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Fastapi Expert

  • 4.7k installs
  • 10.8k repo stars
  • Updated May 20, 2026
  • jeffallan/claude-skills

fastapi-expert is an agent skill for Use when building high-performance async Python APIs with FastAPI and Pydantic V2. Invoke to create REST endpoints, defi

About

The fastapi-expert skill use when building high-performance async Python APIs with FastAPI and Pydantic V2. Invoke to create REST endpoints, define Pydantic models, implement authentication flows, set up async SQLAlchemy database operations, add JWT authentication, build WebSocket endpoints, or generate OpenAPI documentation. Trigger terms: FastAPI, Pydantic, async Python, Python API, REST API Python, SQLAlchemy async, JWT authentication, OpenAPI, Swagger Python.. FastAPI Expert Deep expertise in async Python, Pydantic V2, and production-grade API development with FastAPI. When to Use This Skill - Building REST APIs with FastAPI - Implementing Pydantic V2 validation schemas - Setting up async database operations - Implementing JWT authentication/authorization - Creating WebSocket endpoints - Optimizing API performance Core Workflow 1. Analyze requirements - Identify endpoints, data models, auth needs 2. Design schemas - Create Pydantic V2 models for validation 3. Implement - Write async endpoints with proper dependency injection 4. Secure - Add authentication, authorization, rate limiting 5.

  • Use when building high-performance async Python APIs with FastAPI and Pydantic V2. Invoke to create REST endpoints, defi
  • Building REST APIs with FastAPI
  • Implementing Pydantic V2 validation schemas
  • Setting up async database operations
  • Implementing JWT authentication/authorization

Fastapi Expert by the numbers

  • 4,654 all-time installs (skills.sh)
  • +124 installs in the week ending Jul 28, 2026 (Skillselion tracking)
  • Ranked #281 of 2,184 Testing & QA skills by installs in the Skillselion catalog
  • Security screen: MEDIUM risk (skills.sh audit)
  • Data as of Jul 28, 2026 (Skillselion catalog sync)
At a glance

fastapi-expert capabilities & compatibility

Capabilities
use when building high performance async python · building rest apis with fastapi · implementing pydantic v2 validation schemas
From the docs

What fastapi-expert says it does

**Checkpoint after each step:** confirm schemas validate correctly, endpoints return expected HTTP status codes, and `/docs` reflects the intended API surface before proceeding.
SKILL.md
npx skills add https://github.com/jeffallan/claude-skills --skill fastapi-expert

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Listed on Skillselion
Installs4.7k
repo stars10.8k
Security audit3 / 3 scanners passed
Last updatedMay 20, 2026
Repositoryjeffallan/claude-skills

How do I run fastapi-expert tasks with correct setup and documented commands?

Use when building high-performance async Python APIs with FastAPI and Pydantic V2. Invoke to create REST endpoints, define Pydantic models, implement authentication flows, set up async SQLAlchemy data

Who is it for?

Developers automating fastapi expert via agent-guided SKILL.md workflows.

Skip if: Skip when unrelated tooling already covers the task without this skill's documented flow.

When should I use this skill?

Use when building high-performance async Python APIs with FastAPI and Pydantic V2. Invoke to create REST endpoints, define Pydantic models, implement authentication flows, set up async SQLAlchemy data

What you get

Repeatable fastapi-expert workflows with grounded commands and expected outputs.

  • engine setup
  • ORM models
  • session factory code

Files

SKILL.mdMarkdownGitHub ↗

FastAPI Expert

Deep expertise in async Python, Pydantic V2, and production-grade API development with FastAPI.

When to Use This Skill

  • Building REST APIs with FastAPI
  • Implementing Pydantic V2 validation schemas
  • Setting up async database operations
  • Implementing JWT authentication/authorization
  • Creating WebSocket endpoints
  • Optimizing API performance

Core Workflow

1. Analyze requirements — Identify endpoints, data models, auth needs 2. Design schemas — Create Pydantic V2 models for validation 3. Implement — Write async endpoints with proper dependency injection 4. Secure — Add authentication, authorization, rate limiting 5. Test — Write async tests with pytest and httpx; run pytest after each endpoint group and verify OpenAPI docs at /docs

Checkpoint after each step: confirm schemas validate correctly, endpoints return expected HTTP status codes, and /docs reflects the intended API surface before proceeding.

