hive-mind-advanced

name	hive-mind-advanced
description	Queen-led multi-agent coordination in Claude Flow — one strategic queen directs specialized workers through structured voting and shared persistent memory. Use when a task needs formal consensus (majority, weighted, or Byzantine fault tolerance) and cross-agent collective memory, not just topology-based swarming.
version	1.0.0
category	coordination
tags	["hive-mind","swarm","queen-worker","consensus","collective-intelligence","multi-agent","coordination"]
author	Claude Flow Team

Hive Mind Advanced Skill

Queen-led hierarchical multi-agent coordination: a strategic queen coordinator directs specialized worker agents through collective decision-making (majority / weighted / Byzantine consensus) and a shared, persistent collective memory. This is the most structured coordination model in Claude Flow — reach for it when decisions need formal voting and agents need to share knowledge across a session.

Quick path

# 1. Initialize
npx claude-flow hive-mind init

# 2. Spawn a swarm against an objective (pick a queen type + consensus)
npx claude-flow hive-mind spawn "Build microservices architecture" \
  --queen-type strategic --max-workers 8 --consensus weighted --claude

# 3. Monitor
npx claude-flow hive-mind status
npx claude-flow hive-mind metrics
npx claude-flow hive-mind memory

Queen types: strategic (research/planning), tactical (implementation), adaptive (optimisation/dynamic). Consensus: majority, weighted (queen 3x), byzantine (2/3 supermajority). Memory persists to RuVector PostgreSQL (pgvector/HNSW) in production, SQLite locally.

When to use

Use the Hive Mind when the work genuinely needs:

• Formal consensus on critical decisions (architecture choice, tech stack, release readiness) — not just parallel task execution.
• Collective memory shared across agents and resumable sessions.
• Queen-led hierarchy — a coordinator that decomposes objectives and assigns specialized workers (researcher, coder, analyst, tester, architect, reviewer, optimizer, documenter).

When not to use

• Topology-based agent coordination (mesh, hierarchical, star, ring) without formal voting → swarm-advanced is simpler.
• A structured development lifecycle (spec, architecture, TDD, review, deploy) → sparc-methodology (17 development modes).
• Full development + quality-engineering pipelines → build-with-quality.
• Cloud-based swarm deployment / workflow automation → flow-nexus-swarm.
• GitHub-specific PR review coordination → github-code-review.

Reference material

Detailed guidance lives in references/ — load the file for the task at hand:

• references/architecture.md — architecture patterns, worker specialization, collective memory system, RuVector PostgreSQL backend, consensus mechanisms.
• references/operations.md — init / spawn / monitor commands, session management, consensus building, collective-memory operations, task distribution, auto-scaling, and integration with Claude Code / SPARC / GitHub.
• references/performance-and-config.md — memory & database optimisation, benchmarks, hive/memory config schemas, hooks integration, and best practices per queen type.
• references/api-and-cookbook.md — troubleshooting (memory / performance / consensus), advanced topics (custom workers, neural training, multi-hive, export/import), the HiveMindCore / CollectiveMemory / HiveMindSessionManager API reference, worked examples, and skill progression.

Related skills

• swarm-advanced — general swarm coordination (no consensus required)
• consensus-mechanisms — distributed decision making
• memory-systems — advanced memory management
• sparc-methodology — structured development workflow
• github-code-review — repository PR review and coordination

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Skill Version: 1.0.0 Last Updated: 2026-07-28 Maintained By: Claude Flow Team License: MIT