agent-orchestration-multi-agent-optimize

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Summary

Optimize multi-agent systems with coordinated profiling, workload distribution, and cost-aware orchestration. Use when improving agent performance, throughput, or reliability.

SKILL.md

Multi-Agent Optimization Toolkit

Use this skill when

Improving multi-agent coordination, throughput, or latency
Profiling agent workflows to identify bottlenecks
Designing orchestration strategies for complex workflows
Optimizing cost, context usage, or tool efficiency

Do not use this skill when

You only need to tune a single agent prompt
There are no measurable metrics or evaluation data
The task is unrelated to multi-agent orchestration

Instructions

Establish baseline metrics and target performance goals.
Profile agent workloads and identify coordination bottlenecks.
Apply orchestration changes and cost controls incrementally.
Validate improvements with repeatable tests and rollbacks.

Safety

Avoid deploying orchestration changes without regression testing.
Roll out changes gradually to prevent system-wide regressions.

Role: AI-Powered Multi-Agent Performance Engineering Specialist

Context

The Multi-Agent Optimization Tool is an advanced AI-driven framework designed to holistically improve system performance through intelligent, coordinated agent-based optimization. Leveraging cutting-edge AI orchestration techniques, this tool provides a comprehensive approach to performance engineering across multiple domains.

Core Capabilities

Intelligent multi-agent coordination
Performance profiling and bottleneck identification
Adaptive optimization strategies
Cross-domain performance optimization
Cost and efficiency tracking

Arguments Handling

The tool processes optimization arguments with flexible input parameters:

$TARGET: Primary system/application to optimize

def multi_agent_profiler(target_system):
    agents = [
        DatabasePerformanceAgent(target_system),
        ApplicationPerformanceAgent(target_system),
        FrontendPerformanceAgent(target_system)
    ]

    performance_profile = {}
    for agent in agents:
        performance_profile[agent.__class__.__name__] = agent.profile()

    return aggregate_performance_metrics(performance_profile)

def compress_context(context, max_tokens=4000):
    # Semantic compression using embedding-based truncation
    compressed_context = semantic_truncate(
        context,
        max_tokens=max_tokens,
        importance_threshold=0.7
    )
    return compressed_context

class MultiAgentOrchestrator:
    def __init__(self, agents):
        self.agents = agents
        self.execution_queue = PriorityQueue()
        self.performance_tracker = PerformanceTracker()

    def optimize(self, target_system):
        # Parallel agent execution with coordinated optimization
        with concurrent.futures.ThreadPoolExecutor() as executor:
            futures = {
                executor.submit(agent.optimize, target_system): agent
                for agent in self.agents
            }

            for future in concurrent.futures.as_completed(futures):
                agent = futures[future]
                result = future.result()
                self.performance_tracker.log(agent, result)

class CostOptimizer:
    def __init__(self):
        self.token_budget = 100000  # Monthly budget
        self.token_usage = 0
        self.model_costs = {
            'gpt-5': 0.03,
            'claude-4-sonnet': 0.015,
            'claude-4-haiku': 0.0025
        }

    def select_optimal_model(self, complexity):
        # Dynamic model selection based on task complexity and budget
        pass

agent-orchestration-multi-agent-optimize

Summary

SKILL.md

Multi-Agent Optimization Toolkit

Use this skill when

Do not use this skill when

Instructions

Safety

Role: AI-Powered Multi-Agent Performance Engineering Specialist

Context

Core Capabilities

Arguments Handling

1. Multi-Agent Performance Profiling

Profiling Strategy

Profiling Agents

Profiling Code Example

2. Context Window Optimization

Optimization Techniques

Context Compression Algorithm

3. Agent Coordination Efficiency

Coordination Principles

Orchestration Framework

4. Parallel Execution Optimization

Key Strategies

5. Cost Optimization Strategies

LLM Cost Management

Cost Tracking Example

6. Latency Reduction Techniques

Performance Acceleration

7. Quality vs Speed Tradeoffs

Optimization Spectrum

8. Monitoring and Continuous Improvement

Observability Framework

Reference Workflows

Workflow 1: E-Commerce Platform Optimization

Workflow 2: Enterprise API Performance Enhancement

Key Considerations

Limitations

Related skills