Agent skill · software engineering · proffesor-for-testing
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 executable scripts for QUIC synchronization and advanced vector search configurations that are not commonly documented.
What it needs
Requires node installed locally. About 6k tokens when loaded. Last updated 2026-08-06. 434 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 proffesor-for-testing/agentdb-advanced