embeddings
Vector embeddings with HNSW indexing, sql.js persistence, and hyperbolic support. 75x faster with agentic-flow integration. Use when: semantic search, pattern matching, similarity queries, knowledge retrieval. Skip when: exact text matching, simple lookups, no semantic understanding needed.
Profile →npx skills add ruvnet/ruflo --skill embeddings --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Embeddings Skill ## Purpose Vector embeddings for semantic search and pattern matching with HNSW indexing. ## Features | Feature | Description | |---------|-------------| | **sql.js** | Cross-platform SQLite persistent cache (WASM) | | **HNSW** | 150x-12,500x faster search | | **Hyperbolic** | Poincare ball model for hierarchical data | | **Normalization** | L2, L1, min-max, z-score | | **Chunking** | Configurable overlap and size | | **75x faster** | With agentic-flow ONNX integration | ## Commands ### Initialize Embeddings ```bash npx claude-flow embeddings init --backend sqlite ``` ### Embed Text ```bash npx claude-flow embeddings embed --text "authentication patterns" ``` ### Batch Embed ```bash npx claude-flow embeddings batch --file documents.json ``` ### Semantic Search ```bash npx claude-flow embeddings search --query "security best practices" --top-k 5 ``` ## Memory Integration ```bash # Store with embeddings npx claude-flow memory store --key "pattern-1" --value "description" --embed # Search with embeddings npx claude-flow memory search --query "related patterns" --semantic ``` ## Quantization | Type | Memory Reduction | Speed | |------|-----------------|-------| | Int
- Purpose
- Features
- Commands
- Initialize Embeddings
- Embed Text
- Batch Embed
- Semantic Search
- Memory Integration
- Quantization
- Best Practices
npx claude-flow embeddings init --backend sqlite npx claude-flow embeddings embed --text "authentication patterns" npx claude-flow embeddings batch --file documents.json npx claude-flow embeddings search --query "security best practices" --top-k 5 Store with embeddings npx claude-flow memory store --key "pattern-1" --value "description" --embed Search with embeddings npx claude-flow memory search --query "related patterns" --semantic
What does the embeddings skill do?
Vector embeddings with HNSW indexing, sql.js persistence, and hyperbolic support. 75x faster with agentic-flow integration. Use when: semantic search, pattern matching, similarity queries, knowledge retrieval. Skip when: exact text matching, simple lookups, no semantic understanding needed.
How do I install it?
Run `npx skills add ruvnet/ruflo --skill embeddings --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From ruvnet/ruflo, a repository with 67,015 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.