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

# Basic initialization
npx claude-flow hive-mind init