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Found 9 Skills
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.
Expert in vector databases, embedding strategies, and semantic search implementation. Masters Pinecone, Weaviate, Qdrant, Milvus, and pgvector for RAG applications, recommendation systems, and similar
In-depth analysis of AI papers, generating professional reading notes ready for direct publication
Implement content-based recommendation by matching item features to user preference profiles. Use this skill when the user needs to recommend items based on attributes, solve the cold start problem for new items, or build recommendations without collaborative data — even if they say 'recommend similar products', 'items like this', or 'feature-based matching'.
Reference for X algorithm engagement types and signals. Use when analyzing engagement metrics, action predictions, or understanding what signals the algorithm tracks.
Implement matrix factorization to decompose user-item interaction matrices into latent factor representations. Use this skill when the user needs scalable collaborative filtering, latent feature discovery, or dimensionality reduction for recommendation — even if they say 'SVD recommendations', 'latent factors', or 'factorize the rating matrix'.
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.
Coaches end-to-end ML system design interviews covering inference pipelines, recommendation systems, RAG, feature stores, and monitoring. Use for L6+ design rounds, ML architecture whiteboarding, system design practice, serving tradeoff analysis. Activate on "ML system design", "ML interview", "recommendation system design", "RAG architecture", "feature store design", "model serving". NOT for coding interviews, behavioral questions, ML theory quizzes, or paper implementations.
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.