sqlalchemy-models

Guidance for defining SQLAlchemy models, writing database queries, and creating Alembic migrations. SQLAlchemy maps Python objects to database tables, while Alembic records controlled database-structure changes.

In plain words
What is it for?
Use it when adding or changing tables, writing queries, checking database-layer conventions, or creating migrations that work with SQLite and PostgreSQL.
Why use it?
It keeps database code consistent and prevents unsafe manual schema changes. It also sets conventions for modern asynchronous SQLAlchemy projects.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/tedivm/robs_awesome_python_template/sqlalchemy-models
Any agent
npx skills add tedivm/robs_awesome_python_template --skill sqlalchemy-models
Clone the repo
git clone --depth 1 https://github.com/tedivm/robs_awesome_python_template

Made for: Claude Code, Codex.

Per session 44 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,557 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00044 $0.01557
Opus 5 $0.00022 $0.00779
Sonnet 5 $0.00009 $0.00311
Haiku 4.5 $0.00004 $0.00156

Measured 2d ago against content hash 4bb09766dae8, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

sqlalchemy-models 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 2d 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.

{{cookiecutter.__package_slug}}/.agents/skills/sqlalchemy-models/SKILL.md · 186 lines

How it starts

The opening of the file, as written. The whole thing — 186 lines — stays where its author put it; the contents beside it link to each section on GitHub.

SQLAlchemy Models and Migrations

context7: If the mcp_context7 tool is available, load the full sqlalchemy and alembic documentation before debugging, creating or modifying models or queries, or writing migrations:

mcp_context7_resolve-library-id: "sqlalchemy"
mcp_context7_get-library-docs: <resolved-id>
mcp_context7_resolve-library-id: "alembic"
mcp_context7_get-library-docs: <resolved-id>

Guidelines and patterns for database models, queries, and Alembic migrations in this codebase.


Core Requirements

  • Always use async SQLAlchemy APIs (never synchronous).
  • Always use SQLAlchemy 2.0 syntax.
  • Represent database tables with the declarative class system (Base subclass).
  • Use Alembic for all schema changes — never alter the schema manually.
  • Migrations must be compatible with both SQLite and PostgreSQL.

Model Definition

Models live in {{cookiecutter.__package_slug}}/models/. Import and extend the shared Base from {{cookiecutter.__package_slug}}.models.base.

from uuid import UUID, uuid4

from sqlalchemy.orm import Mapped, mapped_column

from {{cookiecutter.__package_slug}}.models.base import Base


class User(Base):
    __tablename__ = "users"

    id: Mapped[UUID] = mapped_column(primary_key=True, default=uuid4)
    email: Mapped[str] = mapped_column(unique=True)
    name: Mapped[str]
    is_active: Mapped[bool] = mapped_column(default=True)

Typing Conventions

Pattern Meaning
Mapped[str] NOT NULL column
Mapped[str | None] NULLable column
mapped_column(default=...) Server-side / Python-side default
mapped_column(primary_key=True) Primary key
mapped_column(unique=True) Unique constraint
mapped_column(index=True) Index

Read the full file on GitHub · 186 lines

Changes

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.

  1. 2d ago First seen · 186 lines · 44 tokens per session scan A 4bb09766dae8

Subscribe to this mod's changes

sqlalchemy-models is a skill published in the GitHub repository tedivm/robs_awesome_python_template (309 stars, last pushed 3mo ago), licensed MIT. It adds 44 tokens to every session and 1,557 once invoked, about $0.0002 per session on Opus 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.

Related

Other skills, from other repositories

background-task

Add or modify work that runs outside the request/response cycle — emails, document ingestion, webhooks, cleanups, scheduled jobs. Use when something is slow or fire-and-forget, or when adding a periodic/cron task. This project's queue is {{ cookiecutter.backgroundtasks }}.

vstorm-co/full-stack-ai-agent-template · 62 tokens

agent-tool

Add a new tool/function the AI agent can call (e.g. look something up, hit an external API, perform an action). Use when extending the assistant's capabilities, wiring a new function into the agent, or when the model needs a new action. This project uses {{ cookiecutter.aiframework }}.

vstorm-co/full-stack-ai-agent-template · 66 tokens

frontend-feature

Build a new page, view, or data-driven feature in the Next.js frontend. Use when adding a route under the dashboard/marketing area, wiring UI to a backend endpoint, adding client state, or creating a localized page. Covers App Router, data fetching, Zustand stores, and i18n.

vstorm-co/full-stack-ai-agent-template · 64 tokens

rag-knowledge

Work with the RAG knowledge base — ingest documents, run semantic search, manage collections, or add a sync source/connector (Google Drive, S3). Use when populating or debugging the knowledge base, tuning retrieval, or adding a new document source. This project uses {{ cookiecutter.vectorstore }} + {{…

vstorm-co/full-stack-ai-agent-template · 76 tokens

alembic-migration

Create, review, and apply database schema changes with Alembic. Use whenever a SQLAlchemy model is added or changed, a column/index/constraint needs to change, or a data backfill is required — anything that alters the PostgreSQL schema.

vstorm-co/full-stack-ai-agent-template · 56 tokens

channel-bot

Work with messaging-channel bots (Telegram / Slack) — register a bot, route inbound messages through the AI agent, handle webhooks vs polling, or add a new channel adapter. Use when wiring chat into a messaging platform or debugging bot delivery.

vstorm-co/full-stack-ai-agent-template · 53 tokens