Agent skill · research science · aiskillstore
agentdb-advanced-features
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
Why this skill is useful
Adds advanced QUIC synchronization scripts and multi-database management patterns that are not commonly found in public documentation.
What it needs
Requires node installed locally. About 6k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
AgentDB Advanced Features What This Skill Does Covers advanced AgentDB capabilities for distributed systems, multi-database coordination, custom distance metrics, hybrid search (vector + metadata), QUIC synchronization, and production deployment patterns. Enables building sophisticated AI systems with sub-millisecond cross-node communication and advanced search capabilities. Performance: <1ms QUIC sync, hybrid search with filters, custom distance metrics. Prerequisites Node.js 18+ AgentDB v1.0.7+ (via agentic-flow) Understanding of distributed systems (for QUIC sync) Vector search fundamentals --- QUIC Synchronization What is QUIC Sync? QUIC (Quick UDP Internet Connections) enables sub-millisecond latency synchronization between AgentDB instances across network boundaries with automatic retry, multiplexing, and encryption. Benefits: <1ms latency between nodes Multiplexed streams (multiple operations simultaneously) Built-in encryption (TLS 1.3) Automatic retry and recovery Event-based broadcasting Enable QUIC Sync QUIC Configuration Multi-Node Deployment --- Distance Metrics Cosine Similarity (Default) Best for normalized vectors, semantic similarity: Use Cases: Text embeddings (BERT, GPT, etc.) Semantic search Document similarity Most general-purpose applications Formula: cos(θ) = (A · B) / ( A × B ) Range: [-1, 1] (1 = identical, -1 = opposite) Euclidean Distance (L2) Best for spatial data, geometric similarity: Use Cases: Image embeddings Spatial data Computer vision When vector magnitude matters Formula: d = √(Σ(ai - bi)²) Range: [0, ∞] (0 = identical, ∞ = very different) Dot Product Best for pre-normalized vectors, fast computation: Use Cases: Pre-normalized embeddings Fast similarity computation When vectors are already unit-length Formula: dot = Σ(ai × bi) Range: [-∞, ∞] (higher = more similar) Custom Distance Metrics --- Hybrid Search (Vector + Metadata) Basic Hybrid Search Combine vector similarity with metadata filtering: Advanced Filtering Weighted Hybrid …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/agentdb-advanced-features