fastapi

A guide for writing and updating FastAPI web APIs and Pydantic data models. FastAPI is a Python framework for building HTTP services, while Pydantic checks and structures data.

In plain words
What is it for?
Use it when creating or refactoring FastAPI endpoints, dependencies, configuration, and Pydantic models.
Why use it?
It gives coding guidance for keeping FastAPI projects consistent and up to date as the framework changes.

Skill for Claude CodeCodex

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 skills/reflexioai/reflexio/fastapi
Any agent
npx skills add ReflexioAI/reflexio --skill fastapi
Clone the repo
git clone --depth 1 https://github.com/ReflexioAI/reflexio

Made for: Claude Code, Codex.

Per session 57 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,480 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin 100% copy Near-identical to another mod 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 $0.00057 $0.02480
Opus 5 $0.00028 $0.01240
Sonnet 5 $0.00011 $0.00496
Haiku 4.5 $0.00006 $0.00248

Measured yesterday against content hash e9f56d7e2036, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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.

Origin

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.

.agents/skills/fastapi/SKILL.md · 439 lines

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), not fastapi 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.

Read the full file on GitHub · 439 lines

Files

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.

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 · 439 lines · 57 tokens per session scan A e9f56d7e2036

Subscribe to this mod's changes

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.

Related

Other skills, from other repositories

review

Rigorous code review of all uncommitted changes. Analyzes architecture, code quality, security, and engineering best practices. Embeds questions and assumptions inline, then summarizes all proposed changes as a plan for user approval before any edits are made.

ReflexioAI/claude-smart · 52 tokens

fastapi

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.

ReflexioAI/claude-smart · 57 tokens

commit

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…

ReflexioAI/claude-smart · 122 tokens

check-and-test

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…

ReflexioAI/claude-smart · 97 tokens

update-pr

Update an existing pull request with new changes. Use when the user wants to update a PR, push follow-up changes to a PR, refresh a PR description, or sync a PR with latest commits. Triggers on: update pr, update-pr, update the pr, push to pr, refresh pr, sync pr, update pull request.

ReflexioAI/claude-smart · 71 tokens

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.

ReflexioAI/claude-smart · 74 tokens