hive-mind-advanced
Advanced Hive Mind collective intelligence system for queen-led multi-agent coordination with consensus mechanisms and persistent memory
Best use case
hive-mind-advanced is best used when you need a repeatable AI agent workflow instead of a one-off prompt. It is especially useful for teams working in multi. Advanced Hive Mind collective intelligence system for queen-led multi-agent coordination with consensus mechanisms and persistent memory
Advanced Hive Mind collective intelligence system for queen-led multi-agent coordination with consensus mechanisms and persistent memory
Users should expect a more consistent workflow output, faster repeated execution, and less time spent rewriting prompts from scratch.
Practical example
Example input
Use the "hive-mind-advanced" skill to help with this workflow task. Context: Advanced Hive Mind collective intelligence system for queen-led multi-agent coordination with consensus mechanisms and persistent memory
Example output
A structured workflow result with clearer steps, more consistent formatting, and an output that is easier to reuse in the next run.
When to use this skill
- Use this skill when you want a reusable workflow rather than writing the same prompt again and again.
When not to use this skill
- Do not use this when you only need a one-off answer and do not need a reusable workflow.
- Do not use it if you cannot install or maintain the related files, repository context, or supporting tools.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/hive-mind-advanced/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How hive-mind-advanced Compares
| Feature / Agent | hive-mind-advanced | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Advanced Hive Mind collective intelligence system for queen-led multi-agent coordination with consensus mechanisms and persistent memory
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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
**Collective Memory System**
- Shared knowledge base across all agents
- LRU cache with memory pressure handling
- SQLite persistence with WAL mode
- Memory consolidation and association
- Access pattern tracking and optimization
### Consensus Mechanisms
**Majority Consensus**
Simple voting where the option with most votes wins.
**Weighted Consensus**
Queen vote counts as 3x weight, providing strategic guidance.
**Byzantine Fault Tolerance**
Requires 2/3 majority for decision approval, ensuring robust consensus even with faulty agents.
## Getting Started
### 1. Initialize Hive Mind
```bash
# Basic initialization
npx claude-flow hive-mind init
# Force reinitialize
npx claude-flow hive-mind init --force
# Custom configuration
npx claude-flow hive-mind init --config hive-config.json
```
### 2. Spawn a Swarm
```bash
# Basic spawn with objective
npx claude-flow hive-mind spawn "Build microservices architecture"
# Strategic queen type
npx claude-flow hive-mind spawn "Research AI patterns" --queen-type strategic
# Tactical queen with max workers
npx claude-flow hive-mind spawn "Implement API" --queen-type tactical --max-workers 12
# Adaptive queen with consensus
npx claude-flow hive-mind spawn "Optimize system" --queen-type adaptive --consensus byzantine
# Generate Claude Code commands
npx claude-flow hive-mind spawn "Build full-stack app" --claude
```
### 3. Monitor Status
```bash
# Check hive mind status
npx claude-flow hive-mind status
# Get detailed metrics
npx claude-flow hive-mind metrics
# Monitor collective memory
npx claude-flow hive-mind memory
```
## Advanced Workflows
### Session Management
**Create and Manage Sessions**
```bash
# List active sessions
npx claude-flow hive-mind sessions
# Pause a session
npx claude-flow hive-mind pause <session-id>
# Resume a paused session
npx claude-flow hive-mind resume <session-id>
# Stop a running session
npx claude-flow hive-mind stop <session-id>
```
**Session Features**
- Automatic checkpoint creation
- Progress tracking with completion percentages
- Parent-child process management
- Session logs with event tracking
- Export/import capabilities
### Consensus Building
The Hive Mind builds consensus through structured voting:
```javascript
// Programmatic consensus building
