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hive-mind-advanced

Orchestrate queen-led multi-agent coordination with configurable consensus algorithms and persistent memory.

4|3|Updated Oct 26, 2025
Search Tags:#orchestration#memory#consensus#hive-mind#coordination#mult-agent
One-click install
npx skills add https://github.com/natea/fitfinder --skill hive-mind-advanced-natea
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: hive-mind-advanced
Source: https://github.com/natea/fitfinder/tree/main/.claude/skills/hive-mind-advanced
Command: npx skills add https://github.com/natea/fitfinder --skill hive-mind-advanced-natea

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Coordinates and orchestrates a distributed team of AI agents to tackle complex tasks with reliable consensus and persistent memory, reducing manual orchestration and human error in large-scale projects.

Core Features & Use Cases

  • Queen-led coordination with hierarchical roles (queen coordinators, worker agents) for strategic planning and execution.
  • Collective memory with persistent storage, memory types, and memory consolidation for learned patterns.
  • Flexible consensus mechanisms (majority, weighted, Byzantine) to ensure robust decision making across agents.
  • Session management, checkpointing, and export/import for fault tolerance and reproducibility.
  • Use Case: orchestrating end-to-end product development pipelines across AI agents for design, implementation, testing, and documentation.

Quick Start

Initialize a hive mind with a strategic queen, spawn workers, and coordinate a multi-stage deployment with persistent memory.

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 on a complex task together?▼

Multi-agent coordination is handled through queen-led swarm orchestration, where a strategic queen agent plans and delegates execution to worker agents to solve complex tasks collaboratively with persistent collective memory.

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

Consensus algorithms for multi-agent decision making include configurable majority, weighted, and Byzantine fault-tolerant mechanisms, ensuring robust agreement across distributed agents and tasks.

How does persistent memory work across distributed AI agents?▼

Persistent memory for distributed agents uses collective storage with multiple memory types and memory consolidation for learned patterns, retaining coordination context and session history across complex workflows.

Can I checkpoint and export multi-agent coordination sessions?▼

Multi-agent coordination sessions support checkpointing, session management, and export/import capabilities, providing fault tolerance and reproducibility for large-scale AI workflows.

What's the best way to orchestrate end-to-end product development pipelines across AI agents?▼

Orchestrating end-to-end product pipelines is best achieved by initializing a hive mind with a strategic queen, spawning worker agents, and coordinating multi-stage design, implementation, testing, and documentation tasks.

Does multi-agent coordination support hierarchical roles for strategic planning?▼

Multi-agent coordination supports hierarchical roles with queen coordinators for strategic planning and worker agents for execution, reducing manual orchestration and human error in large-scale projects.

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