Orchestration Skill Guidelines

When to Use This Skill

• Parallel multi-agent execution requiring concurrent task processing
• Complex implementation with 6+ independent tasks
• Theater-free development requiring 0% tolerance validation
• Dynamic agent selection from 86+ agent registry
• High-quality delivery needing Byzantine consensus validation

When NOT to Use This Skill

• Single-agent tasks with no parallelization benefit
• Simple sequential work completing in <2 hours
• Planning phase (use research-driven-planning first)
• Trivial changes to single files

Success Criteria

• Agent+skill matrix generated with optimal assignments
• Parallel execution successful with 8.3x speedup achieved
• Theater detection passes with 0% theater detected
• Integration tests pass at 100% rate
• All agents complete with no orphaned workers

Edge Cases to Handle

• Agent failures - Implement agent health monitoring and replacement
• Task timeout - Configure per-task timeout with escalation
• Consensus failure - Have fallback from Byzantine to weighted consensus
• Resource exhaustion - Limit max parallel agents, queue excess
• Conflicting outputs - Implement merge conflict resolution strategy

Guardrails (NEVER Violate)

• NEVER lose agent state - Persist agent progress to memory continuously
• ALWAYS track swarm health - Monitor all agent statuses in real-time
• ALWAYS validate consensus - Require 4/5 agreement for theater detection
• NEVER skip theater audit - Zero tolerance, any theater blocks merge
• ALWAYS cleanup workers - Terminate agents on completion/failure

Evidence-Based Validation

• Check all agent statuses - Verify each agent completed successfully
• Validate parallel execution - Confirm tasks ran concurrently, not sequentially
• Measure speedup - Calculate actual speedup vs sequential baseline
• Audit theater detection - Run 6-agent consensus, verify 0% detection
• Verify integration - Execute sandbox tests, confirm 100% pass rate

Swarm Orchestration SOP

Overview

This skill implements complex multi-agent swarm orchestration with intelligent task decomposition, distributed execution, progress monitoring, and result synthesis. It enables coordinated execution of complex workflows across multiple specialized agents.

Agents & Responsibilities

task-orchestrator

Role: Central orchestration and task decomposition Responsibilities:

• Decompose complex tasks into subtasks
• Assign tasks to appropriate agents
• Monitor execution progress
• Synthesize results from multiple agents

hierarchical-coordinator

Role: Hierarchical task delegation and coordination Responsibilities:

• Manage task hierarchy
• Coordinate parent-child task relationships
• Handle task dependencies
• Ensure proper execution order

adaptive-coordinator

Role: Dynamic workload balancing and optimization Responsibilities:

• Monitor agent workloads
• Rebalance task assignments
• Optimize resource allocation
• Adapt to changing conditions

Phase 1: Plan Orchestration

Objective

Analyze complex task requirements and create detailed decomposition plan with dependency mapping.

Evidence-Based Validation

• Task decomposition tree created
• Dependencies mapped
• Agent assignments planned
• Execution strategy defined

Scripts

# Analyze task complexity
npx claude-flow@alpha task analyze --task "Build full-stack application" --output task-analysis.json

# Generate decomposition tree
npx claude-flow@alpha task decompose \
  --task "Build full-stack application" \
  --max-depth 3 \
  --output decomposition.json

# Visualize decomposition
npx claude-flow@alpha task visualize --input decomposition.json --output task-tree.png

# Store decomposition in memory
npx claude-flow@alpha memory store \
  --key "orchestration/decomposition" \
  --file decomposition.json

# Identify dependencies
npx claude-flow@alpha task dependencies \
  --input decomposition.json \
  --output dependencies.json

# Plan agent assignments
npx claude-flow@alpha task plan \
  --decomposition decomposition.json \
  --available-agents 12 \
  --output execution-plan.json

Task Decomposition Strategy

Level 1: High-Level Goals

{
  "task": "Build full-stack application",
  "subtasks": [
    "Design architecture",
    "Implement backend",
    "Implement frontend",
    "Setup infrastructure",
    "Testing and QA"
  ]
}

Level 2: Component Tasks

{
  "task": "Implement backend",
  "subtasks": [
    "Design API endpoints",
    "Implement authentication",
    "Setup database",
    "Create business logic",
    "API documentation"
  ]
}

Level 3: Atomic Tasks

{
  "task": "Implement authentication",
  "subtasks": [
    "Setup JWT library",
    "Create user model",
    "Implement login endpoint",
    "Implement registration endpoint",
    "Add password hashing",
    "Create auth middleware"
  ]
}

Memory Patterns

# Store orchestration plan
npx claude-flow@alpha memory store \
  --key "orchestration/plan" \
  --value '{
    "totalTasks": 45,
    "levels": 3,
    "estimatedDuration": "2h 30m",
    "requiredAgents": 12
  }'

# Store dependency graph
npx claude-flow@alpha memory store \
  --key "orchestration/dependencies" \
  --value '{
    "task-003": ["task-001", "task-002"],
    "task-008": ["task-003", "task-004"],
    "task-012": ["task-008", "task-009"]
  }'

Validation Criteria

1. Task tree depth ≤ 3 levels
2. All tasks have clear success criteria
3. Dependencies correctly identified
4. No circular dependencies
5. Agent capacity sufficient for load

Phase 2: Initialize Swarm

Objective

Setup swarm infrastructure with appropriate topology and coordinator agents.

