov-report=term-missing

# Type checking
mypy app --strict

# Security scan
bandit -r app -ll

# All must pass before committing

6. Performance Patterns

Pattern 1: Connection Pooling for Database

# BAD - Creates new connection per request
@app.get("/users/{user_id}")
async def get_user(user_id: int):
    conn = await asyncpg.connect(DATABASE_URL)
    try:
        return await conn.fetchrow("SELECT * FROM users WHERE id = $1", user_id)
    finally:
        await conn.close()

# GOOD - Uses connection pool
from contextlib import asynccontextmanager

pool: asyncpg.Pool = None

@asynccontextmanager
async def lifespan(app: FastAPI):
    global pool
    pool = await asyncpg.create_pool(
        DATABASE_URL,
        min_size=5,
        max_size=20,
        command_timeout=60
    )
    yield
    await pool.close()

app = FastAPI(lifespan=lifespan)

@app.get("/users/{user_id}")
async def get_user(user_id: int):
    async with pool.acquire() as conn:
        return await conn.fetchrow("SELECT * FROM users WHERE id = $1", user_id)

Pattern 2: Concurrent Requests with asyncio.gather

# BAD - Sequential external API calls
@app.get("/dashboard")
async def get_dashboard(user_id: int):
    profile = await fetch_profile(user_id)      # 100ms
    orders = await fetch_orders(user_id)        # 150ms
    notifications = await fetch_notifications(user_id)  # 80ms
    return {"profile": profile, "orders": orders, "notifications": notifications}
    # Total: ~330ms

# GOOD - Concurrent calls
@app.get("/dashboard")
async def get_dashboard(user_id: int):
    profile, orders, notifications = await asyncio.gather(
        fetch_profile(user_id),
        fetch_orders(user_id),
        fetch_notifications(user_id)
    )
    return {"profile": profile, "orders": orders, "notifications": notifications}
    # Total: ~150ms (slowest call)

Pattern 3: Response Caching

# BAD - Recomputes expensive data every request
@app.get("/stats")
async def get_stats():
    return await compute_expensive_stats()  # 500ms each time

# GOOD - Cache with Redis
from fastapi_cache import FastAPICache
from fastapi_cache.backends.redis import RedisBackend
from fastapi_cache.decorator import cache

@asynccontextmanager
async def lifespan(app: FastAPI):
    redis = aioredis.from_url("redis://localhost")
    FastAPICache.init(RedisBackend(redis), prefix="api-cache")
    yield

@app.get("/stats")
@cache(expire=300)  # Cache for 5 minutes
async def get_stats():
    return await compute_expensive_stats()

# GOOD - In-memory cache for simpler cases
from functools import lru_cache
from datetime import datetime, timedelta

_cache = {}
_cache_time = {}

async def get_cached_config(key: str, ttl: int = 60):
    now = datetime.utcnow()
    if key in _cache and _cache_time[key] > now:
        return _cache[key]

    value = await fetch_config(key)
    _cache[key] = value
    _cache_time[key] = now + timedelta(seconds=ttl)
    return value

Pattern 4: Pagination for Large Datasets

# BAD - Returns all records
@app.get("/items")
async def list_items():
    return await db.fetch("SELECT * FROM items")  # Could be millions

# GOOD - Cursor-based pagination
from pydantic import BaseModel

class PaginatedResponse(BaseModel):
    items: list
    next_cursor: str | None
    has_more: bool

@app.get("/items")
async def list_items(
    cursor: str | None = None,
    limit: int = Query(default=20, le=100)
) -> PaginatedResponse:
    query = "SELECT * FROM items"
    params = []

    if cursor:
        query += " WHERE id > $1"
        params.append(decode_cursor(cursor))

    query += f" ORDER BY id LIMIT {limit + 1}"

    rows = await db.fetch(query, *params)
    has_more = len(rows) > limit
    items = rows[:limit]

    return PaginatedResponse(
        items=items,
        next_cursor=encode_cursor(items[-1]["id"]) if items else None,
        has_more=has_more
    )

Pattern 5: Background Tasks for Heavy Operations

# BAD - Blocks response for slow operations
@app.post("/reports")
async def create_report(request: ReportRequest):
    report = await generate_report(request)  # Takes 30 seconds
    await send_email(request.email, report)
    return {"status": "completed"}

# GOOD - Return immediately, process in background
from fastapi import BackgroundTasks

@app.post("/reports", status_code=202)
async def create_report(
    request: ReportRequest,
    background_tasks: BackgroundTasks
):
    report_id = str(uuid4())
    background_tasks.add_task(process_report, report_id, request)
    return {"report_id": report_id, "status": "processing"}

async def process_report(report_id: str, request: ReportRequest):
    report = await generate_report(request)
    await save_report(report_id, report)
    await send_email(request.email, report)

@app.get("/reports/{report_id}")
async def get_report_status(report_id: str):
    return await get_report(report_id)

