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Found 6 Skills
Vector embeddings with HNSW indexing, sql.js persistence, and hyperbolic support. 75x faster with agentic-flow integration. Use when: semantic search, pattern matching, similarity queries, knowledge retrieval. Skip when: exact text matching, simple lookups, no semantic understanding needed.
Agent skill for v3-memory-specialist - invoke with $agent-v3-memory-specialist
HNSW vector search with RuVector embeddings for 150x-12500x faster semantic retrieval
Apply quantization to reduce memory by 4-32x. Enable HNSW indexing for 150x faster search. Configure caching strategies and implement batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors. Deploy these optimizations to achieve 12,500x performance gains.
Generate embeddings via npx ruvector (ONNX all-MiniLM-L6-v2, 384-dim), normalize, and store in HNSW index
Ingest and normalize market data into OHLCV vectors with HNSW indexing