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---
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
---
# 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**:
```bash
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:
```bash
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__claude-flow__memory_store({ key: "embed-SOURCE", value: "VECTOR_METADATA", namespace: "vector-patterns" })`
## MCP alternative
Register the MCP server once with the pinned version:
```bash
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.
