/hive-mind-advanced
Advanced Hive Mind collective intelligence system for queen-led multi-agent coordination with consensus mechanisms and persistent memory
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/hive-mind-advanced
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Advanced Hive Mind collective intelligence system for queen-led multi-agent coordination with consensus mechanisms and persistent memory
SKILL.md
hive-mind-advanced.SKILL.mdname: hive-mind-advanced
description: |
Advanced Hive Mind collective intelligence system for queen-led multi-agent coordination with consensus mechanisms and persistent memory
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 ArRead more
name: hive-mind-advanced description: | Advanced Hive Mind collective intelligence system for queen-led multi-agent coordination with consensus mechanisms and persistent memory
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 ArAn agent meta-harness for Claude Code and Codex. Agent = Model + Harness. The model writes; the harness gives it tools, memory, loops, sandboxes, and controls so it can actually work.
Repo: ruvnet/ruflo
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