fastapi-templates

wshobson/agents · updated Apr 8, 2026

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$npx skills add https://github.com/wshobson/agents --skill fastapi-templates
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summary

Production-ready FastAPI project structure with async patterns, dependency injection, and layered architecture.

  • Provides recommended directory layout separating API routes, models, schemas, services, and repositories for maintainable codebases
  • Includes base repository pattern for generic CRUD operations and service layer for business logic encapsulation
  • Demonstrates async/await patterns throughout, from database sessions to route handlers, with proper lifespan management and middlewa
skill.md

FastAPI Project Templates

Production-ready FastAPI project structures with async patterns, dependency injection, middleware, and best practices for building high-performance APIs.

When to Use This Skill

  • Starting new FastAPI projects from scratch
  • Implementing async REST APIs with Python
  • Building high-performance web services and microservices
  • Creating async applications with PostgreSQL, MongoDB
  • Setting up API projects with proper structure and testing

Core Concepts

1. Project Structure

Recommended Layout:

app/
├── api/                    # API routes
│   ├── v1/
│   │   ├── endpoints/
│   │   │   ├── users.py
│   │   │   ├── auth.py
│   │   │   └── items.py
│   │   └── router.py
│   └── dependencies.py     # Shared dependencies
├── core/                   # Core configuration
│   ├── config.py
│   ├── security.py
│   └── database.py
├── models/                 # Database models
│   ├── user.py
│   └── item.py
├── schemas/                # Pydantic schemas
│   ├── user.py
│   └── item.py
├── services/               # Business logic
│   ├── user_service.py
│   └── auth_service.py
├── repositories/           # Data access
│   ├── user_repository.py
│   └── item_repository.py
└── main.py                 # Application entry

2. Dependency Injection

FastAPI's built-in DI system using Depends:

  • Database session management
  • Authentication/authorization
  • Shared business logic
  • Configuration injection

3. Async Patterns

Proper async/await usage:

  • Async route handlers
  • Async database operations
  • Async background tasks
  • Async middleware

Implementation Patterns

Pattern 1: Complete FastAPI Application

# main.py
from fastapi import FastAPI, Depends
from fastapi.middleware.cors import CORSMiddleware
from contextlib import asynccontextmanager

@asynccontextmanager
async def lifespan(app: FastAPI):
    """Application lifespan events."""
    # Startup
    await database.connect()
    yield
    # Shutdown
    await database.disconnect()

app = FastAPI(
    title="API Template",
    version="1.0.0",
    lifespan=lifespan
)

# CORS middleware
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# Include routers
from app.api.v1.router import api_router
app.include_router(api_router, prefix="/api/v1")

# core/config.py
from pydantic_settings import BaseSettings
from functools import lru_cache

class Settings(BaseSettings):
    """Application settings."""
    DATABASE_URL: str
    SECRET_KEY: str
    ACCESS_TOKEN_EXPIRE_MINUTES: int = 30
    API_V1_STR: str = "/api/v1"

    class Config:
        env_file = ".env"

@lru_cache()
def get_settings() -> Settings:
    return Settings()

# core/database.py
from sqlalchemy.ext.asyncio import create_async_engine, AsyncSession
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker
from app.core.config import get_settings

settings = get_settings()

engine = create_async_engine(
    settings.DATABASE_URL,
    echo=True,
    future=True
)

AsyncSessionLocal = sessionmaker(
    engine,
    class_=AsyncSession,
    expire_on_commit=False
)

Base = declarative_base()

async def get_db() -> AsyncSession:
    """Dependency for database session."""
    async with AsyncSessionLocal() as session:
        try:
            yield session
            await session.commit()
        except Exception:
            await session.rollback()
            raise
        finally:
            await session.close()

Pattern 2: CRUD Repository Pattern

# repositories/base_repository.py
from typing import Generic, TypeVar, Type, Optional, List
from sqlalchemy.ext.asyncio import AsyncSession
from sqlalchemy import select
from pydantic import BaseModel

