Embeddings
ruvnet/rufloVector embeddings turn text into numbers that capture meaning. This skill uses HNSW indexing to make searches up to 12,500 times faster. It supports hyperbolic space for hierarchical data and sql.js for persistent storage.
Use this skill for semantic search, pattern matching, and knowledge retrieval. It works with agentic-flow integration to run 75 times faster. You can normalize and quantize embeddings to save memory and speed up processing.
Commands let you initialize, embed text, batch process, and search. It works well with memory systems for storing and finding related patterns.
Add Embeddings skill to your workflow
Global
mkdir -p ~/.claude/skills/embeddingsProject
mkdir -p .claude/skills/embeddingsSource Repository
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