const decision = await hiveMind.buildConsensus(
'Architecture pattern selection',
['microservices', 'monolith', 'serverless']
);
// Result includes:
// - decision: Winning option
// - confidence: Vote percentage
// - votes: Individual agent votes
```
**Consensus Algorithms**
1. **Majority** - Simple democratic voting
2. **Weighted** - Queen has 3x voting power
3. **Byzantine** - 2/3 supermajority required
### Collective Memory
**Storing Knowledge**
```javascript
// Store in collective memory
await memory.store('api-patterns', {
rest: { pros: [...], cons: [...] },
graphql: { pros: [...], cons: [...] }
}, 'knowledge', { confidence: 0.95 });
```
**Memory Types**
- `knowledge`: Permanent insights (no TTL)
- `context`: Session context (1 hour TTL)
- `task`: Task-specific data (30 min TTL)
- `result`: Execution results (permanent, compressed)
- `error`: Error logs (24 hour TTL)
- `metric`: Performance metrics (1 hour TTL)
- `consensus`: Decision records (permanent)
- `system`: System configuration (permanent)
**Searching and Retrieval**
```javascript
// Search memory by pattern
const results = await memory.search('api*', {
type: 'knowledge',
minConfidence: 0.8,
limit: 50
});
// Get related memories
const related = await memory.getRelated('api-patterns', 10);
// Build associations
await memory.associate('rest-api', 'authentication', 0.9);
```
### Task Distribution
**Automatic Worker Assignment**
The system intelligently assigns tasks based on:
- Keyword matching with agent specialization
- Historical performance metrics
- Worker availability and load
- Task complexity analysis
```javascript
// Create task (auto-assigned)
const task = await hiveMind.createTask(
'Implement user authentication',
priority: 8,
{ estimatedDuration: 30000 }
);
```
**Auto-Scaling**
```javascript
// Configure auto-scaling
const config = {
autoScale: true,
maxWorkers: 12,
scaleUpThreshold: 2, // Pending tasks per idle worker
scaleDownThreshold: 2 // Idle workers above pending tasks
};
```
## Integration Patterns
### With Claude Code
Generate Claude Code spawn commands directly:
```bash
npx claude-flow hive-mind spawn "Build REST API" --claude
```
Output:
```javascript
Task("Queen Coordinator", "Orchestrate REST API development...", "coordinator")
Task("Backend Developer", "Implement Express routes...", "backend-dev")
Task("Database Architect", "Design PostgreSQL schema...", "code-analyzer")
Task("Test Engineer", "Create Jest test suite...", "tester")
```
### With SPARC Methodology
```bash
# Use hive mind for SPARC workflow
npx claude-flow sparc tdd "User authentication" --hive-mind
# Spawns:
# - Specification agent
# - Architecture agent
# - Coder agents
# - Tester agents
# - Reviewer agents
```
### With GitHub Integration
```bash
# Repository analysis with hive mind
npx claude-flow hive-mind spawn "Analyze repo quality" --objective "owner/repo"
# PR review coordination
npx claude-flow hive-mind spawn "Review PR #123" --queen-type tactical
```
## Performance Optimization
### Memory Optimization
The collective memory system includes advanced optimizations:
**LRU Cache**
- Configurable cache size (default: 1000 entries)
- Memory pressure handling (default: 50MB)
- Automatic eviction of least-used entries
**Database Optimization**
- WAL (Write-Ahead Logging) mode
- 64MB cache size
- 256MB memory mapping
- Prepared statements for common queries
- Automatic ANALYZE and OPTIMIZE
**Object Pooling**
- Query result pooling
- Memory entry pooling
- Reduced garbage collection pressure
### Performance Metrics
```javascript
// Get performance insights
const insights = hiveMind.getPerformanceInsights();
// Includes:
// - asyncQueue utilization
// - Batch processing stats
// - Success rates
// - Average processing times
// - Memory efficiency
```
### Task Execution
**Parallel Processing**
- Batch agent spawning (5 agents per batch)
- Concurrent task orchestration
- Async operation optimization
- Non-blocking task assignment
**Benchmarks**
- 10-20x faster batch spawning
- 2.8-4.4x speed improvement overall
- 32.3% token reduction
- 84.8% SWE-Bench solve rate
## Configuration
### Hive Mind Config
```javascript
{
"objective": "Build microservices",
"name": "my-hive",
"queenType": "strategic", // strategic | tactical | adaptive
"maxWorkers": 8,
