# Coordinate multi-agent swarms with queen-led architecture

Managing multiple AI agents working together on complex tasks is difficult. The Hive Mind system provides queen-led coordination with consensus mechanisms and persistent collective memory, enabling agents to collaborate effectively on large-scale development projects.

## Install

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

## Metadata

- - Slug: dnyoussef-hive-mind-advanced
- - Version: 1.0.0
- - Author: Claude Flow Team
- - GitHub username: DNYoussef
- - License: MIT
- - Repository: https://github.com/DNYoussef/ai-chrome-extension/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/dnyoussef-hive-mind-advanced
- - Manifest: https://skillstore.pages.dev/api/skills/dnyoussef-hive-mind-advanced/manifest

## Capabilities

- Spawn multi-agent swarms with queen-led coordination
- Implement consensus mechanisms \(majority, weighted, Byzantine\)
- Store and retrieve knowledge in collective memory
- Manage agent sessions with checkpoint and resume
- Auto-scale worker agents based on task complexity
- Generate Claude Code Task commands for swarm execution

## Use Cases

- Coordinating large development teams: Deploy specialized agent teams for enterprise applications with architecture, security, and deployment expertise.
- Research and analysis projects: Run adaptive research swarms that gather data, build consensus on findings, and store insights in collective memory.
- Coordinating code review workflows: Spawn multiple reviewers \(security, performance, style, tests\) that vote on approval using consensus mechanisms.

## Prompt Templates

### Initialize Hive Mind

```
Initialize a Hive Mind called 'project-name' with strategic queen coordination for building a full-stack web application.
```

### Spawn Research Swarm

```
Spawn a research hive to investigate [technology or pattern], using adaptive queen type with Byzantine consensus.
```

### Coordinate Code Review

```
Spawn a tactical code review swarm for PR #123, with 6 workers covering security, performance, tests, and documentation review.
```

### Full-Stack Development

```
Spawn a full-stack development hive with strategic queen, 10 workers, weighted consensus, generating Claude Code Task commands for building an e-commerce platform.
```

## Limitations

- Requires Claude Flow package installation to execute
- Memory persistence requires SQLite with adequate disk space
- Complex consensus may slow decision-making for simple tasks
- Queen types must match task complexity for optimal results

## Best Practices

- Match queen type to task complexity: strategic for research, tactical for implementation, adaptive for optimization
- Use Byzantine consensus only for critical decisions requiring 2/3 supermajority approval
- Store key learnings in collective memory after milestone completion for future reference

## Anti Patterns

- Using strategic queen for simple tasks wastes resources and slows decisions
- Skipping session checkpoints risks progress loss on long-running multi-agent tasks
- Ignoring memory consolidation leads to fragmented knowledge across agent sessions

## Security Audit

- - Safe to publish: true
- - Audited at: 2026-01-17T03:42:04.113\+00:00
- - Summary: This skill is pure documentation \(SKILL.md only, 713 lines\). No executable code, scripts, network calls, or filesystem access is present. All 110 static findings are false positives: backtick patterns are markdown code fences, URLs are documentation links, and cryptographic algorithm flags are misidentified distributed systems terminology.

## Stats

- - Views: 307
- - Downloads: 4
- - Favorites: 0
- - Popularity score: 0