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hive-mind-advanced

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

Updated Jun 5, 2026
Search Tags:#persistence#collective intelligence#multi-agent coordination#consensus algorithms#queen-led architecture
One-click install
npx skills add https://github.com/LGugui/cerebro-template --skill hive-mind-advanced-lgugui
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: hive-mind-advanced
Source: https://github.com/LGugui/cerebro-template/tree/main/.claude/skills/hive-mind-advanced
Command: npx skills add https://github.com/LGugui/cerebro-template --skill hive-mind-advanced-lgugui

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires hive-mind-core, collective-memory, consensus-mechanisms, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill unit tackles complex multi-agent coordination and collective intelligence challenges, enabling queen-led architectures with consensus mechanisms and persistent memory systems.

Core Features & Use Cases

  • Multi-Agent Coordination: Orchestrate sophisticated workflows with researcher, coder, analyst, tester, architect, reviewer, and optimizer agents.
  • Queen-Led Architecture: Implement a queen-led hierarchical structure with strategic, tactical, and adaptive queens.
  • Consensus Mechanisms: Utilize majority, weighted, and Byzantine fault-tolerant consensus algorithms.
  • Collective Memory System: Leverage shared knowledge bases and SQLite persistence with WAL mode.
  • Use Case: Picture a project requiring a comprehensive architecture decision, where this Skill unit can facilitate the collaboration of specialized agents to reach a consensus on the best approach.

Quick Start

To initialize the Hive Mind system, use 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 agents to reach a consensus on complex architecture decisions?▼

You can orchestrate multi-agent coordination by initializing a queen-led collective intelligence system. It utilizes specialized agents, consensus algorithms, and persistent memory to facilitate complex workflows and reach strategic decisions.

What is a queen-led architecture in multi-agent systems?▼

A queen-led architecture in multi-agent systems establishes a hierarchical structure using strategic, tactical, and adaptive queens. This framework directs specialized agents to execute sophisticated workflows and coordinate collective intelligence tasks efficiently.

How do I initialize a hive mind system for multi-agent coordination?▼

You initialize the hive mind system by running the command 'npx claude-flow hive-mind init'. This configures the necessary collective memory and consensus mechanisms for multi-agent coordination.

Do I need specific dependencies to run consensus mechanisms with persistent memory?▼

Yes, utilizing consensus mechanisms with persistent memory requires hive-mind-core, collective-memory, and consensus-mechanisms dependencies. These provide the SQLite WAL mode persistence and fault-tolerant algorithms necessary for operation.

What consensus algorithms are available for multi-agent coordination?▼

Available consensus algorithms for multi-agent coordination include majority, weighted, and Byzantine fault-tolerant models. These algorithms allow specialized agents to agree on complex architecture decisions within the collective intelligence system.

Can I use SQLite for persistent memory in a collective intelligence system?▼

Yes, you can use SQLite for persistent memory in a collective intelligence system. It utilizes SQLite with WAL mode to maintain shared knowledge bases across specialized agents during multi-agent coordination tasks.

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