Does this skill support metadata filtering?

Yes, it provides patterns for hybrid search that allow you to combine vector similarity with complex metadata filters like range queries, membership tests, and boolean logic.

When should I use MMR (Maximal Marginal Relevance)?

Use MMR when you need to retrieve search results that are both highly relevant and diverse. It is particularly useful for avoiding 'echo chambers' or redundant information in AI responses.

What is QUIC synchronization in AgentDB?

QUIC synchronization enables sub-millisecond latency for data syncing between AgentDB instances across network boundaries using encrypted UDP connections with automatic retry logic.

How do I handle database scaling with this skill?

The skill provides patterns for database sharding by domain and connection pooling, allowing you to scale horizontally across multiple SQLite-based AgentDB files.

AgentDB Advanced Features

Name: AgentDB Advanced Features
Author: nbossn

bynbossn

0•

Database Management

Implements high-performance distributed vector database operations and advanced search algorithms for multi-agent AI systems.

This skill equips Claude with specialized knowledge for implementing production-grade AgentDB configurations, focusing on low-latency synchronization and complex data retrieval. It provides comprehensive patterns for QUIC-based multi-node synchronization, hybrid search filtering, and sharding strategies, enabling developers to build highly scalable and redundant AI memory systems. Whether you are optimizing search diversity with Maximal Marginal Relevance (MMR) or managing distributed database clusters, this skill offers the implementation logic and configuration patterns necessary for sophisticated agentic workflows.

Key Features

01Maximal Marginal Relevance (MMR) implementation to ensure diverse, non-redundant search results.

02Multi-database management and sharding strategies for horizontal scaling of agent memory.

030 GitHub stars

04Support for multiple distance metrics including Cosine, Euclidean, and Dot Product for specialized data types.

05Hybrid search capabilities combining vector similarity with complex metadata filtering and weighted scoring.

06High-performance QUIC synchronization for sub-millisecond cross-node data latency.

Use Cases

01Building distributed multi-agent systems requiring synchronized shared memory across different network nodes.

02Scaling AI applications using database sharding, connection pooling, and performance monitoring.

03Developing recommendation engines that require high-relevance search combined with metadata constraints.

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add nbossn/claude-code-base agentdb-advanced

For use in Claude.ai and ChatGPT

Key Features

01Maximal Marginal Relevance (MMR) implementation to ensure diverse, non-redundant search results.

02Multi-database management and sharding strategies for horizontal scaling of agent memory.

030 GitHub stars

04Support for multiple distance metrics including Cosine, Euclidean, and Dot Product for specialized data types.

05Hybrid search capabilities combining vector similarity with complex metadata filtering and weighted scoring.

06High-performance QUIC synchronization for sub-millisecond cross-node data latency.

Use Cases

01Building distributed multi-agent systems requiring synchronized shared memory across different network nodes.

02Scaling AI applications using database sharding, connection pooling, and performance monitoring.

03Developing recommendation engines that require high-relevance search combined with metadata constraints.