What is Collective Memory?

It is a shared SQLite-backed knowledge base that allows all agents in a swarm to store and retrieve insights, context, and execution results in real-time.

What is a Hive Mind in Claude Flow?

It is a collective intelligence system where a 'queen' agent coordinates multiple specialized 'worker' agents (like researchers, coders, and testers) to solve complex tasks through shared goals.

Can I use this for existing codebases?

Yes, it integrates with GitHub and Claude Code to analyze repositories, review pull requests, and implement features across large, established projects.

How does the Byzantine consensus work?

It requires a 2/3 supermajority for decision approval, ensuring the swarm reaches a reliable conclusion even if some agents provide inconsistent or faulty results.

Does it support worker auto-scaling?

Yes, the system can automatically scale worker agents up or down based on task complexity, pending queue size, and available system resources.

Advanced Hive Mind Agent Coordination

Name: Advanced Hive Mind Agent Coordination
Author: ricable

byricable

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Productividad y Flujo de Trabajo

Orchestrates complex multi-agent workflows using a queen-led hierarchical architecture and Byzantine consensus for high-reliability collective intelligence.

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This skill enables sophisticated multi-agent orchestration within the Claude Flow ecosystem, allowing users to deploy specialized swarms directed by a strategic coordinator. It implements robust consensus mechanisms—including Majority, Weighted, and Byzantine Fault Tolerance—to ensure reliable decision-making across distributed agents. With a persistent collective memory system backed by SQLite and LRU caching, the Hive Mind ensures that context and insights are shared seamlessly across researchers, coders, and architects, making it ideal for large-scale system design and complex implementation tasks.

Características Principales

Queen-led hierarchical agent coordination (Strategic, Tactical, Adaptive)
Intelligent task distribution based on worker specialization
2 GitHub stars
Advanced consensus mechanisms including Byzantine Fault Tolerance
Seamless integration with Claude Code and SPARC methodology
Persistent collective memory with SQLite and LRU caching

Casos de Uso

Automated large-scale repository analysis and security auditing
Building complex microservices architectures through specialized worker swarms
High-reliability decision making for critical system architecture choices

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Características Principales

Queen-led hierarchical agent coordination (Strategic, Tactical, Adaptive)
Intelligent task distribution based on worker specialization
2 GitHub stars
Advanced consensus mechanisms including Byzantine Fault Tolerance
Seamless integration with Claude Code and SPARC methodology
Persistent collective memory with SQLite and LRU caching

Casos de Uso

Automated large-scale repository analysis and security auditing
Building complex microservices architectures through specialized worker swarms
High-reliability decision making for critical system architecture choices