Provides expertise in distributing tasks across multi-agent systems efficiently. Specializes in load balancing algorithms, capability-based routing, cost optimization, and ensuring optimal resource utilization across distributed agent pools.
When to Use
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Designing task distribution strategies for multi-agent systems
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Implementing load balancing across worker pools
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Optimizing for cost (token economics) vs speed trade-offs
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Building routing logic based on agent capabilities
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Managing task queues with priorities and deadlines
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Implementing retry and failover strategies
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Scaling agent pools dynamically based on demand
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Monitoring and optimizing task throughput
Quick Start
Invoke this skill when:
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Designing task distribution strategies for multi-agent systems
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Implementing load balancing across worker pools
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Optimizing for cost (token economics) vs speed trade-offs
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Building routing logic based on agent capabilities
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Managing task queues with priorities and deadlines
Do NOT invoke when:
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Designing overall agent architecture → use agent-organizer
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Implementing individual agent logic → use appropriate domain skill
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Handling agent errors and recovery → use error-coordinator
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Building workflow orchestration → use workflow-orchestrator
Decision Framework
Distribution Strategy?
├── Uniform Workloads → Round-robin or random distribution
├── Variable Task Complexity → Weighted distribution by capability
├── Cost Sensitive → Route to cheapest capable agent
├── Latency Sensitive → Route to fastest/nearest agent
├── Specialized Tasks → Capability-based routing
└── Burst Traffic → Dynamic scaling + queue management
Core Workflows
1. Capability-Based Routing
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Define capability taxonomy for agents
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Tag tasks with required capabilities
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Implement capability matching algorithm
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Score agents by capability fit and availability
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Route to best-matched agent
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Track capability utilization for optimization
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Adjust routing weights based on performance
2. Cost-Optimized Distribution
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Define cost model per agent type (tokens, time, money)
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Estimate task cost based on complexity signals
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Set budget constraints and optimization targets
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Route to minimize cost while meeting SLAs
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Implement fallback to higher-cost agents when needed
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Track actual vs estimated costs
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Refine cost models from historical data
3. Queue Management with Priorities
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Define priority levels and SLA requirements
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Implement priority queue with deadline awareness
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Set up work stealing for idle agents
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Handle starvation of low-priority tasks
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Implement backpressure when queue depth exceeds threshold
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Monitor queue latency and throughput
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Scale agent pool based on queue metrics
Best Practices
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Implement health checks and remove unhealthy agents from pool
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Use exponential backoff with jitter for retries
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Track per-agent metrics for informed routing decisions
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Implement circuit breakers for failing agent types
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Design for graceful degradation under load
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Make routing decisions observable for debugging
Anti-Patterns
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Static assignment → Use dynamic routing based on current state
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Ignoring agent health → Remove unhealthy agents from rotation
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FIFO only → Implement priority awareness for SLA compliance
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Tight coupling → Decouple task producers from agent pool
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No backpressure → Implement admission control under overload