multi-agent-coordinator by 404kidwiz/claude-supercode-skills
npx skills add https://github.com/404kidwiz/claude-supercode-skills --skill multi-agent-coordinator为管理分布式系统中的复杂智能体协调提供先进的多智能体编排专业知识。专精于企业级多智能体环境中的分层控制、动态扩展、智能资源分配和复杂的冲突解决。
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coordination_hierarchy:
executive_level:
- strategy_coordinator: overall system objectives
- resource_manager: global resource allocation
- performance_monitor: system-wide optimization
- security_coordinator: enterprise security policies
operational_level:
- domain_coordinators: business domain management
- regional_managers: geographic coordination
- workflow_orchestrators: process management
- quality_managers: service level enforcement
tactical_level:
- team_leaders: agent group coordination
- task_supervisors: specific task oversight
- load_balancers: real-time workload distribution
- conflict_resolvers: operational dispute handling
agent_level:
- specialized_agents: domain-specific expertise
- generalist_agents: flexible task handling
- monitoring_agents: system health and performance
- backup_agents: redundancy and failover
class MultiAgentCoordinator:
def __init__(self):
self.hierarchy_manager = HierarchyManager()
self.topology_optimizer = TopologyOptimizer()
self.resource_allocator = ResourceAllocator()
self.scaling_engine = ScalingEngine()
async def orchestrate_massive_workload(self, workload_profile):
# Analyze workload characteristics
workload_analysis = await self.analyze_workload(workload_profile)
# Determine optimal topology
optimal_topology = await self.topology_optimizer.design(workload_analysis)
# Configure hierarchical coordination
hierarchy_config = await self.hierarchy_manager.configure(optimal_topology)
# Allocate resources globally
resource_allocation = await self.resource_allocator.distribute(
workload_analysis, hierarchy_config
)
# Scale agent deployment
scaling_plan = await self.scaling_engine.execute(resource_allocation)
return {
"hierarchy": hierarchy_config,
"topology": optimal_topology,
"resources": resource_allocation,
"scaling": scaling_plan,
"expected_performance": self.predict_performance(scaling_plan)
}
load_balancing_strategies:
geographic_distribution:
- latency_optimization: minimize response times
- compliance_boundaries: respect data sovereignty
- failover_regions: backup coordination centers
- cost_optimization: leverage regional pricing differences
skill_based_assignment:
- expertise_matching: optimal task-agent pairing
- capability_scaling: dynamic skill development
- specialization_index: measure agent specialization
- cross_training: flexible agent capabilities
performance_optimization:
- throughput_maximization: process as many tasks as possible
- latency_minimization: reduce response times
- quality_optimization: balance speed with accuracy
- cost_efficiency: minimize operational expenses
class PredictiveScalingEngine:
def __init__(self):
self.demand_predictor = DemandPredictionModel()
self.capacity_planner = CapacityPlanningModel()
self.cost_optimizer = CostOptimizationModel()
async def scale_system(self, forecast_horizon=24):
# Predict future demand
demand_forecast = await self.demand_predictor.predict(forecast_horizon)
# Plan capacity requirements
capacity_plan = await self.capacity_planner.optimize(demand_forecast)
# Optimize for cost and performance
scaling_plan = await self.cost_optimizer.balance(capacity_plan)
# Execute scaling operations
scaling_results = await self.execute_scaling(scaling_plan)
return {
"forecast": demand_forecast,
"capacity_plan": capacity_plan,
"scaling_plan": scaling_plan,
"execution_results": scaling_results,
"cost_impact": self.calculate_cost_impact(scaling_results)
}
conflict_types:
resource_conflicts:
- priority_based_resolution: urgent tasks first
- fair_scheduling: equitable resource sharing
- negotiation_protocols: agent-to-agent bargaining
- escalation_procedures: human intervention for disputes
priority_conflicts:
- business_impact_assessment: evaluate organizational impact
- sla_prioritization: service level agreement enforcement
- stakeholder_consensus: collaborative decision making
- executive_override: emergency priority assignment
capability_conflicts:
- skill_development: train agents for missing capabilities
- collaboration_models: multi-agent cooperation for complex tasks
- external_sourcing: third-party service integration
- task_decomposition: break down complex tasks into simpler ones
class MultiTenantCoordinator:
def __init__(self):
self.tenant_manager = TenantManager()
self.isolation_manager = IsolationManager()
self.resource_pool = ResourcePool()
async def coordinate_tenant_workload(self, tenant_id, workload):
# Verify tenant permissions and quotas
tenant_info = await self.tenant_manager.get_info(tenant_id)
# Ensure proper isolation from other tenants
isolated_context = await self.isolation_manager.create_context(tenant_info)
# Allocate dedicated resources
allocated_resources = await self.resource_pool.allocate(
tenant_info.resource_quota, isolated_context
