recommendation-system
Deploy production recommendation systems with feature stores, caching, A/B testing. Use for personalization APIs, low latency serving, or encountering cache invalidation, experiment tracking, quality monitoring issues.
npx skills add majiayu000/claude-skill-registry --skill recommendation-system-secondsky-claude-skills-3 --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.
# 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 ```bash # 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) class RecommendationResponse(BaseMod
- When to Use This Skill
- Quick Start: Recommendation API in 5 Steps
- System Architecture
- Core Components
- 1. Feature Store
- 2. Model Serving
- 3. Caching Layer
- Key Metrics
- Known Issues Prevention
- 1. Cold Start for New Users
- 2. Cache Invalidation on User Actions
- 3. Thundering Herd on Cache Expiry
- 4. Poor Diversity = Filter Bubble
- 5. No Monitoring = Silent Degradation
pip install fastapi==0.109.0 redis==5.0.0 prometheus-client==0.19.0 docker run -d -p 6379:6379 redis:alpine cat > app.py << 'EOF' uvicorn app:app --host 0.0.0.0 --port 8000 curl -X POST "http://localhost:8000/recommendations?user_id=user_123&n=10"
What does the recommendation-system skill do?
Deploy production recommendation systems with feature stores, caching, A/B testing. Use for personalization APIs, low latency serving, or encountering cache invalidation, experiment tracking, quality monitoring issues.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill recommendation-system-secondsky-claude-skills-3 --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.