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Hive Mind Advanced

This is a sophisticated multi-agent orchestration system that implements queen-led architecture where a coordinator agent directs specialized workers (researchers, coders, testers, architects) through Byzantine consensus and shared SQLite memory. You'd use this when a task needs genuine division of labor, like building a full microservices architecture where different agents handle API design, implementation, testing, and documentation in parallel. The collective memory system with LRU caching and association tracking is genuinely clever, letting agents build on each other's work instead of starting fresh. Fair warning though: the complexity overhead only pays off for substantial projects. For simple tasks, you're just adding coordination cost. The 84.8% SWE-Bench solve rate and 10-20x batch spawning improvement suggest it actually works when the problem fits the paradigm.

skills.sh installs
1.1K
GitHub stars
81.4K
last updated
Aug 21, 2026

Install

$ npx -y skills add ruvnet/ruflo --skill hive-mind-advanced --agent claude-code

Installs into .claude/skills of the current project.

Files

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.

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

Sponsors

Details

Repository
github.com/ruvnet/ruflo
Category
AI & Agent Building
First seen
Jun 3, 2026

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