Recommendation System

This is a full production recommendation system architecture with Redis feature stores, multi-tier caching, and A/B testing built in. You get FastAPI serving, cache invalidation strategies, and monitoring for CTR, conversion rate, and diversity metrics. It handles the annoying stuff like cold start fallback to popular items, thundering herd prevention with TTL jitter, and cache invalidation on user actions. The code examples are practical, showing exactly how to set up tiered caching and serve multiple models simultaneously. If you're building personalization at scale and need sub-200ms latency with proper experiment tracking, this gives you the patterns without reinventing infrastructure.

skills.sh installs: 412
GitHub stars: 248
last updated: Aug 21, 2026

View on GitHub

Install

$ npx -y skills add secondsky/claude-skills --skill recommendation-system --agent claude-code

Installs into .claude/skills of the current project.

Files

Recommendation System

Production-ready architecture for scalable recommendation systems with feature stores, multi-tier caching, A/B testing, and comprehensive monitoring.

When to Use This Skill

Load this skill when:

Building Recommendation APIs: Serving personalized recommendations at scale
Implementing Caching: Multi-tier caching for sub-millisecond latency
Running A/B Tests: Experimenting with recommendation algorithms
Monitoring Quality: Tracking CTR, conversion, diversity, coverage
Optimizing Performance: Reducing latency, increasing throughput
Feature Engineering: Managing user/item features with feature stores

Quick Start: Recommendation API in 5 Steps

# 1. Install dependencies
pip install "fastapi>=0.109.0" "redis>=5.0.0" "prometheus-client>=0.19.0"

# 2. Start Redis (for caching and feature store)
docker run -d -p 6379:6379 redis:alpine

# 3. Create recommendation service: app.py
cat > app.py << 'EOF'
from fastapi import FastAPI
from pydantic import BaseModel
from typing import List
import redis
import json

app = FastAPI()
cache = redis.Redis(host='localhost', port=6379, decode_responses=True)

SKILL.md on GitHub