python-fastapi-ops
FastAPI web framework patterns. Triggers on: fastapi, api endpoint, dependency injection, pydantic model, openapi, swagger, starlette, async api, rest api, uvicorn.
What this skill does
# FastAPI Patterns
Modern async API development with FastAPI.
## Basic Application
```python
from fastapi import FastAPI
from contextlib import asynccontextmanager
@asynccontextmanager
async def lifespan(app: FastAPI):
"""Application lifespan - startup and shutdown."""
# Startup
app.state.db = await create_db_pool()
yield
# Shutdown
await app.state.db.close()
app = FastAPI(
title="My API",
version="1.0.0",
lifespan=lifespan,
)
@app.get("/")
async def root():
return {"message": "Hello World"}
```
## Request/Response Models
```python
from pydantic import BaseModel, Field, EmailStr
from datetime import datetime
class UserCreate(BaseModel):
"""Request model with validation."""
name: str = Field(..., min_length=1, max_length=100)
email: EmailStr
age: int = Field(..., ge=0, le=150)
class UserResponse(BaseModel):
"""Response model."""
id: int
name: str
email: EmailStr
created_at: datetime
model_config = {"from_attributes": True} # Enable ORM mode
@app.post("/users", response_model=UserResponse, status_code=201)
async def create_user(user: UserCreate):
db_user = await create_user_in_db(user)
return db_user
```
## Path and Query Parameters
```python
from fastapi import Query, Path
from typing import Annotated
@app.get("/users/{user_id}")
async def get_user(
user_id: Annotated[int, Path(..., ge=1, description="User ID")],
):
return await fetch_user(user_id)
@app.get("/users")
async def list_users(
skip: Annotated[int, Query(ge=0)] = 0,
limit: Annotated[int, Query(ge=1, le=100)] = 10,
search: str | None = None,
):
return await fetch_users(skip=skip, limit=limit, search=search)
```
## Dependency Injection
```python
from fastapi import Depends
from typing import Annotated
async def get_db():
"""Database session dependency."""
async with async_session() as session:
yield session
async def get_current_user(
token: Annotated[str, Depends(oauth2_scheme)],
db: Annotated[AsyncSession, Depends(get_db)],
) -> User:
"""Authenticate and return current user."""
user = await authenticate_token(db, token)
if not user:
raise HTTPException(status_code=401, detail="Invalid token")
return user
# Annotated types for reuse
DB = Annotated[AsyncSession, Depends(get_db)]
CurrentUser = Annotated[User, Depends(get_current_user)]
@app.get("/me")
async def get_me(user: CurrentUser):
return user
```
## Exception Handling
```python
from fastapi import HTTPException
from fastapi.responses import JSONResponse
# Built-in HTTP exceptions
@app.get("/items/{item_id}")
async def get_item(item_id: int):
item = await fetch_item(item_id)
if not item:
raise HTTPException(status_code=404, detail="Item not found")
return item
# Custom exception handler
class ItemNotFoundError(Exception):
def __init__(self, item_id: int):
self.item_id = item_id
@app.exception_handler(ItemNotFoundError)
async def item_not_found_handler(request, exc: ItemNotFoundError):
return JSONResponse(
status_code=404,
content={"detail": f"Item {exc.item_id} not found"},
)
```
## Router Organization
```python
from fastapi import APIRouter
# users.py
router = APIRouter(prefix="/users", tags=["users"])
@router.get("/")
async def list_users():
return []
@router.get("/{user_id}")
async def get_user(user_id: int):
return {"id": user_id}
# main.py
from app.routers import users, items
app.include_router(users.router)
app.include_router(items.router, prefix="/api/v1")
```
## Quick Reference
| Feature | Usage |
|---------|-------|
| Path param | `@app.get("/items/{id}")` |
| Query param | `def f(q: str = None)` |
| Body | `def f(item: ItemCreate)` |
| Dependency | `Depends(get_db)` |
| Auth | `Depends(get_current_user)` |
| Response model | `response_model=ItemResponse` |
| Status code | `status_code=201` |
## Additional Resources
- `./references/dependency-injection.md` - Advanced DI patterns, scopes, caching
- `./references/middleware-patterns.md` - Middleware chains, CORS, error handling
- `./references/validation-serialization.md` - Pydantic v2 patterns, custom validators
- `./references/background-tasks.md` - Background tasks, async workers, scheduling
## Scripts
- `./scripts/scaffold-api.sh` - Generate API endpoint boilerplate
## Assets
- `./assets/fastapi-template.py` - Production-ready FastAPI app skeleton
---
## See Also
**Prerequisites:**
- `python-typing-ops` - Pydantic models and type hints
- `python-async-ops` - Async endpoint patterns
**Related Skills:**
- `python-database-ops` - SQLAlchemy integration
- `python-observability-ops` - Logging, metrics, tracing middleware
- `python-pytest-ops` - API testing with TestClient
Related in Backend & APIs
jfrog
IncludedInteract with the JFrog Platform via the JFrog CLI and REST/GraphQL APIs. Use this skill when the user wants to manage Artifactory repositories, upload or download artifacts, manage builds, configure permissions, manage users and groups, work with access tokens, configure JFrog CLI servers, search artifacts, manage properties, set up replication, manage JFrog Projects, run security audits or scans, look up CVE details, query exposures scan results from JFrog Advanced Security, manage release bundles and lifecycle operations, aggregate or export platform data, or perform any JFrog Platform administration task. Also use when the user mentions jf, jfrog, artifactory, xray, distribution, evidence, apptrust, onemodel, graphql, workers, mission control, curation, advanced security, exposures, or any JFrog product name.
