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hive-mind-advanced

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

11|3|Updated Jun 30, 2025
Search Tags:#ai agents#multi-agent#distributed systems#collective intelligence#coordination#swarm intelligence
One-click install
npx skills add https://github.com/aegntic/cldcde --skill hive-mind-advanced-aegntic
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: hive-mind-advanced
Source: https://github.com/aegntic/cldcde/tree/main/.claude/skills/hive-mind-advanced
Command: npx skills add https://github.com/aegntic/cldcde --skill hive-mind-advanced-aegntic

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill addresses the complexity of coordinating multiple AI agents for sophisticated tasks by providing a robust, queen-led hierarchical architecture with advanced consensus and memory mechanisms.

Core Features & Use Cases

  • Queen-Led Architecture: Orchestrate complex projects with strategic, tactical, and adaptive queen agents.
  • Worker Specialization: Assign specialized agents (researcher, coder, tester, etc.) to specific roles for efficient task execution.
  • Collective Memory: Maintain a shared, persistent knowledge base across all agents for seamless information flow and learning.
  • Consensus Mechanisms: Ensure reliable decision-making through majority, weighted, or Byzantine fault-tolerant consensus.
  • Use Case: Coordinate a team of AI agents to build a full-stack application, with a strategic queen defining the architecture, tactical queens managing frontend and backend development, and specialized agents handling testing, documentation, and optimization, all while learning from a shared memory of past projects.

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 do I coordinate multiple AI agents for complex task execution?▼

You coordinate multiple AI agents using a queen-led hierarchical architecture with specialized worker agents and consensus mechanisms for complex task coordination. The system orchestrates strategic, tactical, and adaptive queens to manage specialized roles like research, coding, and testing.

What is collective memory in multi-agent systems and how does it work?▼

Collective memory in multi-agent systems maintains a shared, persistent knowledge base across all agents for seamless information flow and learning. It enables specialized worker agents to share state, persist project context, and learn from past executions.

How do I initialize a multi-agent swarm intelligence system for development workflows?▼

You initialize the swarm intelligence system by running the command `npx claude-flow hive-mind init`. This sets up the queen-led hierarchy and collective memory required to orchestrate complex development projects.

Can I use distributed AI agents with Claude Code and SPARC methodology?▼

Yes, the multi-agent coordination system supports integration with development workflows like Claude Code and SPARC methodology. It assigns specialized agents to specific roles within these workflows for efficient task execution.

What consensus mechanisms are available for distributed AI agent decision-making?▼

The distributed AI agent system supports majority, weighted, and Byzantine fault-tolerant consensus mechanisms. These mechanisms ensure reliable decision-making across specialized worker agents within the queen-led hierarchy.

When do I need a queen-led hierarchical architecture for multi-agent coordination?▼

You need a queen-led hierarchical architecture when coordinating sophisticated tasks that require strategic, tactical, and adaptive oversight. It is ideal for complex projects like building full-stack applications where multiple specialized agents handle distinct phases.

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