Jeffallan/claude-skills

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.

95Collecting
See how to use itView GitHub source
npx skills add https://github.com/Jeffallan/claude-skills --skill "skills/fastapi-expert"
Automated source guide

Source checked Jul 28, 2026·Refresh due Oct 26, 2026

Reorganized from the pinned upstream SKILL.md

Turn fastapi-expert's source instructions into a guide you can follow

According to the pinned SKILL.md from Jeffallan/claude-skills: Deep expertise in async Python, Pydantic V2, and production-grade API development with FastAPI.

npx skills add https://github.com/Jeffallan/claude-skills --skill "skills/fastapi-expert"
Check the pinned source

Best fit

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

Bring this context

  • A concrete task that matches the documented purpose of fastapi-expert.
  • The files, examples, or context the task depends on.
  • Your constraints, target environment, and definition of done.

Expected outputs

  • Schema + endpoint + dependency injection in one cohesive unit:
  • Schema file (Pydantic models)
  • Endpoint file (router with endpoints)

Key source sections

Read fastapi-expert through these 5 source sections

Sections are extracted automatically from the pinned SKILL.md and link back to the source.

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…

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

When to Use This Skill

Building REST APIs with FastAPI

SKILL.md · When to Use This Skill
Building REST APIs with FastAPIImplementing Pydantic V2 validation schemasSetting up async database operations
03

Minimal Complete Example

Schema + endpoint + dependency injection in one cohesive unit:

SKILL.md · Minimal Complete Example
Schema + endpoint + dependency injection in one cohesive unit:
04

schemas.py

from pydantic import BaseModel, EmailStr, fieldvalidator, modelconfig

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

routers/users.py

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

SKILL.md · routers/users.py
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"])

SkillSignal prompt templates

Provide the task, context, and acceptance criteria

These prompts were written by SkillSignal from the source structure; they are not upstream text.

Task-start prompt

Confirm source fit, inputs, and outputs before acting.

Use fastapi-expert to help me with: [specific task]. Context: [files, data, or background]. Constraints: [environment, scope, and prohibited actions]. Before acting, check the pinned SKILL.md and explain which sections apply, what inputs are still missing, and what you will deliver.

Source-guided execution

Make the Agent explicitly follow the key extracted sections.

Apply the pinned fastapi-expert source to [task]. Pay particular attention to these source sections: “Core Workflow”, “When to Use This Skill”, “Minimal Complete Example”, “schemas.py”, “routers/users.py”. Preserve the important decision at each step. Mark facts not covered by the source as “needs confirmation” instead of inventing them. Then verify the result against my acceptance criteria: [criteria].

Result-review prompt

Check omissions, permissions, and source drift before delivery.

Review the current fastapi-expert result: (1) does it satisfy the original task; (2) were any applicable steps or limits in the pinned SKILL.md missed; (3) did it perform any unauthorized file, command, network, or data action; and (4) which conclusions remain unverified? List issues first, then fix only what the source or user authorization supports.

Output checklist

Verify each item before delivery

The task matches the purpose documented in the SKILL.md.

The source section “Core Workflow” has been checked.

The source section “When to Use This Skill” has been checked.

The source section “Minimal Complete Example” has been checked.

The source section “schemas.py” has been checked.

Inputs, constraints, and acceptance criteria are explicit.

Unverified facts, compatibility, and outcome claims are clearly marked.

Any file, command, network, or data action has been reviewed.

Choose a different workflow

When another Skill is the better fit

genomic-intelligence

Predict regulatory features, gene structure, and expression directly from DNA sequence using Genomic Intelligence's hosted transformer DNA language models — no local GPU or model weights. Six tasks over a REST API and a hosted MCP server (keyless public demo): promoter regions, splice donor/acceptor sites, enhancer activity, chromatin state, sequence-to-expression (log TPM), and de-novo gene annotation, plus a composite find-genes-then-predict-expression workflow. Use when the user has a gene sy

A separate implementation from K-Dense-AI/scientific-agent-skills; compare its source, maintenance signals, and permission requirements.

Open source detail

build-with-exa

Build applications and agents with Exa's API Platform: search, contents, answer, context, Agent API, monitors, websets, OpenAI-compatible endpoints, and exa-py / exa-js. Use when choosing Exa endpoints, writing Exa API calls, integrating semantic web search or research into products, or debugging Exa request shapes. Load references/ on demand for endpoint details.

A separate implementation from MoizIbnYousaf/marketing-cli; compare its source, maintenance signals, and permission requirements.

Open source detail

astropy

Core Python library for astronomy and astrophysics workflows that need Astropy APIs, including units/quantities, coordinates, FITS I/O, tables, time systems, WCS, and cosmology. Use when implementing or debugging astronomical data analysis code with Astropy.

A separate implementation from K-Dense-AI/scientific-agent-skills; compare its source, maintenance signals, and permission requirements.

Open source detail

FAQ

What does fastapi-expert do?

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

How do I start using fastapi-expert?

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

Which Agent platforms does it declare?

No dedicated Agent platform is declared in the pinned source record.

Repository stars
10,762
Repository forks
984
Quality
95/100
Source repository last pushed

Quality breakdown

Based on traceable docs and repository signals; stars are not treated as quality.

95/100
Documentation28/30
Specificity25/25
Maintenance17/20
Trust signals25/25

Compare before choosing

Related Agent Skills and source variants

These links are selected from shared tasks, functions, stacks, platforms, and same-name variants. Compare the source owner, documentation, permissions, and maintenance signals.

genomic-intelligence by k-dense-ai

Predict regulatory features, gene structure, and expression directly from DNA sequence using Genomic Intelligence's hosted transformer DNA language models — no local GPU or model weights. Six tasks over a REST API and a hosted MCP server (keyless public demo): promoter regions, splice donor/acceptor sites, enhancer activity, chromatin state, sequence-to-expression (log TPM), and de-novo gene annotation, plus a composite find-genes-then-predict-expression workflow. Use when the user has a gene sy

build-with-exa by MoizIbnYousaf

Build applications and agents with Exa's API Platform: search, contents, answer, context, Agent API, monitors, websets, OpenAI-compatible endpoints, and exa-py / exa-js. Use when choosing Exa endpoints, writing Exa API calls, integrating semantic web search or research into products, or debugging Exa request shapes. Load references/ on demand for endpoint details.

astropy by k-dense-ai

Core Python library for astronomy and astrophysics workflows that need Astropy APIs, including units/quantities, coordinates, FITS I/O, tables, time systems, WCS, and cosmology. Use when implementing or debugging astronomical data analysis code with Astropy.

mcp-builder by anthropics

Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).

code-documenter by jeffallan

Generates, formats, and validates technical documentation — including docstrings, OpenAPI/Swagger specs, JSDoc annotations, doc portals, and user guides. Use when adding docstrings to functions or classes, creating API documentation, building documentation sites, or writing tutorials and user guides. Invoke for OpenAPI/Swagger specs, JSDoc, doc portals, getting started guides.

View original Skill.mdThis page is parsed directly from the repository SKILL.md without editorial rewriting. Collected: Jul 28, 2026 · about 2 min

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

Skill path
skills/fastapi-expert/SKILL.md
Commit SHA
e8be415bc94d
Repository license
MIT
Data collected