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hive-mind-advanced

Coordinate multi-agent swarms with queen-led governance and consensus algorithms.

Updated Jul 2, 2025
Search Tags:#multi-agent#consensus#hive-mind#distributed-systems#queen-led#collective-memory
One-click install
npx skills add https://github.com/dug-21/neural-data-platform --skill hive-mind-advanced-dug-21
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: hive-mind-advanced
Source: https://github.com/dug-21/neural-data-platform/tree/main/.claude/skills/hive-mind-advanced
Command: npx skills add https://github.com/dug-21/neural-data-platform --skill hive-mind-advanced-dug-21

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Coordinated multi-agent decision-making is complex and error-prone when teams of autonomous agents must align on objectives, actions, and memory. This Skill provides a queen-led governance structure, consensus mechanisms, and a shared memory layer to streamline collaboration and maintain state across agents.

Core Features & Use Cases

  • Queen-Led Coordination: A strategic queen directs high-level objectives while tactical executives handle execution.
  • Worker Specialization: Distinct agent roles for research, coding, analysis, testing, architecture, and review.
  • Collective Memory: Shared memory with LRU caching and persistent WAL-based storage to maintain context and learning across sessions.
  • Consensus Mechanisms: Majority, weighted, and Byzantine options to ensure robust decisions in distributed settings.
  • Use Cases: Complex project governance, multi-agent automation, and collaborative system design.

Quick Start

Use the hive mind CLI to initialize and spawn swarms for a coordinated task: npx claude-flow hive-mind init npx claude-flow hive-mind spawn "Coordinate distributed system design" --queen-type strategic --max-workers 8 npx claude-flow hive-mind status

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 multi-agent swarms for complex software development tasks?▼

Multi-agent swarms are coordinated using a queen-led hive mind, where a strategic queen directs high-level objectives while tactical executives and specialized worker agents handle execution, research, coding, and testing.

How does collective memory maintain context across distributed agents?▼

Collective memory maintains context across distributed agents using shared memory with LRU caching and persistent WAL-based storage, ensuring state and learning persist across multiple collaborative sessions.

What consensus algorithms can I use for distributed agent decision-making?▼

Available consensus algorithms for distributed agent decision-making include majority, weighted, and Byzantine options, ensuring robust decisions in distributed settings where autonomous agents must align on actions.

Can I use this for multi-agent system orchestration without external tooling?▼

No, implementing this queen-led hive mind requires external tooling integration with Claude Flow, utilizing its CLI to initialize swarms, spawn workers, and monitor multi-agent coordination status.

What's the best way to initialize a hive mind for collaborative system design?▼

Initialize a hive mind for collaborative system design by running `npx claude-flow hive-mind init`, then spawn swarms with configurable queen types and worker limits to orchestrate the distributed task.

When should I avoid using a queen-led hive mind for multi-agent automation?▼

Avoid queen-led hive mind coordination for simple, independent tasks that do not require hierarchical governance, shared memory, or consensus, as the overhead of managing distributed swarms outweighs the benefits.

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