Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· AI
Agent Orchestration Multi Agent Optimize
Optimize multi-agent systems with coordinated profiling, workload distribution, and cost-aware orchestration. Use when improving agent performance, throughput, or reliability.
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Optimize multi-agent systems with coordinated profiling, workload distribution, and cost-aware orchestration. Use when improving agent performance, throughput, or reliability.
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Install for OpenClaw
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Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md
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Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
- Publisher
- rustyorb
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Documentation
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.
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 $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 thresholds
Profiling Strategy
Distributed performance monitoring across system layers Real-time metrics collection and analysis Continuous performance signature tracking Profiling Agents Database Performance Agent Query execution time analysis Index utilization tracking Resource consumption monitoring Application Performance Agent CPU and memory profiling Algorithmic complexity assessment Concurrency and async operation analysis Frontend Performance Agent Rendering performance metrics Network request optimization Core Web Vitals monitoring
Profiling Code Example
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)
Optimization Techniques
Intelligent context compression Semantic relevance filtering Dynamic context window resizing Token budget management
Context Compression Algorithm
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
Coordination Principles
Parallel execution design Minimal inter-agent communication overhead Dynamic workload distribution Fault-tolerant agent interactions
Orchestration Framework
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)
Key Strategies
Asynchronous agent processing Workload partitioning Dynamic resource allocation Minimal blocking operations
LLM Cost Management
Token usage tracking Adaptive model selection Caching and result reuse Efficient prompt engineering
Cost Tracking Example
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
Performance Acceleration
Predictive caching Pre-warming agent contexts Intelligent result memoization Reduced round-trip communication
Optimization Spectrum
Performance thresholds Acceptable degradation margins Quality-aware optimization Intelligent compromise selection
Observability Framework
Real-time performance dashboards Automated optimization feedback loops Machine learning-driven improvement Adaptive optimization strategies
Workflow 1: E-Commerce Platform Optimization
Initial performance profiling Agent-based optimization Cost and performance tracking Continuous improvement cycle
Workflow 2: Enterprise API Performance Enhancement
Comprehensive system analysis Multi-layered agent optimization Iterative performance refinement Cost-efficient scaling strategy
Key Considerations
Always measure before and after optimization Maintain system stability during optimization Balance performance gains with resource consumption Implement gradual, reversible changes Target Optimization: $ARGUMENTS
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
1 Docs
- SKILL.md