Evidence-Based Validation

• Swarm initialized successfully
• Topology optimized for workload
• Coordinator agents active
• Memory coordination established

Scripts

# Determine optimal topology
TASK_COUNT=$(jq '.totalTasks' decomposition.json)

if [ "$TASK_COUNT" -gt 30 ]; then
  TOPOLOGY="mesh"
elif [ "$TASK_COUNT" -gt 15 ]; then
  TOPOLOGY="hierarchical"
else
  TOPOLOGY="star"
fi

# Initialize swarm with optimal topology
npx claude-flow@alpha swarm init \
  --topology $TOPOLOGY \
  --max-agents 15 \
  --strategy adaptive

# Spawn task orchestrator
npx claude-flow@alpha agent spawn \
  --type coordinator \
  --role "task-orchestrator" \
  --capabilities "task-decomposition,assignment,synthesis"

# Spawn hierarchical coordinator
npx claude-flow@alpha agent spawn \
  --type coordinator \
  --role "hierarchical-coordinator" \
  --capabilities "hierarchy-management,delegation"

# Spawn adaptive coordinator
npx claude-flow@alpha agent spawn \
  --type coordinator \
  --role "adaptive-coordinator" \
  --capabilities "workload-balancing,optimization"

# Verify swarm status
npx claude-flow@alpha swarm status --show-agents --show-topology

MCP Integration

// Initialize swarm
mcp__claude-flow__swarm_init({
  topology: "hierarchical",
  maxAgents: 15,
  strategy: "adaptive"
})

// Spawn coordinators
mcp__claude-flow__agent_spawn({
  type: "coordinator",
  name: "task-orchestrator",
  capabilities: ["task-decomposition", "assignment", "synthesis"]
})

mcp__claude-flow__agent_spawn({
  type: "coordinator",
  name: "hierarchical-coordinator",
  capabilities: ["hierarchy-management", "delegation"]
})

mcp__claude-flow__agent_spawn({
  type: "coordinator",
  name: "adaptive-coordinator",
  capabilities: ["workload-balancing", "optimization"]
})

Memory Patterns

# Store swarm configuration
npx claude-flow@alpha memory store \
  --key "orchestration/swarm" \
  --value '{
    "swarmId": "swarm-12345",
    "topology": "hierarchical",
    "maxAgents": 15,
    "coordinators": ["task-orchestrator", "hierarchical-coordinator", "adaptive-coordinator"]
  }'

Validation Criteria

1. Swarm operational
2. All coordinators active
3. Topology matches requirements
4. Memory coordination functional
5. Health checks passing

Phase 3: Orchestrate Execution

Objective

Coordinate distributed task execution across swarm agents with proper dependency handling.

Evidence-Based Validation

• All tasks assigned to agents
• Dependencies respected
• Execution in progress
• Progress tracked continuously

Scripts

# Spawn specialized agents based on task requirements
npx claude-flow@alpha agent spawn --type researcher --count 2
npx claude-flow@alpha agent spawn --type coder --count 5
npx claude-flow@alpha agent spawn --type reviewer --count 2
npx claude-flow@alpha agent spawn --type tester --count 2

# Orchestrate task execution
npx claude-flow@alpha task orchestrate \
  --plan execution-plan.json \
  --strategy adaptive \
  --max-agents 12 \
  --priority high

# Alternative: Orchestrate with MCP
# mcp__claude-flow__task_orchestrate({
#   task: "Execute full-stack application build",
#   strategy: "adaptive",
#   maxAgents: 12,
#   priority: "high"
# })

# Monitor orchestration status
npx claude-flow@alpha task status --detailed --json > task-status.json

# Track individual task progress
npx claude-flow@alpha task list --filter "in_progress" --show-timing

# Monitor agent workloads
npx claude-flow@alpha agent metrics --metric tasks --format table

Task Assignment Algorithm

#!/bin/bash
# assign-tasks.sh

# Read decomposition
TASKS=$(jq -r '.tasks[] | @json' decomposition.json)

for TASK in $TASKS; do
  TASK_ID=$(echo $TASK | jq -r '.id')
  TASK_TYPE=$(echo $TASK | jq -r '.type')
  DEPENDENCIES=$(echo $TASK | jq -r '.dependencies[]')

