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.