"consensusAlgorithm": "byzantine", // majority | weighted | byzantine
"autoScale": true,
"memorySize": 100, // MB
"taskTimeout": 60, // minutes
"encryption": false
}
```
### Memory Config
```javascript
{
"maxSize": 100, // MB
"compressionThreshold": 1024, // bytes
"gcInterval": 300000, // 5 minutes
"cacheSize": 1000,
"cacheMemoryMB": 50,
"enablePooling": true,
"enableAsyncOperations": true
}
```
## Hooks Integration
Hive Mind integrates with Claude Flow hooks for automation:
**Pre-Task Hooks**
- Auto-assign agents by file type
- Validate objective complexity
- Optimize topology selection
- Cache search patterns
**Post-Task Hooks**
- Auto-format deliverables
- Train neural patterns
- Update collective memory
- Analyze performance bottlenecks
**Session Hooks**
- Generate session summaries
- Persist checkpoint data
- Track comprehensive metrics
- Restore execution context
## Best Practices
### 1. Choose the Right Queen Type
**Strategic Queens** - For research, planning, and analysis
```bash
npx claude-flow hive-mind spawn "Research ML frameworks" --queen-type strategic
```
**Tactical Queens** - For implementation and execution
```bash
npx claude-flow hive-mind spawn "Build authentication" --queen-type tactical
```
**Adaptive Queens** - For optimization and dynamic tasks
```bash
npx claude-flow hive-mind spawn "Optimize performance" --queen-type adaptive
```
### 2. Leverage Consensus
Use consensus for critical decisions:
- Architecture pattern selection
- Technology stack choices
- Implementation approach
- Code review approval
- Release readiness
### 3. Utilize Collective Memory
**Store Learnings**
```javascript
// After successful pattern implementation
await memory.store('auth-pattern', {
approach: 'JWT with refresh tokens',
pros: ['Stateless', 'Scalable'],
cons: ['Token size', 'Revocation complexity'],
implementation: {...}
}, 'knowledge', { confidence: 0.95 });
```
**Build Associations**
```javascript
// Link related concepts
await memory.associate('jwt-auth', 'refresh-tokens', 0.9);
await memory.associate('jwt-auth', 'oauth2', 0.7);
```
### 4. Monitor Performance
```bash
# Regular status checks
npx claude-flow hive-mind status
# Track metrics
npx claude-flow hive-mind metrics
# Analyze memory usage
npx claude-flow hive-mind memory
```
### 5. Session Management
**Checkpoint Frequently**
```javascript
// Create checkpoints at key milestones
await sessionManager.saveCheckpoint(
sessionId,
'api-routes-complete',
{ completedRoutes: [...], remaining: [...] }
);
```
**Resume Sessions**
```bash
# Resume from any previous state
npx claude-flow hive-mind resume <session-id>
```
## Troubleshooting
### Memory Issues
**High Memory Usage**
```bash
# Run garbage collection
npx claude-flow hive-mind memory --gc
# Optimize database
npx claude-flow hive-mind memory --optimize
# Export and clear
npx claude-flow hive-mind memory --export --clear
```
**Low Cache Hit Rate**
```javascript
// Increase cache size in config
{
"cacheSize": 2000,
"cacheMemoryMB": 100
}
```
### Performance Issues
**Slow Task Assignment**
```javascript
// Enable worker type caching
// The system caches best worker matches for 5 minutes
// Automatic - no configuration needed
```
**High Queue Utilization**
```javascript
// Increase async queue concurrency
{
"asyncQueueConcurrency": 20 // Default: min(maxWorkers * 2, 20)
}
```
### Consensus Failures
**No Consensus Reached (Byzantine)**
```bash
# Switch to weighted consensus for more decisive results
npx claude-flow hive-mind spawn "..." --consensus weighted
# Or use simple majority
npx claude-flow hive-mind spawn "..." --consensus majority
```
## Advanced Topics
### Custom Worker Types
Define specialized workers in `.claude/agents/`:
```yaml
name: security-auditor
type: specialist
capabilities:
- vulnerability-scanning
- security-review
- penetration-testing
- compliance-checking
priority: high
```
### Neural Pattern Training
The system trains on successful patterns:
```javascript
// Automatic pattern learning
// Happens after successful task completion
// Stores in collective memory
// Improves future task matching
```
### Multi-Hive Coordination
Run multiple hive minds simultaneously:
```bash
# Frontend hive
npx claude-flow hive-mind spawn "Build UI" --name frontend-hive
