
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-codeInstalls 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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