hive-mind-advanced

Coordinate distributed AI agents through hierarchical queen-led consensus and shared memory systems.

Updated Mar 2, 2026

Search Tags:#AI orchestration #multi-agent #consensus #collective intelligence #swarm #coordination

Ready to run

npx skills add https://github.com/ExpertVagabond/ruvector --skill hive-mind-advanced-expertvagabond

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: hive-mind-advanced
Source: https://github.com/ExpertVagabond/ruvector/tree/main/.claude/skills/hive-mind-advanced
Command: npx skills add https://github.com/ExpertVagabond/ruvector --skill hive-mind-advanced-expertvagabond

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill streamlines complex multi-agent coordination by providing a robust framework for hierarchical control, collective decision-making, and shared memory, enabling sophisticated AI systems to operate efficiently and adaptively.

Core Features & Use Cases

Queen-Led Architecture: Orchestrate complex tasks using strategic, tactical, and adaptive queen agents.
Byzantine Consensus: Ensure reliable decision-making even with faulty agents through advanced consensus mechanisms.
Collective Memory: Leverage a shared, persistent memory system for knowledge sharing and learning across agents.
Use Case: Deploy a swarm of specialized agents (researchers, coders, testers) to collaboratively build and optimize a software system, with a queen agent managing the overall project and ensuring consensus on architectural decisions.

Quick Start

Initialize the Hive Mind system by running the command npx claude-flow hive-mind init.

Frequently Asked Questions about hive-mind-advanced

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How does multi-agent coordination handle consensus and fault tolerance in distributed AI swarms?▼

Multi-agent coordination manages distributed AI swarms through a queen-led hierarchical architecture utilizing Byzantine fault tolerance consensus algorithms, ensuring reliable collective decision-making even when individual agents fail or behave maliciously.

What is the best way to orchestrate specialized AI agents for collaborative software development?▼

The best way to orchestrate specialized AI agents is using a queen-led architecture where a queen agent manages the overall project, distributing complex tasks to specialized agents like researchers, coders, and testers to collaboratively build software systems.

How do I initialize a hive mind system for collective intelligence and task distribution?▼

To initialize the hive mind system for collective intelligence, run the command `npx claude-flow hive-mind init`, which sets up the environment for strategic, tactical, and adaptive queen agents to begin coordinating complex task distribution.

Can I use persistent collective memory for knowledge sharing across multiple AI agents?▼

Yes, you can use a shared, persistent collective memory system for knowledge sharing and learning across multiple AI agents, which integrates with RuVector for Q-learning and vector memory to facilitate adaptive strategy and fault-tolerant operations.

Does multi-agent swarm coordination work without external dependencies for vector memory integration?▼

Multi-agent swarm coordination operates independently without external dependencies for basic task distribution, but integrating with RuVector is required to enable advanced Q-learning capabilities and vector memory for persistent collective knowledge sharing across agents.

When do I need Byzantine fault tolerance for AI orchestration and collective decision-making?▼

You need Byzantine fault tolerance for AI orchestration when operating distributed systems where faulty or compromised agents might exist, ensuring that consensus mechanisms maintain reliable collective decision-making and adaptive strategy despite potential node failures.