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 skills/ronmkr/promptbook/fastapi-patternsnpx skills add ronmkr/PromptBook --skill fastapi-patternsgit clone --depth 1 https://github.com/ronmkr/PromptBookWrote 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/ronmkr/promptbook/fastapi-patterns)<a href="https://agentmods.dev/skills/ronmkr/promptbook/fastapi-patterns"><img src="https://agentmods.dev/badge/skills/ronmkr/promptbook/fastapi-patterns.svg" alt="Measured on agentmods" 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 | $0.00035 | $0.02024 |
| Opus 5 | $0.00017 | $0.01012 |
| Sonnet 5 | $0.00007 | $0.00405 |
| Haiku 4.5 | $0.00003 | $0.00202 |
Grade A, and why
fastapi-patterns 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.
This is a copy
100% identical to fastapi-patterns — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 328 lines — stays where its author put it; the contents beside it link to each section on GitHub.
FastAPI Patterns
Production-oriented patterns for FastAPI services.
When to Use
- Building or reviewing a FastAPI app.
- Splitting routers, schemas, dependencies, and database access.
- Writing async endpoints that call a database or external service.
- Adding authentication, authorization, OpenAPI docs, tests, or deployment settings.
- Checking a FastAPI PR for copy-pasteable examples and production risks.
How It Works
Treat the FastAPI app as a thin HTTP layer over explicit dependencies and service code:
main.pyowns app construction, middleware, exception handlers, and router registration.schemas/owns Pydantic request and response models.dependencies.pyowns database, auth, pagination, and request-scoped dependencies.services/orcrud/owns business and persistence operations.tests/overrides dependencies instead of opening production resources.
Prefer small routers and explicit response_model declarations. Keep raw ORM objects, secrets, and framework globals out of response schemas.
Project Layout
app/
|-- main.py
|-- config.py
|-- dependencies.py
|-- exceptions.py
|-- api/
| `-- routes/
| |-- users.py
| `-- health.py
|-- core/
| |-- security.py
| `-- middleware.py
|-- db/
| |-- session.py
| `-- crud.py
|-- models/
|-- schemas/
`-- tests/
Application Factory
Use a factory so tests and workers can build the app with controlled settings.
from contextlib import asynccontextmanager
from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from app.api.routes import health, users
from app.config import settings
from app.db.session import close_db, init_db
from app.exceptions import register_exception_handlers
@asynccontextmanager
async def lifespan(app: FastAPI):
await init_db()
yield
await close_db()
def create_app() -> FastAPI:
app = FastAPI(
title=settings.api_title,
version=settings.api_version,
lifespan=lifespan,
)
app.add_middleware(
CORSMiddleware,
allow_origins=settings.cors_origins,
allow_credentials=bool(settings.cors_origins),
allow_methods=["GET", "POST", "PUT", "PATCH", "DELETE"],
allow_headers=["Authorization", "Content-Type"],
)
register_exception_handlers(app)
app.include_router(health.router, prefix="/health", tags=["health"])
app.include_router(users.router, prefix="/api/v1/users", tags=["users"])
return app
app = create_app()
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 · 328 lines · 35 tokens per session scan A 6e809a690fbe
fastapi-patterns is a skill published in the GitHub repository ronmkr/PromptBook (2 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 35 tokens to every session and 2,024 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to fastapi-patterns, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
testing
Testing workflow and quality standards for writing and running tests. Use when: (1) Writing new tests, (2) Adding a new feature that needs tests, (3) Modifying logic that has existing tests, (4) Before claiming a task is complete.
bug-audit
Weekly multi-agent audit for serious bugs (data integrity, silent caps, staleness, timestamp math, trust boundaries). Fans out Sonnet scanners + Opus deep auditors, adversarially verifies every finding, files GitHub issues for confirmed critical/high bugs. Trigger: /bug-audit.
multi-llm-review
Run complete, evidence-backed code reviews through the local stdio gateway across the seven canonical CLI providers: Claude, Codex, Gemini, Grok, Mistral, Devin, and Cursor. Use for quality, security, correctness, or release validation that requires independent reviewers.
secure-orchestration
Orchestrate security-sensitive LLM work with the gateway's Claude-managed approval boundary, provider-native legacy controls, evidence-aware auditing, and complete no-limit review handling.
provider-codex
Track and maintain the upstream OpenAI Codex CLI contract. Use when OpenAI ships a Codex release, when a codex exec flag/sandbox/approval/resume/subcommand behaviour changes, or when an upstream scan flags drift. Process guidance only; src/upstream-contracts.ts is the mechanical source of truth.
retrospective-walk
Walk a human or agent through a diff, worktree, commit range, gateway job, or episode reference as a structured retrospective. Use after implement-review-fix or multi-LLM review cycles, when reviewing prior jobs or uncommitted work, or when durable evidence is needed for what changed, why it changed, who/when…