7. Security Standards

7.1 Domain Vulnerability Landscape

CVE ID	Severity	Description	Mitigation
CVE-2024-47874	HIGH	Starlette multipart DoS via memory exhaustion	Upgrade Starlette 0.40.0+
CVE-2024-12868	HIGH	Downstream DoS via fastapi dependency	Upgrade FastAPI 0.115.3+
CVE-2023-30798	HIGH	Starlette <0.25 DoS	Upgrade FastAPI 0.92+

7.2 OWASP Top 10 Mapping

Category	Risk	Mitigations
A01 Access Control	HIGH	Dependency injection for auth, permission decorators
A02 Crypto Failures	HIGH	JWT with proper algorithms, Argon2 passwords
A03 Injection	HIGH	Pydantic validation, parameterized queries
A04 Insecure Design	MEDIUM	Type safety, validation layers
A05 Misconfiguration	HIGH	Security headers, disable docs in prod
A06 Vulnerable Components	CRITICAL	Keep Starlette/FastAPI updated
A07 Auth Failures	HIGH	Rate limiting on auth, secure JWT

7.3 Error Handling

from fastapi import HTTPException
from fastapi.responses import JSONResponse
import logging

logger = logging.getLogger(__name__)

@app.exception_handler(Exception)
async def global_exception_handler(request, exc):
    # Log full details
    logger.error(f"Unhandled error: {exc}", exc_info=True)

    # Return safe message
    return JSONResponse(
        status_code=500,
        content={"detail": "Internal server error"}
    )

@app.exception_handler(HTTPException)
async def http_exception_handler(request, exc):
    return JSONResponse(
        status_code=exc.status_code,
        content={"detail": exc.detail}
    )

6. Testing & Validation

Security Tests

import pytest
from fastapi.testclient import TestClient

def test_rate_limiting():
    client = TestClient(app)
    # Exceed rate limit
    for _ in range(10):
        response = client.post("/login", json={"username": "test", "password": "test"})
    assert response.status_code == 429

def test_invalid_jwt_rejected():
    client = TestClient(app)
    response = client.get(
        "/protected",
        headers={"Authorization": "Bearer invalid.token.here"}
    )
    assert response.status_code == 401

def test_sql_injection_prevented():
    client = TestClient(app)
    response = client.get("/users", params={"search": "'; DROP TABLE users; --"})
    assert response.status_code in [200, 400]
    # Should not cause 500 (SQL error)

def test_file_upload_type_validation():
    client = TestClient(app)
    # Try uploading executable disguised as image
    response = client.post(
        "/upload",
        files={"file": ("test.jpg", b"MZ\x90\x00", "image/jpeg")}  # EXE magic bytes
    )
    assert response.status_code == 400

8. Common Mistakes & Anti-Patterns

Anti-Pattern 1: Permissive CORS

# NEVER
app.add_middleware(CORSMiddleware, allow_origins=["*"], allow_credentials=True)

# ALWAYS
app.add_middleware(CORSMiddleware, allow_origins=["https://app.example.com"])

Anti-Pattern 2: No Rate Limiting

# NEVER - allows brute force
@app.post("/login")
async def login(creds): ...

# ALWAYS
@app.post("/login")
@limiter.limit("5/minute")
async def login(request, creds): ...

Anti-Pattern 3: Exposing Docs in Production

# NEVER
app = FastAPI()

# ALWAYS
app = FastAPI(
    docs_url=None if os.environ.get("ENV") == "production" else "/docs",
    redoc_url=None
)

Anti-Pattern 4: Weak JWT Configuration

# NEVER
jwt.encode(data, "secret", algorithm="HS256")  # Hardcoded weak secret

# ALWAYS
jwt.encode(data, os.environ["JWT_SECRET"], algorithm="RS256")  # Env var, strong algo

Anti-Pattern 5: File Extension Validation Only

# NEVER
if file.filename.endswith('.jpg'): ...

# ALWAYS
mime = magic.from_buffer(content, mime=True)
if mime not in ALLOWED_TYPES: ...

13. Pre-Deployment Checklist

14. Summary

Your goal is to create FastAPI applications that are:

Secure: Validated inputs, rate limits, security headers
Performant: Async operations, proper connection pooling
Maintainable: Type-safe, well-structured, tested

Security Reminder:

Upgrade Starlette to 0.40.0+ (CVE-2024-47874)
Rate limit all endpoints, especially authentication
Validate file uploads by magic bytes, not extension
Never use wildcard CORS with credentials
Disable API docs in production

FAQ

What does the fastapi skill do?

REST API and WebSocket development with FastAPI emphasizing security, performance, and async patterns

How do I install fastapi?

Run npx skills add https://github.com/martinholovsky/claude-skills-generator --skill fastapi in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/fastapi/ (personal) or .claude/skills/fastapi/ (project).

Which AI agents can use fastapi?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is fastapi safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains fastapi?

It is published by martinholovsky in martinholovsky/claude-skills-generator. It has 263 recorded installs.