)
# Execute tenant-specific coordination
coordination_result = await self.execute_coordination(
workload, allocated_resources, isolated_context
)
# Monitor for cross-tenant interference
await self.isolation_manager.verify_isolation(coordination_result)
return coordination_result
performance_kpis:
operational_metrics:
- agent_utilization_rate
- task_completion_throughput
- average_response_time
- system_availability_percentage
business_metrics:
- cost_per_transaction
- customer_satisfaction_score
- service_level_agreement_compliance
- revenue_impact_assessment
scalability_metrics:
- horizontal_scaling_efficiency
- vertical_scaling_limits
- network_latency_distribution
- resource_waste_percentage
resilience_strategies:
geographic_redundancy:
- multi_region_deployment: distribute across geographic areas
- active_active_configuration: all regions handle production traffic
- automated_failover: seamless transition during outages
- data_replication: synchronous and asynchronous replication
system_resilience:
- circuit_breaker_patterns: prevent cascading failures
- bulkhead_isolation: isolate failure domains
- graceful_degradation: maintain partial functionality
- self_healing_capabilities: automatic recovery procedures
场景: 协调全球市场中 500 多个交易智能体,要求毫秒级延迟。
架构实现:
协调流程:
Global Trading Floor → Regional Trading Centers →
Specialized Trading Teams → Algorithmic Trading Agents →
Market Data Analyzers → Risk Management Agents → Compliance Monitors
关键组件:
结果:
场景: 协调多医院网络中的 1000 多个临床智能体。
协调设计:
网络结构:
Hospital Network → Regional Medical Centers →
Specialty Departments → Medical Teams → Clinical Agents →
Diagnostic Systems → Treatment Coordinators → Patient Care Managers
实现:
结果:
场景: 协调城市服务中的 10,000 多个物联网智能体和人工操作员。
系统架构:
协调框架:
City Operations Center → District Management Offices →
Service Departments → Field Operations Teams → IoT Sensor Networks →
Traffic Management → Public Safety → Utilities Coordination → Emergency Services
关键特性:
结果:
多智能体协调器通过智能分层协调、自适应资源管理和复杂的冲突解决,实现了数百个智能体的企业级编排,确保在复杂分布式环境中的最优性能和可靠性。
每周安装次数
82
代码仓库
GitHub 星标数
45
首次出现
2026 年 1 月 24 日
安全审计
安装于
opencode67
gemini-cli64
codex64
cursor59
github-copilot56
claude-code53
Provides advanced multi-agent orchestration expertise for managing complex coordination of agents across distributed systems. Specializes in hierarchical control, dynamic scaling, intelligent resource allocation, and sophisticated conflict resolution for enterprise-level multi-agent environments.
coordination_hierarchy:
executive_level:
- strategy_coordinator: overall system objectives
- resource_manager: global resource allocation
- performance_monitor: system-wide optimization
- security_coordinator: enterprise security policies
operational_level:
- domain_coordinators: business domain management
- regional_managers: geographic coordination
- workflow_orchestrators: process management
- quality_managers: service level enforcement
tactical_level:
- team_leaders: agent group coordination
- task_supervisors: specific task oversight
- load_balancers: real-time workload distribution
- conflict_resolvers: operational dispute handling
agent_level:
- specialized_agents: domain-specific expertise
- generalist_agents: flexible task handling
- monitoring_agents: system health and performance
- backup_agents: redundancy and failover
class MultiAgentCoordinator:
def __init__(self):
self.hierarchy_manager = HierarchyManager()
self.topology_optimizer = TopologyOptimizer()
self.resource_allocator = ResourceAllocator()
self.scaling_engine = ScalingEngine()
async def orchestrate_massive_workload(self, workload_profile):
# Analyze workload characteristics
workload_analysis = await self.analyze_workload(workload_profile)
# Determine optimal topology
optimal_topology = await self.topology_optimizer.design(workload_analysis)
# Configure hierarchical coordination
hierarchy_config = await self.hierarchy_manager.configure(optimal_topology)
# Allocate resources globally
resource_allocation = await self.resource_allocator.distribute(
workload_analysis, hierarchy_config
)
# Scale agent deployment
scaling_plan = await self.scaling_engine.execute(resource_allocation)
return {
"hierarchy": hierarchy_config,
"topology": optimal_topology,
"resources": resource_allocation,
"scaling": scaling_plan,
"expected_performance": self.predict_performance(scaling_plan)
}
load_balancing_strategies:
geographic_distribution:
- latency_optimization: minimize response times
- compliance_boundaries: respect data sovereignty
- failover_regions: backup coordination centers
- cost_optimization: leverage regional pricing differences
skill_based_assignment:
- expertise_matching: optimal task-agent pairing
- capability_scaling: dynamic skill development
- specialization_index: measure agent specialization
- cross_training: flexible agent capabilities
performance_optimization:
- throughput_maximization: process as many tasks as possible
- latency_minimization: reduce response times
- quality_optimization: balance speed with accuracy
