{ "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": { "downloadMode": "redirect", "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", "installMethod": "Manual import", "extraction": "Extract archive", "prerequisites": [ "OpenClaw" ], "packageFormat": "ZIP package", "includedAssets": [ "SKILL.md" ], "primaryDoc": "SKILL.md", "quickSetup": [ "Download the package from Yavira.", "Extract the archive and review SKILL.md first.", "Import or place the package into your OpenClaw setup." ], "agentAssist": { "summary": "Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.", "steps": [ "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." ], "prompts": [ { "label": "New install", "body": "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." }, { "label": "Upgrade existing", "body": "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." } ] }, "sourceHealth": { "source": "tencent", "status": "healthy", "reason": "direct_download_ok", "recommendedAction": "download", "checkedAt": "2026-04-23T16:43:11.935Z", "expiresAt": "2026-04-30T16:43:11.935Z", "httpStatus": 200, "finalUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=4claw-imageboard", "contentType": "application/zip", "probeMethod": "head", "details": { "probeUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=4claw-imageboard", "contentDisposition": "attachment; filename=\"4claw-imageboard-1.0.1.zip\"", "redirectLocation": null, "bodySnippet": null }, "scope": "source", "summary": "Source download looks usable.", "detail": "Yavira can redirect you to the upstream package for this source.", "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." ] }, "downloadPageUrl": "https://openagent3.xyz/downloads/agent-orchestration-multi-agent-optimize", "agentPageUrl": "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" }, "agentAssist": { "summary": "Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.", "steps": [ "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." ], "prompts": [ { "label": "New install", "body": "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." }, { "label": "Upgrade existing", "body": "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." } ] }, "documentation": { "source": "clawhub", "primaryDoc": "SKILL.md", "sections": [ { "title": "Use this skill when", "body": "Improving multi-agent coordination, throughput, or latency\nProfiling agent workflows to identify bottlenecks\nDesigning orchestration strategies for complex workflows\nOptimizing cost, context usage, or tool efficiency" }, { "title": "Do not use this skill when", "body": "You only need to tune a single agent prompt\nThere are no measurable metrics or evaluation data\nThe task is unrelated to multi-agent orchestration" }, { "title": "Instructions", "body": "Establish baseline metrics and target performance goals.\nProfile agent workloads and identify coordination bottlenecks.\nApply orchestration changes and cost controls incrementally.\nValidate improvements with repeatable tests and rollbacks." }, { "title": "Safety", "body": "Avoid deploying orchestration changes without regression testing.\nRoll out changes gradually to prevent system-wide regressions." }, { "title": "Context", "body": "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." }, { "title": "Core Capabilities", "body": "Intelligent multi-agent coordination\nPerformance profiling and bottleneck identification\nAdaptive optimization strategies\nCross-domain performance optimization\nCost and efficiency tracking" }, { "title": "Arguments Handling", "body": "The tool processes optimization arguments with flexible input parameters:\n\n$TARGET: Primary system/application to optimize\n$PERFORMANCE_GOALS: Specific performance metrics and objectives\n$OPTIMIZATION_SCOPE: Depth of optimization (quick-win, comprehensive)\n$BUDGET_CONSTRAINTS: Cost and resource limitations\n$QUALITY_METRICS: Performance quality thresholds" }, { "title": "Profiling Strategy", "body": "Distributed performance monitoring across system layers\nReal-time metrics collection and analysis\nContinuous performance signature tracking\n\nProfiling Agents\n\nDatabase Performance Agent\n\nQuery execution time analysis\nIndex utilization tracking\nResource consumption monitoring\n\n\n\nApplication Performance Agent\n\nCPU and memory profiling\nAlgorithmic complexity assessment\nConcurrency and async operation analysis\n\n\n\nFrontend Performance Agent\n\nRendering performance metrics\nNetwork request optimization\nCore Web Vitals monitoring" }, { "title": "Profiling Code Example", "body": "def multi_agent_profiler(target_system):\n agents = [\n DatabasePerformanceAgent(target_system),\n ApplicationPerformanceAgent(target_system),\n FrontendPerformanceAgent(target_system)\n ]\n\n performance_profile = {}\n for agent in agents:\n performance_profile[agent.