hive-mind-advanced

Orchestrate multi-agent systems with queen-led hierarchy and consensus mechanisms.

2|Updated Jan 25, 2026
One-click install
npx skills add https://github.com/EarthmanWeb/claude-flow-plugin --skill hive-mind-advanced-earthmanweb
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: hive-mind-advanced
Source: https://github.com/EarthmanWeb/claude-flow-plugin/tree/main/.claude/skills/hive-mind-advanced
Command: npx skills add https://github.com/EarthmanWeb/claude-flow-plugin --skill hive-mind-advanced-earthmanweb

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill provides a sophisticated framework for coordinating multiple AI agents in complex, collaborative tasks, enabling advanced problem-solving and execution through a hierarchical, queen-led architecture.

Core Features & Use Cases

  • Hierarchical Coordination: Employs queen agents (strategic, tactical, adaptive) to direct worker agents.
  • Consensus Mechanisms: Implements majority, weighted, and Byzantine fault-tolerant consensus for robust decision-making.
  • Collective Memory: Features a persistent, shared memory system for knowledge sharing and learning across agents.
  • Use Case: Orchestrate a swarm of AI agents to develop a complex software application, with a strategic queen defining the architecture, tactical queens managing feature development, and worker agents handling coding, testing, and documentation, all while learning from a shared memory of past projects.

Quick Start

Use the hive-mind-advanced skill to spawn a swarm to build a microservices architecture.

Frequently Asked Questions about hive-mind-advanced

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

FAQPage Schema
How do I coordinate multiple AI agents for complex task execution?

You coordinate multiple AI agents using a queen-led hierarchical architecture where strategic and tactical queen agents direct specialized worker agents to execute complex tasks collaboratively.

What is Byzantine fault-tolerant consensus in multi-agent systems?

Byzantine fault-tolerant consensus is a robust decision-making mechanism that allows a multi-agent swarm to reach agreement and maintain operations even when some agents fail or act unreliably.

How does collective memory work for AI agent swarms?

Collective memory provides a persistent, shared storage system that enables AI agents to share knowledge, learn from past projects, and optimize performance across the entire swarm.

Can I use a hierarchical swarm to build a microservices architecture?

Yes, you can spawn a swarm with a strategic queen defining the architecture, tactical queens managing features, and worker agents handling coding, testing, and documentation.

What is the best way to manage enterprise AI applications with fault-tolerant decision-making?

Managing enterprise AI applications is best handled by orchestrating specialized worker agents through adaptive queen agents that utilize robust consensus mechanisms for fault-tolerant decision-making.

Do I need external dependencies to implement swarm coordination?

No external dependencies are required to implement swarm coordination, as the framework operates independently using its internal scripts and references to orchestrate the multi-agent system.