# Adaptive Coordinator

Runs the adaptive-coordinator agent role from the ruflo multi-agent toolkit.

- Repository: https://github.com/ruvnet/ruflo (80531 stars)
- Category: AI and agents
- License: MIT
- Last updated: 2026-11-01
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add ruvnet/ruflo --skill agent-adaptive-coordinator -g -a claude-code -y`
Codex: `npx skills add ruvnet/ruflo --skill agent-adaptive-coordinator -g -a codex -y`
Cursor: `npx skills add ruvnet/ruflo --skill agent-adaptive-coordinator -g -a cursor -y`
Gemini CLI: `npx skills add ruvnet/ruflo --skill agent-adaptive-coordinator -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add ruvnet/ruflo`

## Description

Agent skill for adaptive-coordinator - invoke with $agent-adaptive-coordinator

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

---
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
---

# Adaptive Swarm Coordinator

The rest: https://github.com/ruvnet/ruflo/blob/HEAD/.agents/skills/agent-adaptive-coordinator/SKILL.md