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hive-mind-advanced

Coordinate multi-agent swarms with a queen-led architecture and persistent memory.

Updated Apr 23, 2026
Search Tags:#multi-agent#orchestration#memory#consensus#distributed-systems#queen-led#hivemind
One-click install
npx skills add https://github.com/fableindigo-gif/animated-system --skill hive-mind-advanced-fableindigo-gif
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: hive-mind-advanced
Source: https://github.com/fableindigo-gif/animated-system/tree/main/omnianalytix-mirror/.claude/skills/hive-mind-advanced
Command: npx skills add https://github.com/fableindigo-gif/animated-system --skill hive-mind-advanced-fableindigo-gif

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Hive Mind Advanced Skill enables queen-led, multi-agent coordination with persistent memory to orchestrate complex tasks across heterogeneous agents, reducing coordination overhead and enabling robust consensus.

Core Features & Use Cases

  • Queen-led orchestration and hierarchical coordination for strategic planning and execution.
  • Consensus mechanisms (majority, weighted, Byzantine) to ensure robust decisions in the presence of faulty agents.
  • Collective memory with a shared knowledge base, memory consolidation, and persistent storage for long-running tasks.

Quick Start

Spawn a hive mind with a strategic queen and observe coordinated activity.

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 strategic planning and real-time decision making?▼

You coordinate multi-agent swarms using a queen-led architecture where a central queen agent orchestrates diverse roles like researcher, coder, and analyst to achieve coordinated strategic decisions and long-running task execution.

What consensus mechanisms can I use for distributed agents when some might be faulty?▼

For distributed agents that might be faulty, you can apply majority, weighted, or Byzantine consensus mechanisms to ensure robust coordinated decisions even in the presence of faulty or unreliable agents.

How does persistent memory work for long-running multi-agent tasks across sessions?▼

Persistent memory for multi-agent tasks is maintained through a shared knowledge base and a WAL-backed storage layer, which collectively preserve memory consolidation and coordination state across multiple sessions.

Can I use a hivemind architecture to orchestrate heterogeneous agents like coders and testers?▼

Yes, you can orchestrate heterogeneous agents by assigning specialized roles such as researcher, coder, analyst, tester, and architect within the queen-led hivemind to reduce coordination overhead for complex tasks.

What's the best way to reduce coordination overhead when managing diverse agent roles?▼

The best way to reduce coordination overhead is implementing hierarchical queen-led orchestration with consensus protocols and a collective memory layer, enabling robust autonomous coordination without manual intervention.

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