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/jeffallan/claude-skills/fastapi-expertnpx skills add Jeffallan/claude-skills --skill fastapi-expertgit clone --depth 1 https://github.com/Jeffallan/claude-skillsWhat 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 | $0.00095 | $0.01576 |
| Opus 5 | $0.00048 | $0.00788 |
| Sonnet 5 | $0.00019 | $0.00315 |
| Haiku 4.5 | $0.00010 | $0.00158 |
Grade A, and why
fastapi-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 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.
How it starts
The opening of the file, as written. The whole thing — 188 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
- Analyze requirements — Identify endpoints, data models, auth needs
- Design schemas — Create Pydantic V2 models for validation
- Implement — Write async endpoints with proper dependency injection
- Secure — Add authentication, authorization, rate limiting
- Test — Write async tests with pytest and httpx; run
pytestafter each endpoint group and verify OpenAPI docs at/docs
Checkpoint after each step: confirm schemas validate correctly, endpoints return expected HTTP status codes, and
/docsreflects the intended API surface before proceeding.
Minimal Complete Example
Schema + endpoint + dependency injection in one cohesive unit:
# schemas.py
from pydantic import BaseModel, EmailStr, field_validator, model_config
class UserCreate(BaseModel):
model_config = model_config(str_strip_whitespace=True)
email: EmailStr
password: str
name: str | None = None
@field_validator("password")
@classmethod
def password_strength(cls, v: str) -> str:
if len(v) < 8:
raise ValueError("Password must be at least 8 characters")
return v
class UserResponse(BaseModel):
model_config = model_config(from_attributes=True)
id: int
email: EmailStr
name: str | None = None
# routers/users.py
from fastapi import APIRouter, Depends, HTTPException, status
from sqlalchemy.ext.asyncio import AsyncSession
from typing import Annotated
from app.database import get_db
from app.schemas import UserCreate, UserResponse
from app import crud
router = APIRouter(prefix="/users", tags=["users"])
DbDep = Annotated[AsyncSession, Depends(get_db)]
@router.post("/", response_model=UserResponse, status_code=status.HTTP_201_CREATED)
async def create_user(payload: UserCreate, db: DbDep) -> UserResponse:
existing = await crud.get_user_by_email(db, payload.email)
if existing:
raise HTTPException(status_code=status.HTTP_409_CONFLICT, detail="Email already registered")
return await crud.create_user(db, payload)
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.
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 · 188 lines · 95 tokens per session scan A 73331cf09b52
fastapi-expert is a skill published in the GitHub repository Jeffallan/claude-skills (11,250 stars, last pushed 24d ago), licensed MIT. It adds 95 tokens to every session and 1,576 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 2026-08-30.
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