# Coordinate Multi-Agent AI Teams with Hive Mind

Complex problems require coordinated effort from multiple AI agents. Hive Mind provides a queen-led architecture that enables consensus-based decision-making and shared memory for collective problem-solving across distributed agent swarms.

## Install

```bash
npx skillstore add claude flow team/ruvnet-hive-mind-advanced
```

## Metadata

- - Slug: ruvnet-hive-mind-advanced
- - Version: 1.0.0
- - Author: Claude Flow Team
- - GitHub username: ruvnet
- - License: MIT
- - Repository: https://github.com/ruvnet/claude-flow/tree/main/.claude/skills/hive-mind-advanced
- - Ref: main
- - Supported tools: Claude, Codex, Claude Code
- - Risk level: safe
- - Risk factors: network, filesystem, external\_commands
- - Quality score: 68
- - Quality tier: warning
- - Public page: https://skillstore.pages.dev/skills/ruvnet-hive-mind-advanced
- - Manifest: https://skillstore.pages.dev/api/skills/ruvnet-hive-mind-advanced/manifest

## Capabilities

- Spawns and coordinates multiple worker agents under queen-led supervision for parallel task execution
- Implements consensus voting mechanisms for collective decision-making across agent swarms
- Maintains persistent shared memory for cross-agent context and learnings
- Manages task delegation with dynamic load balancing based on worker capabilities
- Enables emergent problem-solving through agent-to-agent communication protocols
- Tracks collective progress and synthesizes results from distributed agent outputs

## Use Cases

- Parallel Codebase Analysis: Deploy multiple agents to analyze different subsystems of a large codebase simultaneously. The queen agent synthesizes findings into a comprehensive security audit or architecture review.
- Collaborative Writing Projects: Coordinate agents with different expertise to research, outline, draft, and review documentation or technical content in parallel, ensuring consistency through shared memory.
- Research Synthesis: Task multiple agents with exploring different research topics or data sources. The hive mind aggregates insights and identifies patterns across all collected information.

## Prompt Templates

### Basic Hive Mind Setup

```
Activate Hive Mind with 3 worker agents. Queen directive: [describe overall goal]. Workers should coordinate to [specific task]. Report findings back to queen for synthesis.
```

### Consensus-Based Decision

```
Deploy 5 workers to analyze [problem] from different angles. Each worker should propose a solution approach. Use consensus voting to select the best path forward. Queen to break ties.
```

### Research Swarm

```
Queen directive: Research [topic]. Workers specialize: one searches documentation, one analyzes examples, one tests hypotheses, one reviews limitations. Share findings in shared memory. Synthesize comprehensive report.
```

### Iterative Refinement Loop

```
Initialize Hive Mind for iterative improvement. Queen assigns workers to: generate solution, critique solution, improve solution, validate solution. Loop until consensus on quality threshold.
```

## Limitations

- Requires careful queen prompt design to avoid conflicting worker instructions
- Shared memory size constraints may limit context retention for very long tasks
- Consensus voting adds latency for time-critical single-agent decisions
- Complex swarm topologies may become difficult to debug and monitor

## Best Practices

- Define clear queen directives that establish goals without over-constraining worker autonomy
- Use specialized worker roles for distinct concerns to avoid redundant work
- Set explicit consensus thresholds and tie-breaking rules for decision-making
- Monitor shared memory for conflicting conclusions and resolve contradictions

## Anti Patterns

- Overloading the queen with micro-management that defeats parallel execution benefits
- Deploying workers with overlapping responsibilities causing redundant effort
- Ignoring consensus minority views that may represent important edge cases
- Failing to establish memory cleanup policies for long-running swarms

## Security Audit

- - Safe to publish: true
- - Audited at: 2026-01-21T18:45:29.122\+00:00
- - Summary: Static analysis detected 108 potential issues, all evaluated as false positives. Network and URL findings are legitimate source URL references. Cryptographic algorithm alerts are pattern matching errors triggered by text substrings. External command findings are documentation examples, not code vulnerabilities. This is a legitimate multi-agent coordination system.

## Stats

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- - Downloads: 5
- - Favorites: 0
- - Popularity score: 0