[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71R6MPWMEEFW4G0Q8WG4XZ2":3,"skill-same:01M71R6MPWMEEFW4G0Q8WG4XZ2":68},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":22,"publishedAt":43,"linkOnly":44,"markdownContent":45,"markdownBody":46,"frontMatter":47,"refName":48,"commitSha":49,"skillDirectory":50,"forksCount":13,"repositoryHtmlUrl":51,"repositoryHomepageUrl":6,"ownerHtmlUrl":52,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":53,"install":54,"resources":67},"01M71R6MPWMEEFW4G0Q8WG4XZ2","agent-orchestration-multi-agent-optimize",null,"Optimize multi-agent systems with coordinated profiling, workload distribution, and cost-aware orchestration. Use when improving agent performance, throughput, or reliability.","javiertarazon\u002Fide-agente-free-jt7-","javiertarazon","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F153141207?v=4","01M71R52TB3S9HXKH6WSNT8A7V","01M71R52VG5KS2DKDDH4GDQ30A",0,"MIT","https:\u002F\u002Fgithub.com\u002Fjaviertarazon\u002Fide-agente-free-jt7-\u002Fblob\u002Frelease\u002Fv4.2.11-panel-pro\u002F.github\u002Fskills\u002Fagent-orchestration-multi-agent-optimize\u002FSKILL.md",[17],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42],"agent orchestration","orchestration","multi agent","optimize","multi","agent orchestration multi agent optimize","systems","profiling","cost","with","use","throughput","multi-agent","workload","unknown","when","performance","reliability","distribution","community","2026-11-10 13:02:02",false,"---\nname: agent-orchestration-multi-agent-optimize\ndescription: \"Optimize multi-agent systems with coordinated profiling, workload distribution, and cost-aware orchestration. Use when improving agent performance, throughput, or reliability.\"\nrisk: unknown\nsource: community\n---\n\n# Multi-Agent Optimization Toolkit\n\n## Use this skill when\n\n- Improving multi-agent coordination, throughput, or latency\n- Profiling agent workflows to identify bottlenecks\n- Designing orchestration strategies for complex workflows\n- Optimizing cost, context usage, or tool efficiency\n\n## Do not use this skill when\n\n- You only need to tune a single agent prompt\n- There are no measurable metrics or evaluation data\n- The task is unrelated to multi-agent orchestration\n\n## Instructions\n\n1. Establish baseline metrics and target performance goals.\n2. Profile agent workloads and identify coordination bottlenecks.\n3. Apply orchestration changes and cost controls incrementally.\n4. Validate improvements with repeatable tests and rollbacks.\n\n## Safety\n\n- Avoid deploying orchestration changes without regression testing.\n- Roll out changes gradually to prevent system-wide regressions.\n\n## Role: AI-Powered Multi-Agent Performance Engineering Specialist\n\n### Context\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\n### Core Capabilities\n\n- Intelligent multi-agent coordination\n- Performance profiling and bottleneck identification\n- Adaptive optimization strategies\n- Cross-domain performance optimization\n- Cost and efficiency tracking\n\n## Arguments Handling\n\nThe tool processes optimization arguments with flexible input parameters:\n\n- `$TARGET`: Primary system\u002Fapplication to optimize\n- `$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\n\n## 1. Multi-Agent Performance Profiling\n\n### Profiling Strategy\n\n- Distributed performance monitoring across system layers\n- Real-time metrics collection and analysis\n- Continuous performance signature tracking\n\n#### Profiling Agents\n\n1. **Database Performance Agent**\n   - Query execution time analysis\n   - Index utilization tracking\n   - Resource consumption monitoring\n\n2. **Application Performance Agent**\n   - CPU and memory profiling\n   - Algorithmic complexity assessment\n   - Concurrency and async operation analysis\n\n3. **Frontend Performance Agent**\n   - Rendering performance metrics\n   - Network request optimization\n   - Core Web Vitals monitoring\n\n### Profiling Code Example\n\n```python\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```\n\n## 2. Context Window Optimization\n\n### Optimization Techniques\n\n- Intelligent context compression\n- Semantic relevance filtering\n- Dynamic context window resizing\n- Token budget management\n\n### Context Compression Algorithm\n\n```python\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```\n\n## 3. Agent Coordination Efficiency\n\n### Coordination Principles\n\n- Parallel execution design\n- Minimal inter-agent communication overhead\n- Dynamic workload distribution\n- Fault-tolerant agent interactions\n\n### Orchestration Framework\n\n```python\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```\n\n## 4. Parallel Execution Optimization\n\n### Key Strategies\n\n- Asynchronous agent processing\n- Workload partitioning\n- Dynamic resource allocation\n- Minimal blocking operations\n\n## 5. Cost Optimization