  # Check if dependencies completed
  DEPS_COMPLETE=true
  for DEP in $DEPENDENCIES; do
    DEP_STATUS=$(npx claude-flow@alpha task status --task-id $DEP --format json | jq -r '.status')
    if [ "$DEP_STATUS" != "completed" ]; then
      DEPS_COMPLETE=false
      break
    fi
  done

  # Assign task if dependencies complete
  if [ "$DEPS_COMPLETE" = true ]; then
    # Find least loaded agent of required type
    AGENT_ID=$(npx claude-flow@alpha agent list \
      --filter "type=$TASK_TYPE" \
      --sort-by load \
      --format json | jq -r '.[0].id')

    # Assign task
    npx claude-flow@alpha task assign \
      --task-id $TASK_ID \
      --agent-id $AGENT_ID

    echo "Assigned task $TASK_ID to agent $AGENT_ID"
  fi
done

Memory Patterns

# Store task assignments
npx claude-flow@alpha memory store \
  --key "orchestration/assignments" \
  --value '{
    "task-001": {"agent": "agent-researcher-1", "status": "in_progress", "started": "2025-10-30T10:00:00Z"},
    "task-002": {"agent": "agent-coder-1", "status": "in_progress", "started": "2025-10-30T10:01:00Z"}
  }'

# Store execution timeline
npx claude-flow@alpha memory store \
  --key "orchestration/timeline" \
  --value '{
    "started": "2025-10-30T10:00:00Z",
    "tasksCompleted": 12,
    "tasksInProgress": 8,
    "tasksPending": 25
  }'

Validation Criteria

1. All agents assigned tasks
2. No dependency violations
3. Task execution progressing
4. No agent overload
5. Error handling active

Phase 4: Monitor Progress

Objective

Track execution progress, identify blockers, and maintain real-time visibility.

Evidence-Based Validation

• Progress metrics collected
• Blockers identified quickly
• Agents responding properly
• Timeline on track

Scripts

# Start continuous monitoring
npx claude-flow@alpha swarm monitor \
  --interval 10 \
  --duration 3600 \
  --output orchestration-monitor.log &

# Track task completion rate
while true; do
  COMPLETED=$(npx claude-flow@alpha task list --filter "completed" | wc -l)
  TOTAL=$(npx claude-flow@alpha task list | wc -l)
  PROGRESS=$((COMPLETED * 100 / TOTAL))

  echo "Progress: $PROGRESS% ($COMPLETED/$TOTAL tasks)"

  npx claude-flow@alpha memory store \
    --key "orchestration/progress" \
    --value "{\"completed\": $COMPLETED, \"total\": $TOTAL, \"percentage\": $PROGRESS}"

  sleep 30
done &

# Monitor for blocked tasks
npx claude-flow@alpha task detect-blocked \
  --threshold 300 \
  --notify-on-block

# Monitor agent health
npx claude-flow@alpha agent health-check --all --interval 60

# Generate progress report
npx claude-flow@alpha orchestration report \
  --include-timeline \
  --include-agent-metrics \
  --output progress-report.md

Progress Visualization

# Generate Gantt chart
npx claude-flow@alpha task gantt \
  --input task-status.json \
  --output gantt-chart.png

# Generate network diagram
npx claude-flow@alpha task network \
  --show-dependencies \
  --show-progress \
  --output network-diagram.png

Memory Patterns

# Store progress snapshots
npx claude-flow@alpha memory store \
  --key "orchestration/snapshot-$(date +%s)" \
  --value '{
    "timestamp": "'$(date -Iseconds)'",
    "completed": 18,
    "inProgress": 12,
    "pending": 15,
    "blocked": 0,
    "failed": 0
  }'

# Store blocker information
npx claude-flow@alpha memory store \
  --key "orchestration/blockers" \
  --value '{
    "task-015": {"reason": "dependency-failed", "since": "2025-10-30T10:15:00Z"},
    "task-022": {"reason": "agent-unresponsive", "since": "2025-10-30T10:20:00Z"}
  }'

Validation Criteria

1. Progress tracking accurate
2. Blockers detected within 5 minutes
3. No stalled tasks unnoticed
4. Agent failures handled
5. Progress reports generated

Phase 5: Synthesize Results

Objective

Aggregate and synthesize results from all completed tasks into coherent outputs.

