hive-mind-advanced

Name: hive-mind-advanced
Availability: InStock
Author: dejavoo21

Coordinate multi-agent systems with queen-led orchestration and configurable consensus algorithms.

Updated Apr 6, 2026

Search Tags:#multi-agent #orchestration #memory #consensus #hive-mind #coordination #distributed-systems

Ready to run

npx skills add https://github.com/dejavoo21/Claude-Code-Help --skill hive-mind-advanced-dejavoo21

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: hive-mind-advanced
Source: https://github.com/dejavoo21/Claude-Code-Help/tree/main/.claude/skills/hive-mind-advanced
Command: npx skills add https://github.com/dejavoo21/Claude-Code-Help --skill hive-mind-advanced-dejavoo21

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Orchestrates complex, queen-led multi-agent systems to achieve coordinated decisions with reliable memory and fault-tolerant consensus.

Core Features & Use Cases

Queen-led coordination and worker specialization across researchers, coders, analysts, testers, architects, reviewers, optimizers, and documenters.
Persistent collective memory with a shared knowledge base, LRU caching, and SQLite WAL persistence for durable state.
Flexible consensus mechanisms (majority, weighted, Byzantine) to ensure robust decisions across a swarm.
Session management and task distribution to scale orchestration across multiple swarms and objectives.

Quick Start

Initialize a Hive Mind, spawn a swarm with a clear objective, and monitor its consensus-driven progress.

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 AI systems to achieve complex objectives?▼

Multi-agent AI systems are coordinated through queen-led orchestration, which manages worker specialization across roles like researchers, coders, and analysts to achieve strategic objectives with collective memory and configurable consensus algorithms.

What is queen-led orchestration in distributed AI systems?▼

Queen-led orchestration is a coordination mechanism where a central queen agent manages task distribution and monitors consensus-driven progress across specialized worker agents, ensuring adaptive decision making and collaborative problem solving within a swarm.

Can I use Byzantine fault tolerance for multi-agent consensus decisions?▼

Yes, Byzantine fault tolerance is supported as a configurable consensus mechanism alongside majority and weighted algorithms, ensuring robust and reliable decisions across distributed autonomous agents even with conflicting inputs.

How does collective memory persistence work across multiple agent swarms?▼

Collective memory persistence uses a shared knowledge base with LRU caching and SQLite WAL mode, ensuring durable state retention and session management across multiple swarms and complex workflow orchestration.

How do I scale distributed workflow orchestration across multiple agent swarms?▼

Distributed workflow orchestration scales across multiple swarms using session management and task distribution features, allowing you to initialize a Hive Mind, spawn swarms with clear objectives, and monitor consensus-driven progress.