sqlmodel

Guidance for satisfying the type checker when writing SQLModel database queries. SQLModel is a Python library for defining database models and queries with type hints.

In plain words
What is it for?
Use it when a SQLModel query involving selectinload, delete, or in_ produces type errors even though the database operation is valid.
Why use it?
Some valid relationship-loading, deletion, and filtering queries can still confuse the type checker. The examples show how to make the column types explicit so these queries pass checking.

Cursor rule for Cursor

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 rules/promptly-technologies-llc/fastapi-jinja2-postgres-webapp/sqlmodel
Clone the repo
git clone --depth 1 https://github.com/Promptly-Technologies-LLC/fastapi-jinja2-postgres-webapp

Made for: Cursor.

Per session 11 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 243 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 $0.00011 $0.00243
Opus 5 $0.00005 $0.00121
Sonnet 5 $0.00002 $0.00049
Haiku 4.5 $0.00001 $0.00024

Measured 2d ago against content hash 9341305b5f8f, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

sqlmodel 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.

.cursor/rules/sqlmodel.mdc · 28 lines

What it actually says

Complex SQLModel queries sometimes cause the type checker to choke, even though the queries are valid.

For instance, this error sometimes arises when using selectinload:

'error: Argument 1 to "selectinload" has incompatible type "SomeModel"; expected "Literal['*'] | QueryableAttribute[Any]"'

The solution is to explicitly coerce the argument to the appropriate SQLModel type.

E.g., we can resolve the error above by casting the eager-loaded relationship to InstrumentedAttribute:

session.exec(select(SomeOtherModel).options(selectinload(cast(InstrumentedAttribute, SomeOtherModel.some_model))))

Similarly, sometimes we get type checker errors when using delete or comparison operators like in_:

'error: Item "int" of "Optional[int]" has no attribute "in_"'

These can be resolved by wrapping the column in col to let the type checker know these are column objects:

session.exec(select(SomeModel).where(col(SomeModel.id).in_([1,2])))
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 · 28 lines · 11 tokens per session scan A 9341305b5f8f

Subscribe to this mod's changes

sqlmodel is a cursor rule published in the GitHub repository Promptly-Technologies-LLC/fastapi-jinja2-postgres-webapp (10 stars, last pushed 26d ago), licensed MIT. It adds 11 tokens to every session and 243 once invoked, about $0.0001 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 2026-08-31.