MCPInstall

vector-embed

by ruvnetAI & Agents

Generate embeddings via npx ruvector@0.2.25 embed text (ONNX all-MiniLM-L6-v2, 384-dim), normalize, and store in HNSW index

Installs
731
Last 24h
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Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/ruvnet/ruflo --skill vector-embed
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads vector-embed automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

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).

Steps

  1. Ensure ruvector@0.2.25 is available: 
    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
  2. Embed the input (use the text subcommand, with text as a positional arg):
    • Single string: npx -y ruvector@0.2.25 embed text "your text here"
    • With output file: npx -y ruvector@0.2.25 embed text "your text here" -o vec.json
    • For a file: read its content via the Read tool, then pass it as the positional argument.
    • For batch: loop over files in shell — ruvector@0.2.25 has no built-in --batch/--glob flags.
  3. Adaptive (LoRA) variant: npx -y ruvector@0.2.25 embed text "..." --adaptive --domain code
  4. Confirm — report vector dimension (384), norm, and any output path written.
  5. Store metadata in AgentDB if needed: mcp__plugin_ruflo-core_ruflo__memory_store({ key: "embed-SOURCE", value: "VECTOR_METADATA", namespace: "vector-patterns" })

MCP alternative

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.

Caveats

  • The 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.
  • ONNX runtime is not bundled by default. If embedding fails, install ruvector-onnx-embeddings-wasm or run npx -y ruvector@0.2.25 doctor to diagnose.

FAQ

What does the vector-embed skill do?

Generate embeddings via npx ruvector@0.2.25 embed text (ONNX all-MiniLM-L6-v2, 384-dim), normalize, and store in HNSW index

How do I install vector-embed?

Run npx skills add https://github.com/ruvnet/ruflo --skill vector-embed in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/vector-embed/ (personal) or .claude/skills/vector-embed/ (project).

Which AI agents can use vector-embed?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is vector-embed safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains vector-embed?

It is published by ruvnet in ruvnet/ruflo. It has 731 recorded installs.

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