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hive-mind-advanced

Coordinate queen-led multi-agent systems with consensus mechanisms and persistent memory.

25|41|Updated Nov 24, 2025
Search Tags:#multi-agent#consensus#collective intelligence#coordination#ai orchestration#hive mind
One-click install
npx skills add https://github.com/agenticsorg/hackathon-tv5 --skill hive-mind-advanced-agenticsorg
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: hive-mind-advanced
Source: https://github.com/agenticsorg/hackathon-tv5/tree/main/.claude/skills/hive-mind-advanced
Command: npx skills add https://github.com/agenticsorg/hackathon-tv5 --skill hive-mind-advanced-agenticsorg

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill tackles complex multi-agent coordination challenges by providing a robust, queen-led hierarchical system with advanced consensus mechanisms and persistent collective memory.

Core Features & Use Cases

  • Queen-Led Architecture: Orchestrate complex tasks with strategic, tactical, and adaptive queen agents.
  • Worker Specialization: Leverage a diverse set of specialized worker agents (researcher, coder, analyst, etc.) for efficient task execution.
  • Collective Memory: Utilize a shared, persistent knowledge base with LRU caching and SQLite persistence for seamless information sharing.
  • Consensus Mechanisms: Implement majority, weighted, or Byzantine fault-tolerant consensus for reliable decision-making.
  • Use Case: Coordinate a team of AI agents to design, develop, test, and document a new software microservice, ensuring all agents work cohesively and decisions are made collectively.

Quick Start

Initialize the advanced Hive Mind system for a new project 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 to work together on a complex software development task?▼

Multi-agent coordination is managed through a queen-led hierarchical architecture that distributes specialized roles like researcher, coder, and analyst to execute complex software development tasks cohesively.

What is a queen-led multi-agent system and how does it handle collective decision-making?▼

A queen-led multi-agent system uses strategic, tactical, and adaptive queen agents to orchestrate tasks, while consensus mechanisms like majority, weighted, or Byzantine fault-tolerant voting ensure reliable collective decision-making.

How do I initialize a hive mind for distributed AI orchestration?▼

Initialize the hive mind for distributed AI orchestration by running the command 'npx claude-flow hive-mind init', which sets up the advanced collective intelligence environment with persistent memory.

Can I integrate multi-agent coordination with Claude Code and the SPARC methodology?▼

Yes, the multi-agent coordination system supports direct integration with Claude Code and the SPARC methodology to enhance and structure AI-driven development workflows.

Does the multi-agent collective memory support persistent storage for shared knowledge?▼

The collective memory supports persistent storage by utilizing a shared knowledge base with LRU caching and SQLite persistence, ensuring distributed AI agents can seamlessly share and retain information.

What's the best way to ensure fault tolerance in distributed AI agent coordination?▼

Fault tolerance in distributed AI agent coordination is achieved by implementing Byzantine fault-tolerant consensus mechanisms, allowing the collective intelligence system to make reliable decisions even if some agents fail.

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