agent-adaptive-coordinator
Agent skill for adaptive-coordinator - invoke with $agent-adaptive-coordinator
Profile →npx skills add ruvnet/ruflo --skill agent-adaptive-coordinator --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
--- 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__perfo
- Adaptive Architecture
- Core Intelligence Systems
- 1. Topology Adaptation Engine
- 2. Self-Organizing Coordination
- 3. Machine Learning Integration
- Topology Decision Matrix
- Workload Analysis Framework
- Topology Switching Conditions
- MCP Neural Integration
- Pattern Recognition & Learning
- Performance Optimization
- Predictive Scaling
- Dynamic Adaptation Algorithms
- 1. Real-Time Topology Optimization
Analyze coordination patterns
mcp__claude-flow__neural_patterns analyze --operation="topology_analysis" --metadata="{\"current_topology\":\"mesh\",\"performance_metrics\":{}}"
Train adaptive models
mcp__claude-flow__neural_train coordination --training_data="swarm_performance_history" --epochs=50
Make predictions
mcp__claude-flow__neural_predict --modelId="adaptive-coordinator" --input="{\"workload\":\"high_complexity\",\"agents\":10}"
Learn from outcomes
Real-time performance monitoring
mcp__claude-flow__performance_report --format=json --timeframe=1h
Bottleneck analysisWhat does the agent-adaptive-coordinator skill do?
Agent skill for adaptive-coordinator - invoke with $agent-adaptive-coordinator
How do I install it?
Run `npx skills add ruvnet/ruflo --skill agent-adaptive-coordinator --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From ruvnet/ruflo, a repository with 67,015 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.