__class__.__name__] = agent.profile()\n\n return aggregate_performance_metrics(performance_profile)" }, { "title": "Optimization Techniques", "body": "Intelligent context compression\nSemantic relevance filtering\nDynamic context window resizing\nToken budget management" }, { "title": "Context Compression Algorithm", "body": "def compress_context(context, max_tokens=4000):\n # Semantic compression using embedding-based truncation\n compressed_context = semantic_truncate(\n context,\n max_tokens=max_tokens,\n importance_threshold=0.7\n )\n return compressed_context" }, { "title": "Coordination Principles", "body": "Parallel execution design\nMinimal inter-agent communication overhead\nDynamic workload distribution\nFault-tolerant agent interactions" }, { "title": "Orchestration Framework", "body": "class MultiAgentOrchestrator:\n def __init__(self, agents):\n self.agents = agents\n self.execution_queue = PriorityQueue()\n self.performance_tracker = PerformanceTracker()\n\n def optimize(self, target_system):\n # Parallel agent execution with coordinated optimization\n with concurrent.futures.ThreadPoolExecutor() as executor:\n futures = {\n executor.submit(agent.optimize, target_system): agent\n for agent in self.agents\n }\n\n for future in concurrent.futures.as_completed(futures):\n agent = futures[future]\n result = future.result()\n self.performance_tracker.log(agent, result)" }, { "title": "Key Strategies", "body": "Asynchronous agent processing\nWorkload partitioning\nDynamic resource allocation\nMinimal blocking operations" }, { "title": "LLM Cost Management", "body": "Token usage tracking\nAdaptive model selection\nCaching and result reuse\nEfficient prompt engineering" }, { "title": "Cost Tracking Example", "body": "class CostOptimizer:\n def __init__(self):\n self.token_budget = 100000 # Monthly budget\n self.token_usage = 0\n self.model_costs = {\n 'gpt-5': 0.03,\n 'claude-4-sonnet': 0.015,\n 'claude-4-haiku': 0.0025\n }\n\n def select_optimal_model(self, complexity):\n # Dynamic model selection based on task complexity and budget\n pass" }, { "title": "Performance Acceleration", "body": "Predictive caching\nPre-warming agent contexts\nIntelligent result memoization\nReduced round-trip communication" }, { "title": "Optimization Spectrum", "body": "Performance thresholds\nAcceptable degradation margins\nQuality-aware optimization\nIntelligent compromise selection" }, { "title": "Observability Framework", "body": "Real-time performance dashboards\nAutomated optimization feedback loops\nMachine learning-driven improvement\nAdaptive optimization strategies" }, { "title": "Workflow 1: E-Commerce Platform Optimization", "body": "Initial performance profiling\nAgent-based optimization\nCost and performance tracking\nContinuous improvement cycle" }, { "title": "Workflow 2: Enterprise API Performance Enhancement", "body": "Comprehensive system analysis\nMulti-layered agent optimization\nIterative performance refinement\nCost-efficient scaling strategy" }, { "title": "Key Considerations", "body": "Always measure before and after optimization\nMaintain system stability during optimization\nBalance performance gains with resource consumption\nImplement gradual, reversible changes\n\nTarget Optimization: $ARGUMENTS" } ], "body": "Multi-Agent Optimization Toolkit\nUse this skill when\nImproving multi-agent coordination, throughput, or latency\nProfiling agent workflows to identify bottlenecks\nDesigning orchestration strategies for complex workflows\nOptimizing cost, context usage, or tool efficiency\nDo not use this skill when\nYou only need to tune a single agent prompt\nThere are no measurable metrics or evaluation data\nThe task is unrelated to multi-agent orchestration\nInstructions\nEstablish baseline metrics and target performance goals.\nProfile agent workloads and identify coordination bottlenecks.\nApply orchestration changes and cost controls incrementally.\nValidate improvements with repeatable tests and rollbacks.\nSafety\nAvoid deploying orchestration changes without regression testing.\nRoll out changes gradually to prevent system-wide regressions.\nRole: AI-Powered Multi-Agent Performance Engineering Specialist\nContext\n\nThe Multi-Agent Optimization Tool is an advanced AI-driven framework designed to holistically improve system performance through intelligent, coordinated agent-based optimization. Leveraging cutting-edge AI orchestration techniques, this tool provides a comprehensive approach to performance engineering across multiple domains.