Evidence-Based Validation

• All task results collected
• Results synthesized successfully
• Output validated
• Final report generated

Scripts

# Collect all task results
npx claude-flow@alpha task results --all --format json > all-results.json

# Synthesize results by category
npx claude-flow@alpha task synthesize \
  --input all-results.json \
  --group-by category \
  --output synthesized-results.json

# Generate final outputs
npx claude-flow@alpha orchestration finalize \
  --results synthesized-results.json \
  --output final-output/

# Validate outputs
npx claude-flow@alpha orchestration validate \
  --output final-output/ \
  --criteria validation-criteria.json

# Generate final report
npx claude-flow@alpha orchestration report \
  --type final \
  --include-metrics \
  --include-timeline \
  --include-outputs \
  --output final-orchestration-report.md

# Archive orchestration data
npx claude-flow@alpha orchestration archive \
  --output orchestration-archive-$(date +%Y%m%d-%H%M%S).tar.gz

MCP Integration

// Get task results
mcp__claude-flow__task_results({
  taskId: "all",
  format: "detailed"
})

// Check final status
mcp__claude-flow__task_status({
  detailed: true
})

Result Synthesis Strategy

1. Collect Results:

# Get results from each agent type
RESEARCHER_RESULTS=$(npx claude-flow@alpha task results --agent-type researcher --format json)
CODER_RESULTS=$(npx claude-flow@alpha task results --agent-type coder --format json)
REVIEWER_RESULTS=$(npx claude-flow@alpha task results --agent-type reviewer --format json)

2. Aggregate by Phase:

# Architecture phase results
ARCHITECTURE=$(jq '[.[] | select(.phase=="architecture")]' all-results.json)

# Implementation phase results
IMPLEMENTATION=$(jq '[.[] | select(.phase=="implementation")]' all-results.json)

# Testing phase results
TESTING=$(jq '[.[] | select(.phase=="testing")]' all-results.json)

3. Synthesize Final Output:

# Combine all results
jq -s '{
  architecture: .[0],
  implementation: .[1],
  testing: .[2],
  metadata: {
    totalTasks: (.[0] + .[1] + .[2] | length),
    duration: "'$(date -Iseconds)'",
    successRate: 0.98
  }
}' \
  <(echo "$ARCHITECTURE") \
  <(echo "$IMPLEMENTATION") \
  <(echo "$TESTING") \
  > final-synthesis.json

Memory Patterns

# Store final results
npx claude-flow@alpha memory store \
  --key "orchestration/results/final" \
  --file final-synthesis.json

# Store performance metrics
npx claude-flow@alpha memory store \
  --key "orchestration/metrics/final" \
  --value '{
    "totalTasks": 45,
    "completed": 44,
    "failed": 1,
    "duration": "2h 18m",
    "avgTaskTime": "3m 5s",
    "throughput": "0.32 tasks/min"
  }'

Validation Criteria

1. All task results accounted for
2. Synthesis logic correct
3. Outputs validated successfully
4. No data loss
5. Final report comprehensive

Success Criteria

Overall Validation

• Task decomposition accurate
• Swarm orchestration successful
• All tasks completed (≥95%)
• Results synthesized correctly
• Performance targets met

Performance Targets

• Task success rate: ≥95%
• Average task completion time: Within estimates ±20%
• Agent utilization: 70-90%
• Coordination overhead: <15%
• Result synthesis time: <5 minutes

Common Issues & Solutions

Issue: Task Dependencies Not Resolved

Symptoms: Tasks blocked waiting for dependencies Solution: Verify dependency graph, check for circular dependencies

Issue: Agent Overload

Symptoms: Some agents at 100% utilization, others idle Solution: Rebalance task assignments, spawn additional agents

Issue: Task Execution Stalled

Symptoms: Tasks remain in-progress indefinitely Solution: Implement timeout mechanism, restart stuck agents

Issue: Result Synthesis Incomplete

Symptoms: Missing results in final output Solution: Verify all tasks completed, check result collection logic

Best Practices

1. Clear Decomposition: Break tasks into atomic units
2. Explicit Dependencies: Document all task dependencies
3. Progress Tracking: Monitor continuously
4. Error Handling: Implement retry logic
5. Result Validation: Verify outputs at each phase
6. Memory Coordination: Use shared memory for state
7. Agent Specialization: Assign tasks to appropriate agents
8. Performance Monitoring: Track metrics throughout

Integration Points

With Other Skills

• advanced-swarm: For topology optimization
• performance-analysis: For bottleneck detection
• cascade-orchestrator: For workflow chaining
• hive-mind: For collective decision-making

With External Systems

• CI/CD pipelines for automated execution
• Project management tools for tracking
• Monitoring systems for observability
• Storage systems for result archival

Next Steps

After completing this skill:

1. Analyze orchestration metrics
2. Optimize task decomposition strategy
3. Experiment with different topologies
4. Implement custom synthesis logic
5. Create reusable orchestration templates

References

• Claude Flow Documentation
• Task Decomposition Patterns
• Multi-Agent Orchestration Theory
• Distributed Systems Coordination