Vector Embed

Generates 384-dimensional embeddings using the all-MiniLM-L6-v2 model via ruvector's ONNX runtime, then stores them in an HNSW index for fast semantic search. You get 52,000+ inserts per second and sub-millisecond lookups, which is legitimately fast for local embedding work. The skill walks you through text-only embedding (no batch flags exist in 0.2.25, so you loop yourself), offers an adaptive LoRA variant for domain-specific tuning, and can optionally hook into MCP tools for RAG context and collective brain search. Main gotcha is the ONNX runtime isn't bundled by default, so first-run failures usually mean installing the separate wasm package. Solid choice if you need local embeddings without API calls.

skills.sh installs: 732
GitHub stars: 81.4K
last updated: Aug 21, 2026

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Install

$ npx -y skills add ruvnet/ruflo --skill vector-embed --agent claude-code

Installs into .claude/skills of the current project.

Files

Vector Embed

Generate and store vector embeddings using the ruvector npm package.

When to use

Use this skill to embed text, code, or documents into 384-dimensional vectors for semantic search, similarity comparison, or clustering. ruvector uses ONNX all-MiniLM-L6-v2 with HNSW indexing (52,000+ inserts/sec, ~0.045ms search).