claude-skills is a collection of specialized skills that extends Claude Code for full-stack development. Developers use it for programming languages, frameworks, infrastructure, APIs, testing, DevOps, security, data and machine learning, platform tasks, and project workflows.
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 skills add Jeffallan/claude-skills --skill fastapi-expertgit clone --depth 1 https://github.com/Jeffallan/claude-skillsWrote 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/jeffallan/claude-skills/fastapi-expert)<a href="https://agentmods.dev/skills/jeffallan/claude-skills/fastapi-expert"><img src="https://agentmods.dev/badge/skills/jeffallan/claude-skills/fastapi-expert/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/jeffallan/claude-skills/fastapi-expert"><img src="https://agentmods.dev/badge/skills/jeffallan/claude-skills/fastapi-expert.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector pass
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 5.1 | $0.00095 | $0.01624 |
| Opus 5.5 | $0.00038 | $0.00650 |
| Sonnet 5.5 | $0.00019 | $0.00325 |
| Haiku 4.5 | $0.00010 | $0.00162 |
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.
Copies of this mod
1 near-identical copy found in the catalogue:
- fastapi-expert — 91% identical, 7 lines differ
How it starts
The opening of the file, as written. The whole thing — 191 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 Changed aec3e59f351c
- 13d ago Changed · +3 lines 73514d9672e1
- 1mo ago First seen · 188 lines · 95 tokens per session scan A 73331cf09b52
fastapi-expert is a skill published in the GitHub repository Jeffallan/claude-skills (11,907 stars, last pushed today), licensed MIT. It adds 95 tokens to every session and 1,624 once invoked, about $0.0004 per session on Opus 5.5. 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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