# Send Agent Orchestration Multi Agent Optimize to your agent Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually. ## Fast path - Download the package from Yavira. - Extract it into a folder your agent can access. - Paste one of the prompts below and point your agent at the extracted folder. ## Suggested prompts ### New install ```text I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete. ``` ### Upgrade existing ```text I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run. ``` ## Machine-readable fields ```json { "schemaVersion": "1.0", "item": { "slug": "agent-orchestration-multi-agent-optimize", "name": "Agent Orchestration Multi Agent Optimize", "source": "tencent", "type": "skill", "category": "AI 智能", "sourceUrl": "https://clawhub.ai/rustyorb/agent-orchestration-multi-agent-optimize", "canonicalUrl": "https://clawhub.ai/rustyorb/agent-orchestration-multi-agent-optimize", "targetPlatform": "OpenClaw" }, "install": { "downloadUrl": "/downloads/agent-orchestration-multi-agent-optimize", "sourceDownloadUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=agent-orchestration-multi-agent-optimize", "sourcePlatform": "tencent", "targetPlatform": "OpenClaw", "packageFormat": "ZIP package", "primaryDoc": "SKILL.md", "includedAssets": [ "SKILL.md" ], "downloadMode": "redirect", "sourceHealth": { "source": "tencent", "slug": "agent-orchestration-multi-agent-optimize", "status": "healthy", "reason": "direct_download_ok", "recommendedAction": "download", "checkedAt": "2026-04-29T07:08:49.479Z", "expiresAt": "2026-05-06T07:08:49.479Z", "httpStatus": 200, "finalUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=agent-orchestration-multi-agent-optimize", "contentType": "application/zip", "probeMethod": "head", "details": { "probeUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=agent-orchestration-multi-agent-optimize", "contentDisposition": "attachment; filename=\"agent-orchestration-multi-agent-optimize-1.0.0.zip\"", "redirectLocation": null, "bodySnippet": null, "slug": "agent-orchestration-multi-agent-optimize" }, "scope": "item", "summary": "Item download looks usable.", "detail": "Yavira can redirect you to the upstream package for this item.", "primaryActionLabel": "Download for OpenClaw", "primaryActionHref": "/downloads/agent-orchestration-multi-agent-optimize" }, "validation": { "installChecklist": [ "Use the Yavira download entry.", "Review SKILL.md after the package is downloaded.", "Confirm the extracted package contains the expected setup assets." ], "postInstallChecks": [ "Confirm the extracted package includes the expected docs or setup files.", "Validate the skill or prompts are available in your target agent workspace.", "Capture any manual follow-up steps the agent could not complete." ] } }, "links": { "detailUrl": "https://openagent3.xyz/skills/agent-orchestration-multi-agent-optimize", "downloadUrl": "https://openagent3.xyz/downloads/agent-orchestration-multi-agent-optimize", "agentUrl": "https://openagent3.xyz/skills/agent-orchestration-multi-agent-optimize/agent", "manifestUrl": "https://openagent3.xyz/skills/agent-orchestration-multi-agent-optimize/agent.json", "briefUrl": "https://openagent3.xyz/skills/agent-orchestration-multi-agent-optimize/agent.md" } } ``` ## 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 ## Trust - Source: tencent - Verification: Indexed source record - Publisher: rustyorb - Version: 1.0.0 ## Source health - Status: healthy - Item download looks usable. - Yavira can redirect you to the upstream package for this item. - Health scope: item - Reason: direct_download_ok - Checked at: 2026-04-29T07:08:49.479Z - Expires at: 2026-05-06T07:08:49.479Z - Recommended action: Download for OpenClaw ## Links - [Detail page](https://openagent3.xyz/skills/agent-orchestration-multi-agent-optimize) - [Send to Agent page](https://openagent3.xyz/skills/agent-orchestration-multi-agent-optimize/agent) - [JSON manifest](https://openagent3.xyz/skills/agent-orchestration-multi-agent-optimize/agent.json) - [Markdown brief](https://openagent3.xyz/skills/agent-orchestration-multi-agent-optimize/agent.md) - [Download page](https://openagent3.xyz/downloads/agent-orchestration-multi-agent-optimize)