SQLAlchemy ORM Skill

progressive_disclosure: entry_point: summary: "Python SQL toolkit and ORM with powerful query builder and relationship mapping" when_to_use: - "When building Python applications with databases" - "When needing complex SQL queries with type safety" - "When working with FastAPI/Flask/Django" - "When needing database migrations (Alembic)" quick_start: - "pip install sqlalchemy" - "Define models with declarative base" - "Create engine and session" - "Query with select() and commit()" token_estimate: entry: 70-85 full: 4500-5500

Core Concepts

SQLAlchemy 2.0 Modern API

SQLAlchemy 2.0 introduced modern patterns with better type hints, improved query syntax, and async support.

Key Changes from 1.x:

• select() instead of Query
• Mapped[T] and mapped_column() for type hints
• Explicit Session.execute() for queries
• Better async support with AsyncSession

Installation

# Core SQLAlchemy
pip install sqlalchemy

# With async support
pip install sqlalchemy[asyncio] aiosqlite  # SQLite
pip install sqlalchemy[asyncio] asyncpg    # PostgreSQL

# With Alembic for migrations
pip install alembic

# FastAPI integration
pip install fastapi sqlalchemy

Declarative Models (SQLAlchemy 2.0)

Basic Model Definition

from datetime import datetime
from typing import Optional
from sqlalchemy import String, DateTime, ForeignKey, func
from sqlalchemy.orm import DeclarativeBase, Mapped, mapped_column, relationship

# Base class for all models
class Base(DeclarativeBase):
    pass

# User model with type hints
class User(Base):
    __tablename__ = "users"

    # Primary key
    id: Mapped[int] = mapped_column(primary_key=True)

    # Required fields
    email: Mapped[str] = mapped_column(String(255), unique=True, index=True)
    username: Mapped[str] = mapped_column(String(50), unique=True)
    hashed_password: Mapped[str] = mapped_column(String(255))

    # Optional fields
    full_name: Mapped[Optional[str]] = mapped_column(String(100))
    is_active: Mapped[bool] = mapped_column(default=True)

    # Timestamps with server defaults
    created_at: Mapped[datetime] = mapped_column(
        DateTime(timezone=True),
        server_default=func.now()
    )
    updated_at: Mapped[datetime] = mapped_column(
        DateTime(timezone=True),
        server_default=func.now(),
        onupdate=func.now()
    )

    # Relationships
    posts: Mapped[list["Post"]] = relationship(back_populates="author")

    def __repr__(self) -> str:
        return f"User(id={self.id}, email={self.email})"

Relationships

One-to-Many:

class Post(Base):
    __tablename__ = "posts"

    id: Mapped[int] = mapped_column(primary_key=True)
    title: Mapped[str] = mapped_column(String(200))
    content: Mapped[str]
    user_id: Mapped[int] = mapped_column(ForeignKey("users.id"))

    # Relationship with back_populates
    author: Mapped["User"] = relationship(back_populates="posts")
    tags: Mapped[list["Tag"]] = relationship(
        secondary="post_tags",
        back_populates="posts"
    )

Many-to-Many:

from sqlalchemy import Table, Column, Integer, ForeignKey

# Association table
post_tags = Table(
    "post_tags",
    Base.metadata,
    Column("post_id", Integer, ForeignKey("posts.id"), primary_key=True),
    Column("tag_id", Integer, ForeignKey("tags.id"), primary_key=True)
)

class Tag(Base):
    __tablename__ = "tags"

    id: Mapped[int] = mapped_column(primary_key=True)
    name: Mapped[str] = mapped_column(String(50), unique=True)

    posts: Mapped[list["Post"]] = relationship(
        secondary=post_tags,
        back_populates="tags"
    )

Database Setup

Engine and Session Configuration

from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker, Session
from sqlalchemy.pool import QueuePool

# Database URL formats
# SQLite: sqlite:///./database.db
# PostgreSQL: postgresql://user:pass@localhost/dbname
# MySQL: mysql+pymysql://user:pass@localhost/dbname

DATABASE_URL = "postgresql://user:pass@localhost/mydb"

# Create engine with connection pooling
engine = create_engine(
    DATABASE_URL,
    poolclass=QueuePool,
    pool_size=5,
    max_overflow=10,
    pool_pre_ping=True,  # Check connection before using
    echo=False  # Set True for SQL logging
)

# Session factory
SessionLocal = sessionmaker(
    bind=engine,
    autocommit=False,
    autoflush=False,
    expire_on_commit=False
)

# Create tables
Base.metadata.create_all(bind=engine)

Dependency Injection (FastAPI Pattern)

from typing import Generator

def get_db() -> Generator[Session, 
how to use sqlalchemy-orm
CursorClaude CodeClineCodexWindsurfGooseGitHub CopilotVS CodeGemini CLIKiloOpencodeKimi CLIRoo ClineAiderContinueZedBolt.newLovablev0Replit AgentSweepSmol DeveloperDevinCavemanAmpAntigravity
How to use sqlalchemy-orm on Cursor
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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 sqlalchemy-orm
2
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
$npx skills add https://github.com/bobmatnyc/claude-mpm-skills --skill sqlalchemy-ormcopy
The skills CLI fetches sqlalchemy-orm from GitHub repository bobmatnyc/claude-mpm-skills 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?
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│ ── Universal (.agents/skills) ── always included ────
│   • Amp
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│   • Cline
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│ ●Cursor(selected)
│   • Cursor
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4
Verify installation
Confirm successful installation by checking the skill directory location:
.cursor/skills/sqlalchemy-orm
Reload or restart Cursor to activate sqlalchemy-orm. Access the skill through slash commands (e.g., /sqlalchemy-orm) 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.
Additional Resources
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