How does the Collective Memory system benefit my project?

Collective Memory provides a shared, persistent knowledge base (backed by SQLite) that allows all agents to access historical insights, context, and cross-session data without losing track of progress.

Which consensus mechanisms are supported?

Hive Mind Advanced supports Majority voting, Weighted consensus (where the Queen has 3x weight), and Byzantine Fault Tolerance for high-reliability decision-making.

How does this improve performance compared to single-agent tasks?

It provides up to 4.4x speed improvements through parallel processing and has been shown to reduce token consumption by over 32% in complex scenarios.

What is a Queen agent in the Hive Mind system?

A Queen is a high-level coordinator agent that manages strategic objectives, orchestrates tactical workers, and facilitates decision-making across the agent swarm.

Does this skill work with the SPARC methodology?

Yes, it includes built-in integration for SPARC workflows, allowing you to spawn specification, architecture, and testing agents automatically.

Hive Mind Advanced

Name: Hive Mind Advanced
Author: ellisapotheosis

byellisapotheosis

Collaboration Tools

Orchestrates sophisticated multi-agent swarms using queen-led architectures and persistent collective memory systems.

About

Hive Mind Advanced is a high-level coordination skill designed for Claude Code that enables the management of complex, multi-agent workflows through a hierarchical 'queen-worker' model. By implementing robust consensus mechanisms—including Byzantine Fault Tolerance—and a persistent SQLite-backed collective memory, it allows specialized AI agents to collaborate on large-scale software projects with strategic alignment. This skill is essential for developers who need to automate complex architectural planning, distributed implementation tasks, and rigorous multi-stage code reviews while maintaining context across long-running sessions.

Key Features

Hierarchical Queen-Led Coordination for strategic and tactical oversight
Intelligent Task Distribution based on agent specialization and performance
Persistent Collective Memory with SQLite storage and LRU caching
Seamless integration with Claude Code and SPARC methodology
Multi-algorithm consensus (Majority, Weighted, and Byzantine)
0 GitHub stars

Use Cases

Managing high-stakes technical decisions through weighted voting and consensus building
Maintaining deep project context across long-running development and research sessions
Orchestrating a swarm of specialized agents to build complex microservices architectures

About

Key Features

Hierarchical Queen-Led Coordination for strategic and tactical oversight
Intelligent Task Distribution based on agent specialization and performance
Persistent Collective Memory with SQLite storage and LRU caching
Seamless integration with Claude Code and SPARC methodology
Multi-algorithm consensus (Majority, Weighted, and Byzantine)
0 GitHub stars

Use Cases

Managing high-stakes technical decisions through weighted voting and consensus building
Maintaining deep project context across long-running development and research sessions
Orchestrating a swarm of specialized agents to build complex microservices architectures