fastapi

srbhr/resume-matcher · updated Apr 8, 2026

MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.

$npx skills add https://github.com/srbhr/resume-matcher --skill fastapi
0 commentsdiscussion
summary

Production-tested patterns for FastAPI with Pydantic v2, SQLAlchemy 2.0 async, and JWT authentication.

skill.md

FastAPI Skill

Production-tested patterns for FastAPI with Pydantic v2, SQLAlchemy 2.0 async, and JWT authentication.

Latest Versions (verified January 2026):

  • FastAPI: 0.128.0
  • Pydantic: 2.11.7
  • SQLAlchemy: 2.0.30
  • Uvicorn: 0.35.0
  • python-jose: 3.3.0

Requirements:

  • Python 3.9+ (Python 3.8 support dropped in FastAPI 0.125.0)
  • Pydantic v2.7.0+ (Pydantic v1 support completely removed in FastAPI 0.128.0)

Quick Start

Project Setup with uv

# Create project
uv init my-api
cd my-api

# Add dependencies
uv add fastapi[standard] sqlalchemy[asyncio] aiosqlite python-jose[cryptography] passlib[bcrypt]

# Run development server
uv run fastapi dev src/main.py

Minimal Working Example

# src/main.py
from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI(title="My API")

class Item(BaseModel):
    name: str
    price: float

@app.get("/")
async def root():
    return {"message": "Hello World"}

@app.post("/items")
async def create_item(item: Item):
    return item

Run: uv run fastapi dev src/main.py

Docs available at: http://127.0.0.1:8000/docs


Project Structure (Domain-Based)

For maintainable projects, organize by domain not file type:

my-api/
├── pyproject.toml
├── src/
│   ├── __init__.py
│   ├── main.py              # FastAPI app initialization
│   ├── config.py            # Global settings
│   ├── database.py          # Database connection
│   │
│   ├── auth/                # Auth domain
│   │   ├── __init__.py
│   │   ├── router.py        # Auth endpoints
│   │   ├── schemas.py       # Pydantic models
│   │   ├── models.py        # SQLAlchemy models
│   │   ├── service.py       # Business logic
│   │   └── dependencies.py  # Auth dependencies
│   │
│   ├── items/               # Items domain
│   │   ├── __init__.py
│   │   ├── router.py
│   │   ├── schemas.py
│   │   ├── models.py
│   │   └── service.py
│   │
│   └── shared/              # Shared utilities
│       ├── __init__.py
│       └── exceptions.py
└── tests/
    └── test_main.py

Core Patterns

Pydantic Schemas (Validation)

# src/items/schemas.py
from pydantic import BaseModel, Field, ConfigDict
from datetime import datetime
from enum import Enum

class ItemStatus(str, Enum):
    DRAFT = "draft"
    PUBLISHED = "published"
    ARCHIVED = "archived"

class ItemBase(BaseModel):
    name: str = Field(..., min_length=1, max_length=100)
    description: str | None = Field(None, max_length=500)
    price: float = Field(..., gt=0, description="Price must be positive")
    status: ItemStatus = ItemStatus.DRAFT

class ItemCreate(ItemBase):
    pass

class ItemUpdate(BaseModel):
    name: str | None = Field(None, min_length=1, max_length=100)
    description: str | None = None
    price: float | None = Field(None, gt=0)
    status: ItemStatus | None = None

class ItemResponse(ItemBase):
    id: int
    created_at: datetime

    model_config = ConfigDict(from_attributes=True)

Key Points:

  • Use Field() for validation constraints
  • Separate Create/Update/Response schemas
  • from_attributes=True enables SQLAlchemy model conversion
  • Use str | None (Python 3.10+) not Optional[str]

SQLAlchemy Models (Database)

# src/items/models.py
from sqlalchemy import String, Float, DateTime, Enum as SQLEnum
from sqlalchemy.orm import Mapped, mapped_column
from datetime import datetime
from src.database import Base
from src.items.schemas import ItemStatus

class Item(Base):
    __tablename__ = "items"

    id: Mapped[int] = mapped_column(primary_key=True)
    name: Mapped[str] = mapped_column(String(100))
    description: Mapped[str | None] = mapped_column(String(500), nullable=True)
    price: Mapped[float] = mapped_column(Float)
    status: Mapped[ItemStatus] = mapped_column(
        SQLEnum(ItemStatus), default=ItemStatus.DRAFT
    )
    created_at: Mapped[datetime] = mapped_column(
        DateTime, default=datetime.utcnow
    )

Database Setup (Async SQLAlchemy 2.0)

# src/database.py
from sqlalchemy.ext.asyncio import create_async_engine, AsyncSession, async_sessionmaker
from sqlalchemy.orm import DeclarativeBase

DATABASE_URL = "sqlite+aiosqlite:///./database.db"

engine = create_async_engine(DATABASE_URL, echo=True)
async_session = async_sessionmaker(engine, expire_on_commit=False)

class Base(DeclarativeBase):
    pass

async def get_db():
    async with async_session() as session:
        try:
            yield session
            await session.commit()
        except Exception:
            await session.rollback()
            raise

Router Pattern

# src/items/router.py
from fastapi import APIRouter, Depends, HTTPException, status
from sqlalchemy.ext.asyncio import AsyncSession
from sqlalchemy import select

from src.database import get_db
from src.items import schemas, models

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

@router.get("", response_model=list[schemas.ItemResponse
how to use fastapi

How to use fastapi 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
2

Execute installation command

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

$npx skills add https://github.com/srbhr/resume-matcher --skill fastapi

The skills CLI fetches fastapi from GitHub repository srbhr/resume-matcher 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

Reload or restart Cursor to activate fastapi. Access the skill through slash commands (e.g., /fastapi) 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.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

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

4.858 reviews
  • Sofia Gonzalez· Dec 28, 2024

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

  • Pratham Ware· Dec 24, 2024

    fastapi reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Chaitanya Patil· Dec 16, 2024

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

  • Sofia Jackson· Dec 12, 2024

    Useful defaults in fastapi — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Min Flores· Dec 4, 2024

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

  • Jin Kim· Nov 23, 2024

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

  • Mateo Smith· Nov 19, 2024

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

  • Anaya Ghosh· Nov 19, 2024

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

  • Sofia White· Nov 15, 2024

    fastapi reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Piyush G· Nov 7, 2024

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

showing 1-10 of 58

1 / 6