Orchestration Skill Guidelines

When to Use This Skill

• Queen-led coordination requiring hierarchical multi-agent control
• Consensus-driven decisions needing Byzantine fault tolerance
• Collective intelligence tasks benefiting from shared memory
• Strategic planning with tactical execution delegation
• Large-scale swarms with 10+ specialized worker agents

When NOT to Use This Skill

• Single-agent tasks with no coordination requirements
• Simple workflows without consensus needs
• Flat topologies where hierarchy adds no value
• Ephemeral tasks not needing collective memory

Success Criteria

• Queen successfully coordinates all worker agents
• Consensus achieved using configured algorithm (majority/weighted/Byzantine)
• Collective memory shared across all agents with <10ms access time
• All workers complete tasks with 100% assignment success
• Session state persisted with checkpoint recovery capability

Edge Cases to Handle

• Queen failure - Implement queen failover and re-election
• Worker unresponsiveness - Timeout detection and task reassignment
• Consensus deadlock - Fallback to weighted or majority consensus
• Memory corruption - Validate memory integrity with checksums
• Session crash - Resume from last checkpoint with full state recovery

Guardrails (NEVER Violate)

• NEVER lose collective memory - Persist to SQLite with WAL mode
• ALWAYS validate queen health - Monitor queen heartbeat continuously
• ALWAYS track worker states - Real-time worker status in shared memory
• NEVER skip consensus - Critical decisions require configured consensus
• ALWAYS checkpoint sessions - Save state at key milestones

Evidence-Based Validation

• Verify queen coordination - Check queen issued commands to all workers
• Validate consensus results - Confirm vote counts meet algorithm threshold
• Check memory consistency - Query collective memory, verify no conflicts
• Measure worker efficiency - Calculate task completion rate per worker
• Audit session recovery - Test checkpoint restore, verify full state

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

# 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

# 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

# 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

# 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:

// 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

// 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

// 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

// Create task (auto-assigned)
const task = await hiveMind.createTask(
  'Implement user authentication',
  priority: 8,
  { estimatedDuration: 30000 }
);

Auto-Scaling

// 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:

npx claude-flow hive-mind spawn "Build REST API" --claude

Output:

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

# 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

# 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

// 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

{
  "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

{
  "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

npx claude-flow hive-mind spawn "Research ML frameworks" --queen-type strategic

Tactical Queens - For implementation and execution

npx claude-flow hive-mind spawn "Build authentication" --queen-type tactical

Adaptive Queens - For optimization and dynamic tasks

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

// 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

// Link related concepts
await memory.associate('jwt-auth', 'refresh-tokens', 0.9);
await memory.associate('jwt-auth', 'oauth2', 0.7);

4. Monitor Performance

# 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

// Create checkpoints at key milestones
await sessionManager.saveCheckpoint(
  sessionId,
  'api-routes-complete',
  { completedRoutes: [...], remaining: [...] }
);

Resume Sessions

# Resume from any previous state
npx claude-flow hive-mind resume <session-id>

Troubleshooting

Memory Issues

High Memory Usage

# 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

// Increase cache size in config
{
  "cacheSize": 2000,
  "cacheMemoryMB": 100
}

Performance Issues

Slow Task Assignment

// Enable worker type caching
// The system caches best worker matches for 5 minutes
// Automatic - no configuration needed

High Queue Utilization

// Increase async queue concurrency
{
  "asyncQueueConcurrency": 20 // Default: min(maxWorkers * 2, 20)
}

Consensus Failures

No Consensus Reached (Byzantine)

# 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 agents/:

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:

// Automatic pattern learning
// Happens after successful task completion
// Stores in collective memory
// Improves future task matching

Multi-Hive Coordination

Run multiple hive minds simultaneously:

# 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

# 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

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

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

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

# 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

# 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

# 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
• Collective Intelligence Patterns
• Byzantine Consensus
• Memory Optimization

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Skill Version: 1.0.0 Last Updated: 2025-10-19 Maintained By: Claude Flow Team License: MIT