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Found 107 Skills
Complete RAG and search engineering skill. Covers chunking strategies, hybrid retrieval (BM25 + vector), cross-encoder reranking, query rewriting, ranking pipelines, nDCG/MRR evaluation, and production search systems. Modern patterns for retrieval-augmented generation and semantic search.
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.
Use Orchata CLI commands to manage knowledge bases from the terminal. For shell/terminal operations only.
Agentic social media assistant for social.sh - enables autonomous engagement, content discovery, network analysis, conversational queries, workflow-driven musing generation, and automated posting using semantic search and heuristic network analysis.
This skill should be used when searching Claude Code session transcripts with semantic understanding. Triggers on queries like "find sessions about X", "when did I work on Y", "search previous conversations". Supports natural language queries with synonym matching.
Bridge Claude Code auto-memory into AgentDB with ONNX embeddings, deduplicate, and enable unified cross-project search
Cloudflare Vectorize vector database for semantic search and RAG. Use for vector indexes, embeddings, similarity search, or encountering dimension mismatches, filter errors.
Google Gemini embeddings API (gemini-embedding-001) for RAG and semantic search. Use for vector search, Vectorize integration, or encountering dimension mismatches, rate limits, text truncation.
Generate embeddings via npx ruvector (ONNX all-MiniLM-L6-v2, 384-dim), normalize, and store in HNSW index
Store and query vector embeddings using Amazon S3 Vectors, a cost-effective long-term vector storage service with its own API namespace (s3vectors). Triggers on: create S3 vector bucket, vector index, store embeddings, semantic search, RAG vector storage, similarity search, vector database, migrate from other vector databases. Do NOT use for: querying tabular data (use querying-data-lake), S3 object storage, or hundreds/thousands of sustained QPS (use OpenSearch).
Sets up vector databases for semantic search including Pinecone, Chroma, pgvector, and Qdrant with embedding generation and similarity search. Use when users request "vector database", "semantic search", "embeddings storage", "Pinecone setup", or "similarity search".
Vector database implementation for AI/ML applications, semantic search, and RAG systems. Use when building chatbots, search engines, recommendation systems, or similarity-based retrieval. Covers Qdrant (primary), Pinecone, Milvus, pgvector, Chroma, embedding generation (OpenAI, Voyage, Cohere), chunking strategies, and hybrid search patterns.