python-backend

Coding rules for a Python backend built with FastAPI, Beanie, and MongoDB, including asynchronous operations, data validation, authentication, and module organization.

In plain words
What is it for?
Use them when adding routes, services, schemas, database models, authentication dependencies, and error handling.
Why use it?
They reduce inconsistent implementations and prevent known mistakes such as returning database documents directly or using deprecated database drivers.

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/ntnhan94/ai-ready-project-template/python-backend
Clone the repo
git clone --depth 1 https://github.com/ntnhan94/ai-ready-project-template

Made for: Cursor.

Per session 0 Nothing until a file matches its globs; then the whole rule loads.
When invoked 167 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 $0.00000 $0.00167
Opus 5 $0.00000 $0.00084
Sonnet 5 $0.00000 $0.00033
Haiku 4.5 $0.00000 $0.00017

Measured yesterday against content hash 86fbb019c815, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

python-backend 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 yesterday.

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/python-backend.mdc · 17 lines

What it actually says

Python Backend Rules

  • Use async/await everywhere — FastAPI runs on an async event loop
  • Use Pydantic v2 for all request/response schemas
  • Never return Beanie Document objects from endpoints — use response schemas
  • Follow module pattern: router.py → service.py → schemas.py
  • Use Depends(get_current_user) and Depends(get_current_tenant) for auth
  • Use datetime.now(UTC) — never datetime.now() or datetime.utcnow()
  • Do not import motor — use PyMongo Async (Motor is deprecated)
  • Use HTTPException for expected errors
  • Register new Beanie Documents in app/core/database.py
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. yesterday First seen · 17 lines · 0 tokens per session scan A 86fbb019c815

Subscribe to this mod's changes

python-backend is a cursor rule published in the GitHub repository ntnhan94/ai-ready-project-template (2 stars, last pushed 5mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 167 tokens. 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.