# Hive Mind Advanced

Advanced Hive Mind collective intelligence system for queen-led multi-agent...

- Repository: https://github.com/ruvnet/RuVector (4653 stars)
- Category: AI and agents
- License: MIT
- Last updated: 2026-11-01
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add ruvnet/RuVector --skill hive-mind-advanced -g -a claude-code -y`
Codex: `npx skills add ruvnet/RuVector --skill hive-mind-advanced -g -a codex -y`
Cursor: `npx skills add ruvnet/RuVector --skill hive-mind-advanced -g -a cursor -y`
Gemini CLI: `npx skills add ruvnet/RuVector --skill hive-mind-advanced -g -a gemini-cli -y`

## Description

Advanced Hive Mind collective intelligence system for queen-led multi-agent coordination with consensus mechanisms and persistent memory

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Hive Mind Advanced Skill

Master the advanced Hive Mind collective intelligence system for sophisticated multi-agent coordination using queen-led architecture, Byzantine consensus, and collective memory.

## 🧠 Self-Learning Intelligence
Integrates with RuVector's Q-learning and vector memory for improved performance.
CLI: `node .claude/intelligence/cli.js stats`

## Overview

The Hive Mind system represents the pinnacle of multi-agent coordination in Claude Flow, implementing a queen-led hierarchical architecture where a strategic queen coordinator directs specialized worker agents through collective decision-making and shared memory.

## Core Concepts

### Architecture Patterns

**Queen-Led Coordination**
- Strategic queen agents orchestrate high-level objectives
- Tactical queens manage mid-level execution
- Adaptive queens dynamically adjust strategies based on performance

**Worker Specialization**
- Researcher agents: Analysis and investigation
- Coder agents: Implementation and development
- Analyst agents: Data processing and metrics
- Tester agents: Quality assurance and validation
- Architect agents: System design and planning
- Reviewer agents: Code review and improvement
- Optimizer agents: Performance enhancement
- Documenter agents: Documentation generation

**Collective Memory System**
- Shared knowledge base across all agents
- LRU cache with memory pressure handling
- SQLite persistence with WAL mode
- Memory consolidation and association
- Access pattern tracking and optimization

### Consensus Mechanisms

**Majority Consensus**
Simple voting where the option with most votes wins.

**Weighted Consensus**
Queen vote counts as 3x weight, providing strategic guidance.

**Byzantine Fault Tolerance**
Requires 2/3 majority for decision approval, ensuring robust consensus even with faulty agents.

## Getting Started

### 1. Initialize Hive Mind

```bash
# Basic initialization
npx claude-flow hive-mind init

The rest: https://github.com/ruvnet/RuVector/blob/HEAD/.claude/skills/hive-mind-advanced/SKILL.md