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/reflexioai/reflexio/fastapinpx skills add ReflexioAI/reflexio --skill fastapigit clone --depth 1 https://github.com/ReflexioAI/reflexioWhat 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 | $0.00057 | $0.02480 |
| Opus 5 | $0.00028 | $0.01240 |
| Sonnet 5 | $0.00011 | $0.00496 |
| Haiku 4.5 | $0.00006 | $0.00248 |
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 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 — 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 — 439 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.
Project note: This project starts the server via
./run_services.sh(uvicorn), notfastapi dev. The patterns below still apply to all endpoint/model code.
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.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 439 lines · 57 tokens per session scan A e9f56d7e2036
fastapi is a skill published in the GitHub repository ReflexioAI/reflexio (338 stars, last pushed 2d ago), licensed Apache-2.0. It adds 57 tokens to every session and 2,480 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to fastapi, differing in 0 lines, and is treated as a copy.
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FastAPI best practices and conventions. Use when working with FastAPI APIs and Pydantic models for them. Keeps FastAPI code clean and up to date with the latest features and patterns, updated with new versions. Write new code or refactor and update old code.
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Git commit workflow with precommit hook handling, lint/type checking, README updates, and API reference updates. Use when the user wants to commit changes. Handles precommit hooks that modify files (formatting, linting) by re-staging and retrying. Runs ruff lint and pyright type checks on staged Python files, and…
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Run lint checks (ruff for Python, Biome for TS/JS), type checks (pyright for Python, tsc for TS/JS), and the standard pytest tiers (unit + e2e + tests skipped during pre-commit). Investigates failures to determine if they are application bugs or test issues, and fixes application bugs rather than weakening tests. Does…
update-pr
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create-pr
Create high-quality pull requests via gh pr create. Use when the user wants to create a PR, submit a PR, open a pull request, submit for review, or push changes for review. Triggers on: create a pr, create-pr, submit a pr, open a pull request, submit for review, make a pr, gh pr create.