Minimal Complete Example

Schema + endpoint + dependency injection in one cohesive unit:

# schemas.py
from pydantic import BaseModel, EmailStr, field_validator, model_config

class UserCreate(BaseModel):
    model_config = model_config(str_strip_whitespace=True)

    email: EmailStr
    password: str
    name: str | None = None

    @field_validator("password")
    @classmethod
    def password_strength(cls, v: str) -> str:
        if len(v) < 8:
            raise ValueError("Password must be at least 8 characters")
        return v

class UserResponse(BaseModel):
    model_config = model_config(from_attributes=True)

    id: int
    email: EmailStr
    name: str | None = None
# routers/users.py
from fastapi import APIRouter, Depends, HTTPException, status
from sqlalchemy.ext.asyncio import AsyncSession
from typing import Annotated

from app.database import get_db
from app.schemas import UserCreate, UserResponse
from app import crud

router = APIRouter(prefix="/users", tags=["users"])

DbDep = Annotated[AsyncSession, Depends(get_db)]

@router.post("/", response_model=UserResponse, status_code=status.HTTP_201_CREATED)
async def create_user(payload: UserCreate, db: DbDep) -> UserResponse:
    existing = await crud.get_user_by_email(db, payload.email)
    if existing:
        raise HTTPException(status_code=status.HTTP_409_CONFLICT, detail="Email already registered")
    return await crud.create_user(db, payload)
# crud.py
from sqlalchemy import select
from sqlalchemy.ext.asyncio import AsyncSession
from app.models import User
from app.schemas import UserCreate
from app.security import hash_password

async def get_user_by_email(db: AsyncSession, email: str) -> User | None:
    result = await db.execute(select(User).where(User.email == email))
    return result.scalar_one_or_none()

async def create_user(db: AsyncSession, payload: UserCreate) -> User:
    user = User(email=payload.email, hashed_password=hash_password(payload.password), name=payload.name)
    db.add(user)
    await db.commit()
    await db.refresh(user)
    return user

JWT Authentication Snippet

# security.py
from datetime import datetime, timedelta, timezone
from jose import JWTError, jwt
from fastapi import Depends, HTTPException, status
from fastapi.security import OAuth2PasswordBearer
from typing import Annotated

SECRET_KEY = "read-from-env"  # use os.environ / settings
ALGORITHM = "HS256"
oauth2_scheme = OAuth2PasswordBearer(tokenUrl="/auth/token")

def create_access_token(subject: str, expires_delta: timedelta = timedelta(minutes=30)) -> str:
    payload = {"sub": subject, "exp": datetime.now(timezone.utc) + expires_delta}
    return jwt.encode(payload, SECRET_KEY, algorithm=ALGORITHM)

async def get_current_user(token: Annotated[str, Depends(oauth2_scheme)]) -> str:
    try:
        data = jwt.decode(token, SECRET_KEY, algorithms=[ALGORITHM])
        subject: str | None = data.get("sub")
        if subject is None:
            raise ValueError
        return subject
    except (JWTError, ValueError):
        raise HTTPException(status_code=status.HTTP_401_UNAUTHORIZED, detail="Invalid credentials")

CurrentUser = Annotated[str, Depends(get_current_user)]

Reference Guide

Load detailed guidance based on context:

TopicReferenceLoad When
Pydantic V2references/pydantic-v2.mdCreating schemas, validation, model_config
SQLAlchemyreferences/async-sqlalchemy.mdAsync database, models, CRUD operations
Endpointsreferences/endpoints-routing.mdAPIRouter, dependencies, routing
Authenticationreferences/authentication.mdJWT, OAuth2, get_current_user
Testingreferences/testing-async.mdpytest-asyncio, httpx, fixtures
Django Migrationreferences/migration-from-django.mdMigrating from Django/DRF to FastAPI

Constraints

MUST DO

  • Use type hints everywhere (FastAPI requires them)
  • Use Pydantic V2 syntax (field_validator, model_validator, model_config)
  • Use Annotated pattern for dependency injection
  • Use async/await for all I/O operations
  • Use X | None instead of Optional[X]
  • Return proper HTTP status codes
  • Document endpoints (auto-generated OpenAPI)

MUST NOT DO

  • Use synchronous database operations
  • Skip Pydantic validation
  • Store passwords in plain text
  • Expose sensitive data in responses
  • Use Pydantic V1 syntax (@validator, class Config)
  • Mix sync and async code improperly
  • Hardcode configuration values

Output Templates

When implementing FastAPI features, provide: 1. Schema file (Pydantic models) 2. Endpoint file (router with endpoints) 3. CRUD operations if database involved 4. Brief explanation of key decisions

Knowledge Reference

FastAPI, Pydantic V2, async SQLAlchemy, Alembic migrations, JWT/OAuth2, pytest-asyncio, httpx, BackgroundTasks, WebSockets, dependency injection, OpenAPI/Swagger

Documentation

Related skills

How it compares

Use fastapi-expert for async SQLAlchemy in FastAPI; use a general Python skill for scripts without web API or ORM needs.

FAQ

Who is fastapi-expert for?

Developers using agents to execute fastapi expert workflows from SKILL.md.

When should I use fastapi-expert?

Use when building high-performance async Python APIs with FastAPI and Pydantic V2. Invoke to create REST endpoints, define Pydantic models, implement authentication flows, set up a

Is fastapi-expert safe to install?

Review the Security Audits panel on this page before installing in production.

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