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.
npx agentmods add rules/ofershap/fastapi-best-practices/best-practicesgit clone --depth 1 https://github.com/ofershap/fastapi-best-practicesWhat 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 | $0.00189 | $0.00189 |
| Opus 5 | $0.00095 | $0.00095 |
| Sonnet 5 | $0.00038 | $0.00038 |
| Haiku 4.5 | $0.00019 | $0.00019 |
Grade A, and why
best-practices 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.
What it actually says
Use async def for I/O-bound endpoints and def for CPU-bound. Never make all endpoints async or all sync.
Use Depends() for dependency injection. Never use global variables for shared resources like database sessions or config.
Use Pydantic v2 patterns (model_validator, field_validator, ConfigDict). Never use v1 @validator or class Config.
Use lifespan context manager for startup/shutdown. Never use deprecated @app.on_event decorators.
Use APIRouter for route organization. Never put all routes in a single file.
Use BackgroundTasks for fire-and-forget work after response. Never use asyncio.create_task for request-scoped tasks.
Use response_model on path operations. Never return raw dicts without schema validation.
Use status codes from fastapi.status. Never use magic numbers like 404 or 401.
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.
- yesterday First seen · 21 lines · 189 tokens per session scan A 8092fc6191d5
best-practices is a cursor rule published in the GitHub repository ofershap/fastapi-best-practices (1 stars, last pushed 6mo ago), licensed MIT. It adds 189 tokens to every session, about $0.0009 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.
Other cursor rules, from other repositories
best-practices
Python Best Practices - enforces current best practices when working with Python code.
avoid_source_deduplication
name: avoid-source-deduplication description: Preserve unique source entries in search results to maintain proper citation tracking globs: ['/connectorservice.py', '/searchservice.py'] alwaysApply: true.
consistent_container_image_sources
name: consistent-container-image-sources description: Maintain consistent image sources in Docker compose files using authorized registries globs: ['/docker-compose.yml', '/docker-compose..yml'] alwaysApply: true.
cursorrules
use pnpm as default package manager.
niyam-conventions
Niyam is a full-stack AI-powered Applicant Tracking System (ATS) — a modern alternative to Greenhouse/Lever/SmartRecruiters.
stock-data
Stock data domain (TW/US markets, FinMind, Massive, ticker insights, charts, translations). Auto-attached when editing stock-related code.