- cost_efficiency: minimize operational expenses
class PredictiveScalingEngine:
def __init__(self):
self.demand_predictor = DemandPredictionModel()
self.capacity_planner = CapacityPlanningModel()
self.cost_optimizer = CostOptimizationModel()
async def scale_system(self, forecast_horizon=24):
# Predict future demand
demand_forecast = await self.demand_predictor.predict(forecast_horizon)
# Plan capacity requirements
capacity_plan = await self.capacity_planner.optimize(demand_forecast)
# Optimize for cost and performance
scaling_plan = await self.cost_optimizer.balance(capacity_plan)
# Execute scaling operations
scaling_results = await self.execute_scaling(scaling_plan)
return {
"forecast": demand_forecast,
"capacity_plan": capacity_plan,
"scaling_plan": scaling_plan,
"execution_results": scaling_results,
"cost_impact": self.calculate_cost_impact(scaling_results)
}
conflict_types:
resource_conflicts:
- priority_based_resolution: urgent tasks first
- fair_scheduling: equitable resource sharing
- negotiation_protocols: agent-to-agent bargaining
- escalation_procedures: human intervention for disputes
priority_conflicts:
- business_impact_assessment: evaluate organizational impact
- sla_prioritization: service level agreement enforcement
- stakeholder_consensus: collaborative decision making
- executive_override: emergency priority assignment
capability_conflicts:
- skill_development: train agents for missing capabilities
- collaboration_models: multi-agent cooperation for complex tasks
- external_sourcing: third-party service integration
- task_decomposition: break down complex tasks into simpler ones
class MultiTenantCoordinator:
def __init__(self):
self.tenant_manager = TenantManager()
self.isolation_manager = IsolationManager()
self.resource_pool = ResourcePool()
async def coordinate_tenant_workload(self, tenant_id, workload):
# Verify tenant permissions and quotas
tenant_info = await self.tenant_manager.get_info(tenant_id)
# Ensure proper isolation from other tenants
isolated_context = await self.isolation_manager.create_context(tenant_info)
# Allocate dedicated resources
allocated_resources = await self.resource_pool.allocate(
tenant_info.resource_quota, isolated_context
)
# Execute tenant-specific coordination
coordination_result = await self.execute_coordination(
workload, allocated_resources, isolated_context
)
# Monitor for cross-tenant interference
await self.isolation_manager.verify_isolation(coordination_result)
return coordination_result
performance_kpis:
operational_metrics:
- agent_utilization_rate
- task_completion_throughput
- average_response_time
- system_availability_percentage
business_metrics:
- cost_per_transaction
- customer_satisfaction_score
- service_level_agreement_compliance
- revenue_impact_assessment
scalability_metrics:
- horizontal_scaling_efficiency
- vertical_scaling_limits
- network_latency_distribution
- resource_waste_percentage
resilience_strategies:
geographic_redundancy:
- multi_region_deployment: distribute across geographic areas
- active_active_configuration: all regions handle production traffic
- automated_failover: seamless transition during outages
- data_replication: synchronous and asynchronous replication
system_resilience:
- circuit_breaker_patterns: prevent cascading failures
- bulkhead_isolation: isolate failure domains
- graceful_degradation: maintain partial functionality
- self_healing_capabilities: automatic recovery procedures
Scenario: Coordinate 500+ trading agents across global markets with millisecond latency requirements.
Architecture Implementation:
Coordination Flow:
Global Trading Floor → Regional Trading Centers →
Specialized Trading Teams → Algorithmic Trading Agents →
Market Data Analyzers → Risk Management Agents → Compliance Monitors
Key Components:
Results:
Scenario: Coordinate 1,000+ clinical agents across a multi-hospital network.
Coordination Design:
Network Structure:
Hospital Network → Regional Medical Centers →
Specialty Departments → Medical Teams → Clinical Agents →
Diagnostic Systems → Treatment Coordinators → Patient Care Managers
Implementation:
Results:
Scenario: Coordinate 10,000+ IoT agents and human operators across urban services.
System Architecture:
Coordination Framework:
City Operations Center → District Management Offices →
Service Departments → Field Operations Teams → IoT Sensor Networks →
Traffic Management → Public Safety → Utilities Coordination → Emergency Services
Key Features:
Results:
The Multi-Agent Coordinator enables enterprise-scale orchestration of hundreds of agents through intelligent hierarchical coordination, adaptive resource management, and sophisticated conflict resolution, ensuring optimal performance and reliability in complex distributed environments.
Weekly Installs
82
Repository
GitHub Stars
45
First Seen
Jan 24, 2026
Security Audits
Gen Agent Trust HubPassSocketPassSnykPass
Installed on
opencode67
gemini-cli64
codex64
cursor59
github-copilot56
claude-code53
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