Embeddings
Skill Verified ActiveVector 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.
To enable efficient and scalable semantic search, pattern matching, similarity queries, and knowledge retrieval using advanced vector embedding techniques.
Features
- HNSW indexing for fast search
- sql.js persistence for cross-platform SQLite
- Hyperbolic embeddings for hierarchical data
- Multiple normalization options (L2, L1, min-max, z-score)
- Configurable chunking for text processing
- ONNX integration for 75x faster agentic-flow performance
- Quantization for memory efficiency (Int8, Int4, Binary)
Use Cases
- Performing semantic search on large datasets
- Implementing pattern matching and similarity queries
- Building knowledge retrieval systems
- Integrating vector embeddings with agentic workflows
Non-Goals
- Exact text matching
- Simple key-value lookups
- Scenarios requiring no semantic understanding
Installation
npx skills add ruvnet/rufloRuns the Vercel skills CLI (skills.sh) via npx — needs Node.js locally and at least one installed skills-compatible agent (Claude Code, Cursor, Codex, …). Assumes the repo follows the agentskills.io format.
Quality Score
VerifiedTrust Signals
Similar Extensions
Rag Architect
100Use when the user asks to design RAG pipelines, optimize retrieval strategies, choose embedding models, implement vector search, or build knowledge retrieval systems.
Mongodb Search And Ai
100Guides MongoDB users through implementing and optimizing Atlas Search (full-text), Vector Search (semantic), and Hybrid Search solutions. Use this skill when users need to build search functionality for text-based queries (autocomplete, fuzzy matching, faceted search), semantic similarity (embeddings, RAG applications), or combined approaches. Also use when users need text containment, substring matching ('contains', 'includes', 'appears in'), case-insensitive or multi-field text search, or filtering across many fields with variable combinations. Provides workflows for selecting the right search type, creating indexes, constructing queries, and optimizing performance using the MongoDB MCP server.
AgentDB Vector Search
99Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.
Vector Search Workflows
99Vector search indexing and querying workflows using MCP Vector Search, including setup, reindexing, auto-index strategies, and MCP integration.
V3 Memory Unification
99Unify 6+ memory systems into AgentDB with HNSW indexing for 150x-12,500x search improvements. Implements ADR-006 (Unified Memory Service) and ADR-009 (Hybrid Memory Backend).
Memory Management
99AgentDB memory system with HNSW vector search. Provides 150x-12,500x faster pattern retrieval, persistent storage, and semantic search capabilities for learning and knowledge management. Use when: need to store successful patterns, searching for similar solutions, semantic lookup of past work, learning from previous tasks, sharing knowledge between agents, building knowledge base. Skip when: no learning needed, ephemeral one-off tasks, external data sources available, read-only exploration.