cupynumeric-migration-readiness
IncludedPre-migration readiness assessor for porting NumPy to cuPyNumeric. Use BEFORE substantial porting work begins when the user asks whether code will scale on GPU, whether they should migrate to cuPyNumeric, which NumPy patterns transfer cleanly, what must be refactored before porting, or mentions pre-port assessment, scaling analysis, or refactor planning. Inspect the user's source code, look up NumPy usage, cross-reference the cuPyNumeric API support manifest, and distinguish distributed-scaling-friendly patterns from blockers such as unsupported APIs, scalar synchronization, host round-trips, Python/object-heavy control flow, shape/data-dependent branching, and in-place mutation hazards. Produce a verdict of READY, LIGHT REFACTOR, SIGNIFICANT REFACTOR, or NOT RECOMMENDED, with concrete refactor pointers.
alibabacloud-data-agent-skill
IncludedInvoke Alibaba Cloud Apsara Data Agent for Analytics via CLI to perform natural language-driven data analysis on enterprise databases. Data Agent for Analytics is an intelligent data analysis agent developed by Alibaba Cloud Database team for enterprise users. It automatically completes requirement analysis, data understanding, analysis insights, and report generation based on natural language descriptions. This tool supports: discovering data resources (instances/databases/tables) managed in DMS, initiating query or deep analysis sessions, real-time progress tracking, and retrieving analysis conclusions and generated reports. Use this Skill when users need to query databases, analyze data trends, generate data reports, ask questions in natural language, or mention "Data Agent", "data analysis", "database query", "SQL analysis", "data insights".
token-optimizer
IncludedReduce OpenClaw token usage and API costs through smart model routing, heartbeat optimization, budget tracking, and native 2026.2.15 features (session pruning, bootstrap size limits, cache TTL alignment). Use when token costs are high, API rate limits are being hit, or hosting multiple agents at scale. The 4 executable scripts (context_optimizer, model_router, heartbeat_optimizer, token_tracker) are local-only — no network requests, no subprocess calls, no system modifications. Reference files (PROVIDERS.md, config-patches.json) document optional multi-provider strategies that require external API keys and network access if you choose to use them. See SECURITY.md for full breakdown.
resend-cli
IncludedUse this skill when the task is specifically about operating Resend from an AI agent, terminal session, or CI job via the official resend CLI: installing/authenticating the CLI, sending/listing/updating/cancelling emails, batch sends, domains and DNS, webhooks and local listeners, inbound receiving, contacts, topics, segments, broadcasts, templates, API keys, profiles, or debugging Resend CLI/API failures. Trigger on mentions of Resend CLI, `resend`, `resend doctor`, `resend emails send`, `resend domains`, `resend webhooks listen`, `resend emails receiving`, or agent-friendly terminal automation.
alibabacloud-odps-maxframe-coding
IncludedUse this skill for MaxFrame SDK development and documentation navigation on Alibaba Cloud MaxCompute (ODPS). Helps answer MaxFrame API, concept, official example, and supported pandas API questions; create data processing programs; read/write MaxCompute tables; debug jobs (remote or local); and build custom DPE runtime images. Trigger when users mention MaxFrame, MaxCompute with MaxFrame, ODPS table processing, DPE runtime, MaxFrame docs/examples, DataFrame/Tensor operations, or GPU runtime setup. Works for both English and Chinese queries about Alibaba Cloud data processing with MaxFrame.