Adaptive Coordinator

name: adaptive-coordinator type: coordinator color: "#9C27B0"
description: Dynamic topology switching coordinator with self-organizing swarm patterns and real-time optimization capabilities:

• topology_adaptation
• performance_optimization
• real_time_reconfiguration
• pattern_recognition
• predictive_scaling
• intelligent_routing priority: critical hooks: pre: | echo "🔄 Adaptive Coordinator analyzing workload patterns: $TASK"

  Initialize with auto-detection

  mcp__claude-flow__swarm_init auto --maxAgents=15 --strategy=adaptive

  Analyze current workload patterns

  mcp__claude-flow__neural_patterns analyze --operation="workload_analysis" --metadata="{"task":"$TASK"}"

  Train adaptive models

  mcp__claude-flow__neural_train coordination --training_data="historical_swarm_data" --epochs=30

  Store baseline metrics

  mcp__claude-flow__memory_usage store "adaptive:baseline:${TASK_ID}" "$(mcp__claude-flow__performance_report --format=json)" --namespace=adaptive

  Set up real-time monitoring

  mcp__claude-flow__swarm_monitor --interval=2000 --swarmId="${SWARM_ID}" post: | echo "✨ Adaptive coordination complete - topology optimized"

  Generate comprehensive analysis

  mcp__claude-flow__performance_report --format=detailed --timeframe=24h

  Store learning outcomes

  mcp__claude-flow__neural_patterns learn --operation="coordination_complete" --outcome="success" --metadata="{"final_topology":"$(mcp__claude-flow__swarm_status | jq -r '.topology')"}"

  Export learned patterns

  mcp__claude-flow__model_save "adaptive-coordinator-${TASK_ID}" "$tmp$adaptive-model-$(date +%s).json"

  Update persistent knowledge base

  mcp__claude-flow__memory_usage store "adaptive:learned:${TASK_ID}" "$(date): Adaptive patterns learned and saved" --namespace=adaptive