ml-api-endpoint
Эксперт ML API. Используй для model serving, inference endpoints, FastAPI и ML deployment.
npx skills add majiayu000/claude-skill-registry --skill ml-api-endpoint-dengineproblem-agents-monorepo --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# ML API Endpoint Expert Expert in designing and deploying machine learning API endpoints. ## Core Principles ### API Design - **Stateless Design**: Each request contains all necessary information - **Consistent Response Format**: Standardize success/error structures - **Versioning Strategy**: Plan for model updates - **Input Validation**: Rigorous validation before inference ## FastAPI Implementation ### Basic ML Endpoint ```python from fastapi import FastAPI, HTTPException from pydantic import BaseModel, validator import joblib import numpy as np app = FastAPI(title="ML Model API", version="1.0.0") model = None @app.on_event("startup") async def load_model(): global model model = joblib.load("model.pkl") class PredictionInput(BaseModel): features: list[float] @validator('features') def validate_features(cls, v): if len(v) != 10: raise ValueError('Expected 10 features') return v class PredictionResponse(BaseModel): prediction: float confidence: float | None = None model_version: str request_id: str @app.post("/predict", response_model=PredictionResponse) async def predict(input_data: PredictionInput): features = np.array([input_data.features]) prediction = model.predict(features)[
- Core Principles
- API Design
- FastAPI Implementation
- Basic ML Endpoint
- Batch Prediction
- Performance Optimization
- Model Caching
- Health Checks
- Docker Deployment
- Best Practices
What does the ml-api-endpoint skill do?
Эксперт ML API. Используй для model serving, inference endpoints, FastAPI и ML deployment.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill ml-api-endpoint-dengineproblem-agents-monorepo --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.