/vector-embed
Generate embeddings via npx ruvector@0.3.3 embed text (ONNX all-MiniLM-L6-v2, 384-dim), normalize, and store in HNSW index
Claude Code
$ npx -y skills add ruvnet/ruflo --skill vector-embed --agent claude-code
Codex
$ npx skills add ruvnet/ruflo --skill vector-embed -a codex -y
Cursor
$ npx skills add ruvnet/ruflo --skill vector-embed -a cursor -y
Gemini CLI
$ npx skills add ruvnet/ruflo --skill vector-embed -a gemini-cli -y
GitHub Copilot
$ npx skills add ruvnet/ruflo --skill vector-embed -a github-copilot -y
OpenCode
$ npx skills add ruvnet/ruflo --skill vector-embed -a opencode -y
Windsurf
$ npx skills add ruvnet/ruflo --skill vector-embed -a windsurf -y
SKILL.md
vector-embed.SKILL.mdname: vector-embed
description: Generate embeddings via npx ruvector@0.3.13 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__plugin_ruflo-core_ruflo__memory_store mcp__plugin_ruflo-core_ruflo__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@0.3.13 is available**:
npm ls ruvector 2>/dev/null | grep '0.3.3' || npm install ruvector@0.3.13
ruvector 0.3.3 bundles the ONNX runtime; no add-on is needed. If `embed text` reports `ONNX WASM files not bundled`, an older ruvector (0.2.x) is being resolved — rerun with the pinned `npx -y ruvector@0.3.13`. 2. **Embed the input** (use the `text` subcommand, with text as a positional arg):
- Single string: `npx -y ruvector@0.3.13 embed text "your text here"`
- With output file: `npx -y ruvector@0.3.13 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@0.3.13 has no built-in `--batch`/`--glob` flags.
3. **Adaptive (LoRA) variant**: `npx -y ruvector@0.3.13 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@0.3.13 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` flags do **not** exist in ruvector@0.3.13. `embed text` accepts one text, `--stdin`, or `--input-file <path>` (keeps sensitive text off argv); call it per file.
- If embedding fails, run `npx -y ruvector@0.3.13 doctor` to diagnose.
Read more
name: vector-embed description: Generate embeddings via npx ruvector@0.3.13 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__plugin_ruflo-core_ruflo__memory_store mcp__plugin_ruflo-core_ruflo__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@0.3.13 is available**:
npm ls ruvector 2>/dev/null | grep '0.3.3' || npm install ruvector@0.3.13
ruvector 0.3.3 bundles the ONNX runtime; no add-on is needed. If `embed text` reports `ONNX WASM files not bundled`, an older ruvector (0.2.x) is being resolved — rerun with the pinned `npx -y ruvector@0.3.13`. 2. **Embed the input** (use the `text` subcommand, with text as a positional arg):
- Single string: `npx -y ruvector@0.3.13 embed text "your text here"`
- With output file: `npx -y ruvector@0.3.13 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@0.3.13 has no built-in `--batch`/`--glob` flags.
3. **Adaptive (LoRA) variant**: `npx -y ruvector@0.3.13 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@0.3.13 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` flags do **not** exist in ruvector@0.3.13. `embed text` accepts one text, `--stdin`, or `--input-file <path>` (keeps sensitive text off argv); call it per file.
- If embedding fails, run `npx -y ruvector@0.3.13 doctor` to diagnose.
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Questions about vector-embed.
- Which coding agents can use vector-embed?
- vector-embed installs in Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode and Windsurf with the skills CLI, using each agent's flag shown on this page. Flowy's auto-invocation, which fires a skill when your prompt matches, runs in Claude Code only.

