Python SQL toolkit and ORM with modern query builder, relationship mapping, and async support.
Works with
Supports SQLAlchemy 2.0 modern API with type hints, select() queries, and Mapped[T] declarative models; includes async support via AsyncSession and AsyncSessionLocal
Covers one-to-many and many-to-many relationships with eager loading patterns ( selectinload , joinedload ) to prevent N+1 query problems
Integrates with FastAPI via dependency injection, Alembic for schema migrations, and conn
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionsqlalchemy-ormExecute the skills CLI command in your project's root directory to begin installation:
Fetches sqlalchemy-orm from bobmatnyc/claude-mpm-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate sqlalchemy-orm. Access via /sqlalchemy-orm in your agent's command palette.
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 environment. Always review source, verify the publisher, and test in isolation before production.
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Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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SQLAlchemy 2.0 introduced modern patterns with better type hints, improved query syntax, and async support.
Key Changes from 1.x:
select() instead of QueryMapped[T] and mapped_column() for type hintsSession.execute() for queriesAsyncSession# 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
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})"
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"
)
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)
from typing import Generator
def get_db() -> Generator[Session, ✓Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
✓Save 3-5 hours/week on communication overhead
Implementation Guide
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Steps
- 1Install product management skill
- 2Start with user story generation for known feature
- 3Progress to competitive analysis: research 2-3 competitors
- 4Use for roadmap prioritization: apply RICE/ICE scoring
- 5Draft stakeholder communications and refine based on feedback
- 6Build template library for recurring PM tasks
- 7Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
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4.6★★★★★61 reviews- SShikha Mishra★★★★★Dec 28, 2024
sqlalchemy-orm reduced setup friction for our internal harness; good balance of opinion and flexibility.
- SSophia Huang★★★★★Dec 20, 2024
sqlalchemy-orm reduced setup friction for our internal harness; good balance of opinion and flexibility.
- TTariq Kapoor★★★★★Dec 16, 2024
sqlalchemy-orm has been reliable in day-to-day use. Documentation quality is above average for community skills.
- AArya Liu★★★★★Dec 12, 2024
sqlalchemy-orm has been reliable in day-to-day use. Documentation quality is above average for community skills.
- GGanesh Mohane★★★★★Dec 8, 2024
Solid pick for teams standardizing on skills: sqlalchemy-orm is focused, and the summary matches what you get after install.
- AAmelia Reddy★★★★★Dec 8, 2024
sqlalchemy-orm is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- LLuis Park★★★★★Dec 4, 2024
Useful defaults in sqlalchemy-orm — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- XXiao Johnson★★★★★Nov 27, 2024
sqlalchemy-orm fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ZZara Harris★★★★★Nov 23, 2024
Registry listing for sqlalchemy-orm matched our evaluation — installs cleanly and behaves as described in the markdown.
- YYash Thakker★★★★★Nov 19, 2024
I recommend sqlalchemy-orm for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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