\n\nCore Capabilities\nIntelligent multi-agent coordination\nPerformance profiling and bottleneck identification\nAdaptive optimization strategies\nCross-domain performance optimization\nCost and efficiency tracking\nArguments Handling\n\nThe tool processes optimization arguments with flexible input parameters:\n\n$TARGET: Primary system/application to optimize\n$PERFORMANCE_GOALS: Specific performance metrics and objectives\n$OPTIMIZATION_SCOPE: Depth of optimization (quick-win, comprehensive)\n$BUDGET_CONSTRAINTS: Cost and resource limitations\n$QUALITY_METRICS: Performance quality thresholds\n1. Multi-Agent Performance Profiling\nProfiling Strategy\nDistributed performance monitoring across system layers\nReal-time metrics collection and analysis\nContinuous performance signature tracking\nProfiling Agents\n\nDatabase Performance Agent\n\nQuery execution time analysis\nIndex utilization tracking\nResource consumption monitoring\n\nApplication Performance Agent\n\nCPU and memory profiling\nAlgorithmic complexity assessment\nConcurrency and async operation analysis\n\nFrontend Performance Agent\n\nRendering performance metrics\nNetwork request optimization\nCore Web Vitals monitoring\nProfiling Code Example\ndef multi_agent_profiler(target_system):\n agents = [\n DatabasePerformanceAgent(target_system),\n ApplicationPerformanceAgent(target_system),\n FrontendPerformanceAgent(target_system)\n ]\n\n performance_profile = {}\n for agent in agents:\n performance_profile[agent.__class__.__name__] = agent.profile()\n\n return aggregate_performance_metrics(performance_profile)\n\n2. Context Window Optimization\nOptimization Techniques\nIntelligent context compression\nSemantic relevance filtering\nDynamic context window resizing\nToken budget management\nContext Compression Algorithm\ndef compress_context(context, max_tokens=4000):\n # Semantic compression using embedding-based truncation\n compressed_context = semantic_truncate(\n context,\n max_tokens=max_tokens,\n importance_threshold=0.7\n )\n return compressed_context\n\n3. Agent Coordination Efficiency\nCoordination Principles\nParallel execution design\nMinimal inter-agent communication overhead\nDynamic workload distribution\nFault-tolerant agent interactions\nOrchestration Framework\nclass MultiAgentOrchestrator:\n def __init__(self, agents):\n self.agents = agents\n self.execution_queue = PriorityQueue()\n self.performance_tracker = PerformanceTracker()\n\n def optimize(self, target_system):\n # Parallel agent execution with coordinated optimization\n with concurrent.futures.ThreadPoolExecutor() as executor:\n futures = {\n executor.submit(agent.optimize, target_system): agent\n for agent in self.agents\n }\n\n for future in concurrent.futures.as_completed(futures):\n agent = futures[future]\n result = future.result()\n self.performance_tracker.log(agent, result)\n\n4. Parallel Execution Optimization\nKey Strategies\nAsynchronous agent processing\nWorkload partitioning\nDynamic resource allocation\nMinimal blocking operations\n5. Cost Optimization Strategies\nLLM Cost Management\nToken usage tracking\nAdaptive model selection\nCaching and result reuse\nEfficient prompt engineering\nCost Tracking Example\nclass CostOptimizer:\n def __init__(self):\n self.token_budget = 100000 # Monthly budget\n self.token_usage = 0\n self.model_costs = {\n 'gpt-5': 0.03,\n 'claude-4-sonnet': 0.015,\n 'claude-4-haiku': 0.0025\n }\n\n def select_optimal_model(self, complexity):\n # Dynamic model selection based on task complexity and budget\n pass\n\n6. Latency Reduction Techniques\nPerformance Acceleration\nPredictive caching\nPre-warming agent contexts\nIntelligent result memoization\nReduced round-trip communication\n7. Quality vs Speed Tradeoffs\nOptimization Spectrum\nPerformance thresholds\nAcceptable degradation margins\nQuality-aware optimization\nIntelligent compromise selection\n8. Monitoring and Continuous Improvement\nObservability Framework\nReal-time performance dashboards\nAutomated optimization feedback loops\nMachine learning-driven improvement\nAdaptive optimization strategies\nReference Workflows\nWorkflow 1: E-Commerce Platform Optimization\nInitial performance profiling\nAgent-based optimization\nCost and performance tracking\nContinuous improvement cycle\nWorkflow 2: Enterprise API Performance Enhancement\nComprehensive system analysis\nMulti-layered agent optimization\nIterative performance refinement\nCost-efficient scaling strategy\nKey Considerations\nAlways measure before and after optimization\nMaintain system stability during optimization\nBalance performance gains with resource consumption\nImplement gradual, reversible changes\n\nTarget Optimization: $ARGUMENTS" }, "trust": { "sourceLabel": "tencent", "provenanceUrl": "https://clawhub.ai/rustyorb/agent-orchestration-multi-agent-optimize", "publisherUrl": "https://clawhub.ai/rustyorb/agent-orchestration-multi-agent-optimize", "owner": "rustyorb", "version": "1.0.0", "license": null, "verificationStatus": "Indexed source record" }, "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" } }