Skill UI

This skill generates high-dimensional vector embeddings (384-dim) from text, code, or documents using the ruvector package. It utilizes the ONNX all-MiniLM-L6-v2 model, making it ideal for implementing advanced semantic search, similarity comparison, and data clustering. The resulting vectors are normalized and can be efficiently stored and indexed using HNSW, significantly enhancing retrieval accuracy in RAG and knowledge systems.

A comprehensive vector search plugin offering two distinct paths: large-scale corpus search using HNSW (supporting RaBitQ quantization for memory efficiency) and a fast WASM-backed router for high-priority hot patterns. Ideal for implementing Retrieval-Augmented Generation (RAG), semantic search across massive datasets, or routing queries to specific, limited patterns.

This skill automates the comprehensive setup and bootstrapping of the ruvector framework. It ensures all necessary optional add-ons—including ONNX, Brain, and LLM components—are installed. Furthermore, it registers the required MCP server and runs a full battery of diagnostics to verify the entire toolchain is functional and ready for immediate use.

Manage the lifecycle of WebAssembly (WASM) agents, providing a secure and isolated environment for running untrusted code and conducting experiments. This tool allows users to create agents, interact via prompts, equip them with specific tools, manage sandboxed files, and export the configuration, all while guaranteeing full isolation from the host system.