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Found 13 Skills
Latest AI models reference - Claude, OpenAI, Gemini, Eleven Labs, Replicate
Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embedding quality for specific domains.
Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications. Use when "building RAG, vector search, embeddings, semantic search, document retrieval, context retrieval, knowledge base, LLM with documents, chunking strategy, pinecone, weaviate, chromadb, pgvector, rag, embeddings, vector-database, retrieval, semantic-search, llm, ai, langchain, llamaindex" mentioned.
Upstash Vector DB setup, semantic search, namespaces, and embedding models (MixBread preferred). Use when building vector search features on Vercel.
Provides expertise on Chroma vector database integration for semantic search applications. Use when the user asks about vector search, embeddings, Chroma, semantic search, RAG systems, nearest neighbor search, or adding search functionality to their application.
Retrieval-Augmented Generation (RAG) system design patterns, chunking strategies, embedding models, retrieval techniques, and context assembly. Use when designing RAG pipelines, improving retrieval quality, or building knowledge-grounded LLM applications.
CLIP, SigLIP 2, Voyage multimodal-3 patterns for image+text retrieval, cross-modal search, and multimodal document chunking. Use when building RAG with images, implementing visual search, or hybrid retrieval.
Guides embedding model migration in Qdrant without downtime. Use when someone asks 'how to switch embedding models', 'how to migrate vectors', 'how to update to a new model', 'zero-downtime model change', 'how to re-embed my data', or 'can I use two models at once'. Also use when upgrading model dimensions, switching providers, or A/B testing models.
Diagnoses and improves Qdrant search relevance. Use when someone reports 'search results are bad', 'wrong results', 'low precision', 'low recall', 'irrelevant matches', 'missing expected results', or asks 'how to improve search quality?', 'which embedding model?', 'should I use hybrid search?', 'should I use reranking?'. Also use when search quality degrades after quantization, model change, or data growth.
Build RAG systems - embeddings, vector stores, chunking, and retrieval optimization
Guide for using the `paper` CLI tool — a local academic paper management system with AI-powered vector search. Use this skill whenever the user wants to manage academic papers, create knowledge bases, add PDFs to a knowledge base, search papers semantically, configure embedding models, or manage literature metadata and notes. Also trigger when the user mentions "paper" CLI, knowledge bases for research, literature management, or wants to query their paper collection. Even if the user just says something like "add this PDF" or "search my papers" in a project that uses paper-manager, this skill should activate.
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.