Qfastapi-expert

Qfastapi-expert is a skill for Claude Code, Codex from inho-team/qe-mcp. It costs 96 tokens per session (1,530 once invoked), scanned A, original, MIT.

A FastAPI development guide for building Python APIs that handle asynchronous work. FastAPI is a Python web framework, and Pydantic validates incoming and outgoing data.

In plain words
What is it for?
Use it to create REST endpoints, define validation models, connect to databases asynchronously, add JWT authentication, build WebSocket features, apply rate limits, and generate OpenAPI documentation.
Why use it?
It helps keep API data valid and supports authentication, database operations, testing, and generated documentation in a consistent structure.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to create REST endpoints, define validation models, connect to databases asynchronously, add JWT authentication, build WebSocket features, apply rate limits, and generate OpenAPI documentation.

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Install with agentmods
npx agentmods add skills/inho-team/qe-mcp/qfastapi-expert
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.

Any agent
npx skills add inho-team/qe-mcp --skill qfastapi-expert
Clone the repo
git clone --depth 1 https://github.com/inho-team/qe-mcp

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for Qfastapi-expert

README.md
[![agentmods](https://agentmods.dev/badge/skills/inho-team/qe-mcp/qfastapi-expert.svg)](https://agentmods.dev/skills/inho-team/qe-mcp/qfastapi-expert)
Your own site
<a href="https://agentmods.dev/skills/inho-team/qe-mcp/qfastapi-expert"><img src="https://agentmods.dev/badge/skills/inho-team/qe-mcp/qfastapi-expert.svg" alt="Measured on agentmods" height="20"></a>
Per session 96 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,530 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found 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 5.1 $0.00096 $0.01530
Opus 5 $0.00048 $0.00765
Sonnet 5 $0.00019 $0.00306
Haiku 4.5 $0.00010 $0.00153

Measured 7d ago against content hash 8315cd3b3db5, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

Qfastapi-expert 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 7d 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.

expert-library/packs/core-experts/skills/Qfastapi-expert/SKILL.md · 172 lines

How it starts

The opening of the file, as written. The whole thing — 172 lines — stays where its author put it; the contents beside it link to each section on GitHub.

FastAPI Expert

Deep expertise in async Python, Pydantic V2, and production-grade API development with FastAPI.

When to Use This Skill

  • Building REST APIs with FastAPI
  • Implementing Pydantic V2 validation schemas
  • Setting up async database operations
  • Implementing JWT authentication/authorization
  • Creating WebSocket endpoints
  • Optimizing API performance

Core Workflow

  1. Analyze requirements — Identify endpoints, data models, auth needs
  2. Design schemas — Create Pydantic V2 models for validation
  3. Implement — Write async endpoints with proper dependency injection
  4. Secure — Add authentication, authorization, rate limiting
  5. Test — Run pytest after each endpoint group; verify /docs before proceeding

Code Patterns

Basic: Route with Pydantic Model + Docstring

@router.get("/users/{user_id}", response_model=UserResponse)
async def get_user(user_id: int, db: DbDep) -> UserResponse:
    """Retrieve a user by ID. Returns 404 if not found."""
    user = await crud.get_user(db, user_id)
    if not user:
        raise HTTPException(status_code=404, detail="User not found")
    return user

Error Handling: Custom Exception + HTTPException

class DuplicateEmailError(Exception):
    pass

@router.exception_handler(DuplicateEmailError)
async def duplicate_email_handler(req, exc):
    return JSONResponse(status_code=409, content={"detail": "Email already exists"})

Advanced: Dependency Injection + Async SQLAlchemy

async def get_current_user(token: Annotated[str, Depends(oauth2_scheme)], db: DbDep) -> User:
    """Verify JWT and return authenticated user."""
    payload = jwt.decode(token, SECRET_KEY, algorithms=[ALGORITHM])
    user = await crud.get_user(db, int(payload["sub"]))
    if not user:
        raise HTTPException(status_code=401, detail="Invalid token")
    return user

Comment Template

Use Google-style Python docstrings:

def process_payment(amount: float, user_id: int) -> dict[str, str]:
    """Process a payment transaction.
    
    Args:
        amount: Payment amount in USD.
        user_id: ID of the user making payment.
        
    Returns:
        Transaction confirmation with status and ID.
        
    Raises:
        ValueError: If amount is negative.
        HTTPException: If user not found (status 404).
    """

Read the full file on GitHub · 172 lines

Files

What ships with it

6 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. 7d ago First seen · 172 lines · 96 tokens per session scan A 8315cd3b3db5

Subscribe to this mod's changes

Qfastapi-expert is a skill published in the GitHub repository inho-team/qe-mcp (0 stars, last pushed 1mo ago), licensed MIT. It adds 96 tokens to every session and 1,530 once invoked, about $0.0005 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 2026-09-01.