Tested demoQuality 98/100

Jeffallan/claude-skills/skills/fastapi-expert/SKILL.md

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 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.

Source repository stars
11,156
Declared platforms
0
Static risk flags
0
Last source update
2026-08-07
Source checked
2026-08-25

Decision brief

What it does: where it fits

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

Best for

  • Building REST APIs with FastAPI
  • Implementing Pydantic V2 validation schemas
  • Setting up async database operations

Not for

  • Tasks that require unconfirmed production actions or broad system permissions.
  • Environments where the pinned source and install steps cannot be inspected.
Controlled single-run demoChecked 2026-08-20

What changed when the Skill was used

In this controlled same-task single run, enabling fastapi-expert changed the output from 2472 non-whitespace characters and 15 headings to 3034 characters and 17 headings. Matches among 8 signals extracted from the pinned source changed from 1 to 1. Both actual outputs are shown; this is a structural observation, not a quality score or a universal performance claim.

Same test task

Create an implementation guide for adding a webhook retry queue to a TypeScript service. Include prerequisites, steps, verification, and common mistakes. The deliverable must specifically reflect this user intent: 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.

Without the Skill
Screenshot of the actual model output for fastapi-expert without the Skill

Baseline: 2472 non-whitespace characters, 15 headings, and 63 list items.

With the Skill
Screenshot of the actual model output for fastapi-expert with the Skill

With Skill: 3034 non-whitespace characters, 17 headings, and 79 list items.

ObservationWithout SkillWith Skill
Source-signal coverage1/8: fastapi1/8: fastapi
Output structure2472 chars · 15 headings · 63 list items · 0 code blocks3034 chars · 17 headings · 79 list items · 0 code blocks
Verification and caution signals14 verification signals · 7 risk/limitation signals14 verification signals · 1 risk/limitation signals

A prompt you can use

Use the fastapi-expert Skill pinned at 882ef55e377d for my task. Follow its source-specific constraints around `fastapi-expert`, `fastapi`, `expert`, `minimal`, then return the finished deliverable with explicit assumptions, verification, failure conditions, and limits. Do not treat the Skill text as a factual source or claim that a single demonstration proves universal performance.

Method and limitationsExpand

Test method

  • Baseline and treatment used the same task, model (gpt-5.3-codex-low), and runner; the only planned difference was whether the complete target Skill text was injected.
  • The treatment used snapshot e8be415bc94d8d6ebddc2fb50e5d03c6e27d4319; the current source commit 882ef55e377dbf9a4dbe496bb41ac6ccd0e555cf was verified against content hash a496a69f2fff. The baseline explicitly prohibited loading any Skill or external rule file.
  • The same deterministic script counted characters, headings, lists, code blocks, verification terms, caution terms, and source signals in both artifacts. Source signals: `fastapi-expert`, `fastapi`, `expert`, `minimal`, `example`, `schemas`, `routers`, `users`.
  • The visuals are local screenshots of the actual Markdown artifacts in a fixed 1200 × 800 evidence canvas, not recreated product mockups. Raw JSON artifacts and request records are retained in the research directory.

Do not over-read this demo

  • This is one controlled demonstration per condition, not a multi-run statistical benchmark; the model is stochastic.
  • Character, structure, and keyword counts show observable differences but cannot by themselves prove correctness, originality, or business impact.
  • The task is a representative test designed for repeatability, not every real-world use of the Skill; rerun after a material source change.
Editorial review
SkillSignal editorial
Runner
Cursor Agent 2026.07.09-a3815c0
Model
gpt-5.3-codex-low
Refresh due
2026-11-18
Reviewed commit
882ef55e377dbf9a4dbe496bb41ac6ccd0e555cf
Test snapshot
e8be415bc94d8d6ebddc2fb50e5d03c6e27d4319

Compatibility matrix

Platform support, with evidence labels

PlatformStatusEvidenceWhat to check
CodexNot declaredNo explicit evidencePortability before use
Claude CodeNot declaredNo explicit evidencePortability before use
CursorNot declaredNo explicit evidencePortability before use
Gemini CLINot declaredNo explicit evidencePortability before use
Open the compatibility checker

Installation

Inspect first. Install second.

The source command is displayed only when detected. A safe inspection prompt is always available so your agent can explain every action before execution.

Source-detected install commandSource
npx skills add https://github.com/Jeffallan/claude-skills --skill "skills/fastapi-expert"
Safe inspection promptEditorial

Inspect the Agent Skill "fastapi-expert" from https://github.com/Jeffallan/claude-skills/blob/882ef55e377dbf9a4dbe496bb41ac6ccd0e555cf/skills/fastapi-expert/SKILL.md at commit 882ef55e377dbf9a4dbe496bb41ac6ccd0e555cf. List every install step, command, network request, credential, file read/write, external action, and rollback step. Explain whether it fits my task. Do not install or execute anything until I approve.

Workflow

What the source asks the agent to do

  1. 01

    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 a…

    Analyze requirements — Identify endpoints, data models, auth needsDesign schemas — Create Pydantic V2 models for validationImplement — Write async endpoints with proper dependency injection
  2. 02

    When to Use This Skill

    Building REST APIs with FastAPI

    Building REST APIs with FastAPIImplementing Pydantic V2 validation schemasSetting up async database operations
  3. 03

    Minimal Complete Example

    Schema + endpoint + dependency injection in one cohesive unit:

    Schema + endpoint + dependency injection in one cohesive unit:
  4. 04

    schemas.py

    from pydantic import BaseModel, EmailStr, fieldvalidator, modelconfig

    from pydantic import BaseModel, EmailStr, fieldvalidator, modelconfigclass UserCreate(BaseModel): modelconfig = modelconfig(strstripwhitespace=True)email: EmailStr password: str name: str | None = None
  5. 05

    routers/users.py

    from fastapi import APIRouter, Depends, HTTPException, status from sqlalchemy.ext.asyncio import AsyncSession from typing import Annotated

    from fastapi import APIRouter, Depends, HTTPException, status from sqlalchemy.ext.asyncio import AsyncSession from typing import Annotatedfrom app.database import getdb from app.schemas import UserCreate, UserResponse from app import crudrouter = APIRouter(prefix="/users", tags=["users"])

Permission review

Static risk signals and limitations

No configured static risk pattern was detected

This is not proof of safety. Runtime behavior, indirect dependencies, and hidden external systems are outside the static scan.

Evidence record

Why each signal appears

EvidenceSourceComputedTestedEditorial
SignalValueEvidence typeMeaning
Quality score98/100ComputedDocumentation, specificity, maintenance, and trust rules
Repository stars11,156SourceRepository attention, not individual Skill quality
Compatibility0 platformsSourceDeclared in the catalog source record
Usage guidetested outcome pageTestedGenerated or reviewed according to the visible evidence level

Pinned source

Provenance and original SKILL.md

Repository
Jeffallan/claude-skills
Skill path
skills/fastapi-expert/SKILL.md
Commit
882ef55e377dbf9a4dbe496bb41ac6ccd0e555cf
License
MIT
Collected
2026-08-25
Default branch
main
View the original SKILL.md

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

Frequently asked questions

What to verify before installation and use

What does the fastapi-expert source document cover?

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

How do I install fastapi-expert?

The source record exposes this install command: npx skills add https://github.com/Jeffallan/claude-skills --skill "skills/fastapi-expert". Inspect the command and pinned source before running it.

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