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/martineserios/thebrana/fastapinpx skills add martineserios/thebrana --skill fastapigit clone --depth 1 https://github.com/martineserios/thebranaWrote 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/martineserios/thebrana/fastapi)<a href="https://agentmods.dev/skills/martineserios/thebrana/fastapi"><img src="https://agentmods.dev/badge/skills/martineserios/thebrana/fastapi.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 | $0.00021 | $0.02520 |
| Opus 5 | $0.00010 | $0.01260 |
| Sonnet 5 | $0.00004 | $0.00504 |
| Haiku 4.5 | $0.00002 | $0.00252 |
Grade A, and why
fastapi 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 5d 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.
How it starts
The opening of the file, as written. The whole thing — 443 lines — stays where its author put it; the contents beside it link to each section on GitHub.
FastAPI
Official FastAPI skill to write code with best practices, keeping up to date with new versions and features.
Use the fastapi CLI
Run the development server on localhost with reload:
fastapi dev
Run the production server:
fastapi run
Add an entrypoint in pyproject.toml
FastAPI CLI will read the entrypoint in pyproject.toml to know where the FastAPI app is declared.
[tool.fastapi]
entrypoint = "my_app.main:app"
Use fastapi with a path
When adding the entrypoint to pyproject.toml is not possible, or the user explicitly asks not to, or it's running an independent small app, you can pass the app file path to the fastapi command:
fastapi dev my_app/main.py
Prefer to set the entrypoint in pyproject.toml when possible.
Use Annotated
Always prefer the Annotated style for parameter and dependency declarations.
It keeps the function signatures working in other contexts, respects the types, allows reusability.
In Parameter Declarations
Use Annotated for parameter declarations, including Path, Query, Header, etc.:
from typing import Annotated
from fastapi import FastAPI, Path, Query
app = FastAPI()
@app.get("/items/{item_id}")
async def read_item(
item_id: Annotated[int, Path(ge=1, description="The item ID")],
q: Annotated[str | None, Query(max_length=50)] = None,
):
return {"message": "Hello World"}
instead of:
# DO NOT DO THIS
@app.get("/items/{item_id}")
async def read_item(
item_id: int = Path(ge=1, description="The item ID"),
q: str | None = Query(default=None, max_length=50),
):
return {"message": "Hello World"}
For Dependencies
Use Annotated for dependencies with Depends().
Unless asked not to, create a new type alias for the dependency to allow re-using it.
from typing import Annotated
from fastapi import Depends, FastAPI
app = FastAPI()
def get_current_user():
return {"username": "johndoe"}
CurrentUserDep = Annotated[dict, Depends(get_current_user)]
@app.get("/items/")
async def read_item(current_user: CurrentUserDep):
return {"message": "Hello World"}
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.
- 5d ago First seen · 443 lines · 21 tokens per session scan A ddfdc803ea71
fastapi is a skill published in the GitHub repository martineserios/thebrana (3 stars, last pushed today), licensed MIT. It adds 21 tokens to every session and 2,520 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.
Other skills, from other repositories
fastapi-expert
Expert-level FastAPI development for high-performance Python APIs with async support. Use when the user mentions Python, API, async, REST, OpenAPI, or Pydantic, or when the task involves FastAPI Features.
python
Use when building FastAPI applications, implementing async endpoints, setting up Pydantic schemas, working with SQLAlchemy, or writing pytest tests for Python backend services.
fastapi-patterns
FastAPI production patterns — routing, dependency injection, background tasks, streaming, error handling, and async. Use when building or reviewing a FastAPI service.
FastAPI Patterns
Use this skill when building Python APIs with FastAPI and you want consistent boundary validation, async correctness, and maintainable dependency injection patterns.
fastapi-endpoint
Plan and build production-ready FastAPI endpoints with async SQLAlchemy, Pydantic v2 models, dependency injection for auth, and pytest tests. Uses interview-driven planning to clarify data models, authentication method, pagination strategy, and caching before writing any code.
python-backend
Production Python async patterns including asyncio TaskGroup, FastAPI dependency injection and middleware, SQLAlchemy 2.0 async sessions, and database connection pool tuning. Python 3.11+ runtime concerns such as ExceptionGroup, cancellation semantics, and session rollback. Use when building async services, wiring…