By wshobson
Build semantic search, RAG retrieval, and recommendation engines using vector databases with efficient nearest neighbor queries and optimized retrieval performance.
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npx claudepluginhub aiskillstore/marketplace --plugin wshobson-similarity-search-patternsQuantitative analysis, algorithmic trading strategies, financial modeling, portfolio risk management, and backtesting
Database query optimization, cloud cost optimization, and scalability improvements
Cross-platform application development coordinating web, iOS, Android, and desktop implementations
Database architecture, schema design, and SQL optimization for production systems
Pre-deployment checks, configuration validation, and deployment readiness assessment
Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.
Manage vector embeddings and similarity search
Cloudflare Vectorize vector database for semantic search and RAG. Use for vector indexes, embeddings, similarity search, or encountering dimension mismatches, filter errors.
Agent skills for Qdrant vector search: scaling, performance optimization, search quality, monitoring, deployment, edge, model migration, multitenancy, version upgrades, and SDK usage
Pinecone vector database integration. Streamline your Pinecone development with powerful tools for managing vector indexes, querying data, and rapid prototyping. Use slash commands like /quickstart to generate AGENTS.md files and initialize Python projects and /query to quickly explore indexes. Access the Pinecone MCP server for creating, describing, upserting and querying indexes with Claude. Perfect for developers building semantic search, RAG applications, recommendation systems, and other vector-based applications with Pinecone.
LLM application development with RAG, embeddings, LangChain, and prompt engineering