Borrowing it
Nothing to install: this file belongs to cohen-liel/hivemind. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/cohen-liel/hivemind/main/.claude/skills/sqlalchemy-orm/SKILL.mdgit clone --depth 1 https://github.com/cohen-liel/hivemindWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/cohen-liel/hivemind/sqlalchemy-orm)<a href="https://agentmods.dev/skills/cohen-liel/hivemind/sqlalchemy-orm"><img src="https://agentmods.dev/badge/skills/cohen-liel/hivemind/sqlalchemy-orm/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/cohen-liel/hivemind/sqlalchemy-orm"><img src="https://agentmods.dev/badge/skills/cohen-liel/hivemind/sqlalchemy-orm.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00032 | $0.00895 |
| Opus 5.5 | $0.00013 | $0.00358 |
| Sonnet 5.5 | $0.00006 | $0.00179 |
| Haiku 4.5 | $0.00003 | $0.00089 |
Grade A, and why
sqlalchemy-orm scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SQLAlchemy 2.0 Async ORM Patterns
Database Setup
# database.py
from sqlalchemy.ext.asyncio import create_async_engine, async_sessionmaker, AsyncSession
from sqlalchemy.orm import DeclarativeBase
engine = create_async_engine(
settings.DATABASE_URL, # postgresql+asyncpg://user:pass@host/db
pool_size=10,
max_overflow=20,
pool_pre_ping=True, # Verify connection before use
echo=settings.DEBUG,
)
AsyncSessionLocal = async_sessionmaker(engine, expire_on_commit=False)
class Base(DeclarativeBase):
pass
Model Pattern
from sqlalchemy import String, ForeignKey, func, text
from sqlalchemy.orm import Mapped, mapped_column, relationship
class TimestampMixin:
created_at: Mapped[datetime] = mapped_column(
server_default=func.now(), nullable=False
)
updated_at: Mapped[datetime] = mapped_column(
server_default=func.now(), onupdate=func.now(), nullable=False
)
class User(Base, TimestampMixin):
__tablename__ = "users"
id: Mapped[int] = mapped_column(primary_key=True)
email: Mapped[str] = mapped_column(String(255), unique=True, nullable=False, index=True)
name: Mapped[str] = mapped_column(String(100), nullable=False)
hashed_password: Mapped[str] = mapped_column(nullable=False)
is_active: Mapped[bool] = mapped_column(default=True, server_default=text("true"))
# Relationship
posts: Mapped[list["Post"]] = relationship("Post", back_populates="author", lazy="select")
class Post(Base, TimestampMixin):
__tablename__ = "posts"
id: Mapped[int] = mapped_column(primary_key=True)
user_id: Mapped[int] = mapped_column(ForeignKey("users.id"), nullable=False, index=True)
title: Mapped[str] = mapped_column(String(255), nullable=False)
body: Mapped[str] = mapped_column(nullable=False)
author: Mapped["User"] = relationship("User", back_populates="posts")
CRUD Patterns
# SELECT with filter
async def get_user(db: AsyncSession, user_id: int) -> User | None:
return await db.get(User, user_id)
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()
# SELECT with join (avoid N+1)
async def get_posts_with_authors(db: AsyncSession) -> list[Post]:
result = await db.execute(
select(Post).options(selectinload(Post.author)).order_by(Post.created_at.desc())
)
return list(result.scalars())
# INSERT
async def create_user(db: AsyncSession, data: UserCreate) -> User:
user = User(**data.model_dump())
db.add(user)
await db.flush() # Get ID without committing
await db.refresh(user)
return user
# UPDATE
async def update_user(db: AsyncSession, user_id: int, data: dict) -> User:
await db.execute(update(User).where(User.id == user_id).values(**data))
return await get_user(db, user_id)
# Bulk insert
async def bulk_create_posts(db: AsyncSession, posts: list[dict]):
await db.execute(insert(Post), posts)
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 107 lines · 32 tokens per session scan A a7fef59ec648
sqlalchemy-orm is a skill published in the GitHub repository cohen-liel/hivemind (112 stars, last pushed 6mo ago), licensed Apache-2.0. It adds 32 tokens to every session and 895 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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