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Found 9 Skills
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.
In-depth analysis of AI papers, generating professional reading notes ready for direct publication
Intelligent skill retrieval and recommendation system for Claude Code. Uses semantic search, intent analysis, and confidence scoring to recommend the most appropriate skills. Features: (1) Smart skill matching via bilingual embeddings (Chinese/English), (2) Prudent decision-making with three confidence tiers, (3) Historical learning from usage patterns, (4) Automatic health checking and lifecycle management, (5) Intelligent cache cleanup. Use when: User asks to find/recommend a skill, multiple skills might match a request, or skill selection requires intelligent analysis.
Build recommendation systems with collaborative filtering, matrix factorization, hybrid approaches. Use for product recommendations, personalization, or encountering cold start, sparsity, quality evaluation issues.
Expert in vector databases, embedding strategies, and semantic search implementation. Masters Pinecone, Weaviate, Qdrant, Milvus, and pgvector for RAG applications, recommendation systems, and similar
Reference for X algorithm engagement types and signals. Use when analyzing engagement metrics, action predictions, or understanding what signals the algorithm tracks.
Intelligent recommendation system analysis tool that provides implementations of multiple recommendation algorithms, evaluation frameworks, and visual analysis. It requires user behavior data, product information, or rating data for use, supports recommendation algorithms such as collaborative filtering and matrix factorization, and generates personalized recommendation results and evaluation reports.
Search Amazon.com, extract product data, and present ranked recommendations. Use when user asks to shop on Amazon, find products, compare items, or research purchases. Prioritizes review count over rating.
TasteRay API integration for personalized recommendations across verticals (movies, restaurants, products, travel, jobs). Use when you need to: (1) recommend movies, restaurants, products, travel, or jobs, (2) answer "what would I like" questions, (3) provide personalized recommendations based on preferences, (4) rank or score items for a user, (5) explain why something matches a user's taste, (6) build recommendation context from conversation, (7) integrate psychological profiles with recommendation systems.