with one click
vector-embed
Generate embeddings via npx ruvector@0.2.25 embed text (ONNX all-MiniLM-L6-v2, 384-dim), normalize, and store in HNSW index
Generate embeddings via npx ruvector@0.2.25 embed text (ONNX all-MiniLM-L6-v2, 384-dim), normalize, and store in HNSW index
Author a workflow — either an MCP workflow template (persisted, lifecycle) or a native .claude/workflows/*.js orchestration script (agent/parallel/pipeline fan-out)
Run a workflow — drive an MCP workflow lifecycle (execute/pause/resume/cancel) or invoke + resume a native .claude/workflows/*.js orchestration via the Workflow tool
Side-by-side comparison of ruflo vs HAL vs other GAIA harnesses — capability gaps, design decisions, and improvement roadmap
Diagnose why a GAIA question failed — extract trace, classify failure mode, and propose a fix
Walk through a complete GAIA benchmark→submit flow — from key resolution through HAL-compatible package generation
Scaffold a new Claude Code plugin with proper directory structure, plugin.json, skills, commands, and agents
| name | vector-embed |
| description | Generate embeddings via npx ruvector@0.2.25 embed text (ONNX all-MiniLM-L6-v2, 384-dim), normalize, and store in HNSW index |
| argument-hint | <text-or-file> |
| allowed-tools | Bash Read mcp__claude-flow__memory_store mcp__claude-flow__memory_search |
Generate and store vector embeddings using the ruvector npm package.
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).
npm ls ruvector 2>/dev/null | grep '0.2.25' || npm install ruvector@0.2.25
If embed text later reports ONNX WASM files not bundled, also run:
npm install ruvector-onnx-embeddings-wasm
text subcommand, with text as a positional arg):
npx -y ruvector@0.2.25 embed text "your text here"npx -y ruvector@0.2.25 embed text "your text here" -o vec.json--batch/--glob flags.npx -y ruvector@0.2.25 embed text "..." --adaptive --domain codemcp__claude-flow__memory_store({ key: "embed-SOURCE", value: "VECTOR_METADATA", namespace: "vector-patterns" })Register the MCP server once with the pinned version:
claude mcp add ruvector -- npx -y ruvector@0.2.25 mcp start
Then call MCP tools directly: hooks_rag_context (semantic context), brain_search (collective brain), hooks_ast_analyze, hooks_route.
embed --batch --glob and embed --file flags do not exist in ruvector@0.2.25; only embed text <text> is supported. Read files yourself and call embed text per file.ruvector-onnx-embeddings-wasm or run npx -y ruvector@0.2.25 doctor to diagnose.