Convert text, code, or documents into 384-dimensional vectors, numerical representations used for meaning-based search, similarity comparison, and clustering. Save vectors to files, check their dimensions and magnitude, and optionally record metadata in AgentDB.
Generate embeddings via npx ruvector@0.3.3 embed text (ONNX all-MiniLM-L6-v2, 384-dim), normalize, and store in HNSW index
Review the instructions the agent will receive before you install.
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 '0.3.3' || npm install ruvector@0.3.3
embed text reports ONNX WASM files not bundled, an older ruvector (0.2.x) is being resolved — rerun with the pinned npx -y ruvector@0.3.3.text subcommand, with text as a positional arg):
npx -y ruvector@0.3.3 embed text "your text here"npx -y ruvector@0.3.3 embed text "your text here" -o vec.json--batch/--glob flags.npx -y ruvector@0.3.3 embed text "..." --adaptive --domain codemcp__plugin_ruflo-core_ruflo__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@0.3.3 mcp start
Then call MCP tools directly: hooks_rag_context (semantic context), brain_search (collective brain), hooks_ast_analyze, hooks_route.
embed --batch --glob flags do not exist in ruvector@0.3.3. embed text accepts one text, --stdin, or --input-file <path> (keeps sensitive text off argv); call it per file.npx -y ruvector@0.3.3 doctor to diagnose.No reviews yet. Tell others what you thought.
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Compare models available through the HuggingFace router with chat-ui configuration, research missing models, and write short English descriptions of their architecture and capabilities. Add entries to both production and development configuration files and commit the changes.
Create an Architecture Decision Record, a document for recording important design choices, with the next sequential number. It generates a Markdown template, registers the record in AgentDB, and links related decisions found through search.
Build a searchable index and relationship graph from ADRs, records of architectural decisions. Import two supported document formats, refresh existing metadata without duplicate relationships, and review summaries of statuses, missing references, and inconsistencies.
Rebuild the index of ADRs, records of architectural decisions, after files or relationship lines are deleted. It clears stored records and relationships, indexes the files still present, and checks that the final record count matches.
Review code changes against accepted Architecture Decision Records, documents that capture agreed design choices. Report violations, outdated references, compliant changes, and areas without ADR coverage, then suggest code fixes or a new decision record where appropriate.
Check saved architecture decision records (ADRs) for broken references, circular replacement relationships, and inconsistent statuses. Use the results to verify imported records or stop a CI check when selected integrity rules fail.