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From agent-orchestration
Profiles and optimizes system performance with multi-agent framework targeting database, application, frontend, and context window. Accepts target, goals, scope, budget, quality args.
npx claudepluginhub sumeet138/qwen-code-agents --plugin agent-orchestrationHow this command is triggered — by the user, by Claude, or both
Slash command
/agent-orchestration:multi-agent-optimizeThe summary Claude sees in its command listing — used to decide when to auto-load this command
# Multi-Agent Optimization Toolkit ## 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...
/multi-agent-optimizeProfiles and optimizes system performance with multi-agent framework targeting database, application, frontend, and context window. Accepts target, goals, scope, budget, quality args.
/multi-agent-optimizeCoordinates AI agents to profile and optimize multi-agent system performance across database, application, and frontend layers, reducing latency and improving coordination.
/optimize-performanceProfiles application performance, identifies bottlenecks, implements optimizations for backend, frontend, and infrastructure, then benchmarks improvements.
/performance-analysisProfiles application performance for frontend and backend, identifies bottlenecks via metrics like Core Web Vitals and API analysis, and suggests optimizations.
/monitor-performanceMonitors AI agent performance metrics, task execution efficiency, resource utilization, and workflow quality, providing dashboards, insights, and optimization recommendations.
/optimizeOptimizes performance of <PERFORMANCE_TARGET> via bottleneck analysis, expert-coordinated strategies for algorithms/resources/scalability, and outputs plans with benchmarks/monitoring.
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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.
The tool processes optimization arguments with flexible input parameters:
$TARGET: Primary system/application to optimize$PERFORMANCE_GOALS: Specific performance metrics and objectives$OPTIMIZATION_SCOPE: Depth of optimization (quick-win, comprehensive)$BUDGET_CONSTRAINTS: Cost and resource limitations$QUALITY_METRICS: Performance quality thresholdsDatabase Performance Agent
Application Performance Agent
Frontend Performance Agent
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-5.2': 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
Target Optimization: $ARGUMENTS