ModelType = TypeVar("ModelType")
CreateSchemaType = TypeVar("CreateSchemaType", bound=BaseModel)
UpdateSchemaType = TypeVar("UpdateSchemaType", bound=BaseModel)

class BaseRepository(Generic[ModelType, CreateSchemaType, UpdateSchemaType]):
    """Base repository for CRUD operations."""

    def __init__(self, model: Type[ModelType]):
        self.model = model

    async def get(self, db: AsyncSession, id: int) -> Optional[ModelType]:
        """Get by ID."""
        result = await db.execute(
            select(self.model).where(self.model.id == id)
        )
        return result.scalars().first()

    async def get_multi(
        self,
        db: AsyncSession,
        skip: int = 0,
        limit: int = 100
    ) -> List[ModelType]:
        """Get multiple records."""
        result = await db.execute(
            select(self.model).offset(skip).limit(limit)
        )
        return result.scalars().all()

    async def create(
        self,
        db: AsyncSession,
        obj_in: CreateSchemaType
    ) -> ModelType:
        """Create new record."""
        db_obj = self.model(**obj_in.dict())
        db.add(db_obj)
        await db.flush()
        await db.refresh(db_obj)
        
how to use fastapi-templates

How to use fastapi-templates on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add fastapi-templates
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/wshobson/agents --skill fastapi-templates

The skills CLI fetches fastapi-templates from GitHub repository wshobson/agents and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/fastapi-templates

Reload or restart Cursor to activate fastapi-templates. Access the skill through slash commands (e.g., /fastapi-templates) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

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Use Cases

Task Automation & Efficiency

Automate repetitive workflows and reduce manual effort

Example

Generate reports, summarize documents, draft communications

Save 3-5 hours per week on routine tasks

Knowledge Enhancement

Learn new skills, understand complex topics, get expert guidance

Example

Explain concepts, provide examples, suggest learning resources

Accelerate learning and skill development by 2x

Quality Improvement

Enhance output quality through reviews, suggestions, and refinements

Example

Review drafts, suggest improvements, catch errors

Improve work quality by 30-40% with less effort

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client with skill support
  • Clear understanding of task or problem to solve
  • Willingness to iterate and refine outputs

Time Estimate

15-45 minutes depending on use case complexity

Installation Steps

  1. 1.Install skill using provided installation command
  2. 2.Test with simple use case relevant to your work
  3. 3.Evaluate output quality and relevance
  4. 4.Iterate on prompts to improve results
  5. 5.Integrate into regular workflow if valuable

Common Pitfalls

  • Expecting perfect results without iteration
  • Not providing enough context in prompts
  • Using skill for tasks outside its intended scope
  • Accepting outputs without review and validation

Best Practices

✓ Do

  • +Start with clear, specific prompts
  • +Provide relevant context and constraints
  • +Review and refine all outputs before using
  • +Iterate to improve output quality
  • +Document successful prompt patterns

✗ Don't

  • Don't use without understanding skill limitations
  • Don't skip validation of outputs
  • Don't share sensitive information in prompts
  • Don't expect skill to replace human judgment

💡 Pro Tips

  • Be specific about desired format and style
  • Ask for multiple options to choose from
  • Request explanations to understand reasoning
  • Combine AI efficiency with human expertise

When to Use This

✓ Use When

Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.

✗ Avoid When

Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.

Learning Path

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.468 reviews
  • Advait Liu· Dec 28, 2024

    fastapi-templates is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Neel Harris· Dec 28, 2024

    Solid pick for teams standardizing on skills: fastapi-templates is focused, and the summary matches what you get after install.

  • Dhruvi Jain· Dec 24, 2024

    fastapi-templates has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Chaitanya Patil· Dec 20, 2024

    Registry listing for fastapi-templates matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Nia Li· Dec 8, 2024

    I recommend fastapi-templates for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Advait Jackson· Dec 4, 2024

    fastapi-templates fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Emma Robinson· Nov 27, 2024

    Keeps context tight: fastapi-templates is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Advait Shah· Nov 23, 2024

    We added fastapi-templates from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Kofi Garcia· Nov 19, 2024

    Solid pick for teams standardizing on skills: fastapi-templates is focused, and the summary matches what you get after install.

  • Neel Bhatia· Nov 19, 2024

    fastapi-templates is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

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