# Backend hive
npx claude-flow hive-mind spawn "Build API" --name backend-hive
# They share collective memory for coordination
```
### Export/Import Sessions
```bash
# Export session for backup
npx claude-flow hive-mind export <session-id> --output backup.json
# Import session
npx claude-flow hive-mind import backup.json
```
## API Reference
### HiveMindCore
```javascript
const hiveMind = new HiveMindCore({
objective: 'Build system',
queenType: 'strategic',
maxWorkers: 8,
consensusAlgorithm: 'byzantine'
});
await hiveMind.initialize();
await hiveMind.spawnQueen(queenData);
await hiveMind.spawnWorkers(['coder', 'tester']);
await hiveMind.createTask('Implement feature', 7);
const decision = await hiveMind.buildConsensus('topic', options);
const status = hiveMind.getStatus();
await hiveMind.shutdown();
```
### CollectiveMemory
```javascript
const memory = new CollectiveMemory({
swarmId: 'hive-123',
maxSize: 100,
cacheSize: 1000
});
await memory.store(key, value, type, metadata);
const data = await memory.retrieve(key);
const results = await memory.search(pattern, options);
const related = await memory.getRelated(key, limit);
await memory.associate(key1, key2, strength);
const stats = memory.getStatistics();
const analytics = memory.getAnalytics();
const health = await memory.healthCheck();
```
### HiveMindSessionManager
```javascript
const sessionManager = new HiveMindSessionManager();
const sessionId = await sessionManager.createSession(
swarmId, swarmName, objective, metadata
);
await sessionManager.saveCheckpoint(sessionId, name, data);
const sessions = await sessionManager.getActiveSessions();
const session = await sessionManager.getSession(sessionId);
await sessionManager.pauseSession(sessionId);
await sessionManager.resumeSession(sessionId);
await sessionManager.stopSession(sessionId);
await sessionManager.completeSession(sessionId);
```
## Examples
### Full-Stack Development
```bash
# Initialize hive mind
npx claude-flow hive-mind init
# Spawn full-stack hive
npx claude-flow hive-mind spawn "Build e-commerce platform" \
--queen-type strategic \
--max-workers 10 \
--consensus weighted \
--claude
# Output generates Claude Code commands:
# - Queen coordinator
# - Frontend developers (React)
# - Backend developers (Node.js)
# - Database architects
# - DevOps engineers
# - Security auditors
# - Test engineers
# - Documentation specialists
```
### Research and Analysis
```bash
# Spawn research hive
npx claude-flow hive-mind spawn "Research GraphQL vs REST" \
--queen-type adaptive \
--consensus byzantine
# Researchers gather data
# Analysts process findings
# Queen builds consensus on recommendation
# Results stored in collective memory
```
### Code Review
```bash
# Review coordination
npx claude-flow hive-mind spawn "Review PR #456" \
--queen-type tactical \
--max-workers 6
# Spawns:
# - Code analyzers
# - Security reviewers
# - Performance reviewers
# - Test coverage analyzers
# - Documentation reviewers
# - Consensus on approval/changes
```
## Skill Progression
### Beginner
1. Initialize hive mind
2. Spawn basic swarms
3. Monitor status
4. Use majority consensus
### Intermediate
1. Configure queen types
2. Implement session management
3. Use weighted consensus
4. Access collective memory
5. Enable auto-scaling
### Advanced
1. Byzantine fault tolerance
2. Memory optimization
3. Custom worker types
4. Multi-hive coordination
5. Neural pattern training
6. Session export/import
7. Performance tuning
## Related Skills
- `swarm-orchestration`: Basic swarm coordination
- `consensus-mechanisms`: Distributed decision making
- `memory-systems`: Advanced memory management
- `sparc-methodology`: Structured development workflow
- `github-integration`: Repository coordination
## References
- [Hive Mind Documentation](https://github.com/ruvnet/claude-flow/docs/hive-mind)
- [Collective Intelligence Patterns](https://github.com/ruvnet/claude-flow/docs/patterns)
- [Byzantine Consensus](https://github.com/ruvnet/claude-flow/docs/consensus)
- [Memory Optimization](https://github.com/ruvnet/claude-flow/docs/memory)
---
**Skill Version**: 1.0.0
**Last Updated**: 2025-10-19
**Maintained By**: Claude Flow Team
**License**: MITRelated Skills
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