Strategies\n\n### LLM Cost Management\n\n- Token usage tracking\n- Adaptive model selection\n- Caching and result reuse\n- Efficient prompt engineering\n\n### Cost Tracking Example\n\n```python\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```\n\n## 6. Latency Reduction Techniques\n\n### Performance Acceleration\n\n- Predictive caching\n- Pre-warming agent contexts\n- Intelligent result memoization\n- Reduced round-trip communication\n\n## 7. Quality vs Speed Tradeoffs\n\n### Optimization Spectrum\n\n- Performance thresholds\n- Acceptable degradation margins\n- Quality-aware optimization\n- Intelligent compromise selection\n\n## 8. Monitoring and Continuous Improvement\n\n### Observability Framework\n\n- Real-time performance dashboards\n- Automated optimization feedback loops\n- Machine learning-driven improvement\n- Adaptive optimization strategies\n\n## Reference Workflows\n\n### Workflow 1: E-Commerce Platform Optimization\n\n1. Initial performance profiling\n2. Agent-based optimization\n3. Cost and performance tracking\n4. Continuous improvement cycle\n\n### Workflow 2: Enterprise API Performance Enhancement\n\n1. Comprehensive system analysis\n2. Multi-layered agent optimization\n3. Iterative performance refinement\n4. Cost-efficient scaling strategy\n\n## Key Considerations\n\n- Always measure before and after optimization\n- Maintain system stability during optimization\n- Balance performance gains with resource consumption\n- Implement gradual, reversible changes\n\nTarget Optimization: $ARGUMENTS\n","# Multi-Agent Optimization Toolkit\n\n## Use this skill when\n\n- Improving multi-agent coordination, throughput, or latency\n- Profiling agent workflows to identify bottlenecks\n- Designing orchestration strategies for complex workflows\n- Optimizing cost, context usage, or tool efficiency\n\n## Do not use this skill when\n\n- You only need to tune a single agent prompt\n- There are no measurable metrics or evaluation data\n- The task is unrelated to multi-agent orchestration\n\n## Instructions\n\n1. Establish baseline metrics and target performance goals.\n2. Profile agent workloads and identify coordination bottlenecks.\n3. Apply orchestration changes and cost controls incrementally.\n4. Validate improvements with repeatable tests and rollbacks.\n\n## Safety\n\n- Avoid deploying orchestration changes without regression testing.\n- Roll out changes gradually to prevent system-wide regressions.\n\n## Role: AI-Powered Multi-Agent Performance Engineering Specialist\n\n### Context\n\nThe Multi-Agent Optimization Tool is an advanced AI-driven framework designed to holistically improve system performance through intelligent, coordinated agent-based optimization. Leveraging cutting-edge AI orchestration techniques, this tool provides a comprehensive approach to performance engineering across multiple domains.\n\n### Core Capabilities\n\n- Intelligent multi-agent coordination\n- Performance profiling and bottleneck identification\n- Adaptive optimization strategies\n- Cross-domain performance optimization\n- Cost and efficiency tracking\n\n## Arguments Handling\n\nThe tool processes optimization arguments with flexible input parameters:\n\n- `$TARGET`: Primary system\u002Fapplication 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\n\n## 1. Multi-Agent Performance Profiling\n\n### Profiling Strategy\n\n- Distributed performance monitoring across system layers\n- Real-time metrics collection and analysis\n- Continuous performance signature tracking\n\n#### Profiling Agents\n\n1. **Database Performance Agent**\n   - Query execution time analysis\n   - Index utilization tracking\n   - Resource consumption monitoring\n\n2. **Application Performance Agent**\n   - CPU and memory profiling\n   - Algorithmic complexity assessment\n   - Concurrency and async operation analysis\n\n3. **Frontend Performance Agent**\n   - Rendering performance metrics\n   - Network request optimization\n   - Core Web Vitals monitoring\n\n### Profiling Code Example\n\n```python\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```\n\n## 2. Context Window Optimization\n\n### Optimization Techniques\n\n- Intelligent context compression\n- Semantic relevance filtering\n- Dynamic context window resizing\n- Token budget management\n\n### Context Compression Algorithm\n\n```python\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```\n\n## 3. Agent Coordination Efficiency\n\n### Coordination Principles\n\n- Parallel execution design\n- Minimal inter-agent communication overhead\n- Dynamic workload distribution\n- Fault-tolerant agent interactions\n\n### Orchestration Framework\n\n```python\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```\n\n## 4. Parallel Execution Optimization\n\n### Key Strategies\n\n- Asynchronous agent processing\n- Workload partitioning\n- Dynamic resource allocation\n- Minimal blocking operations\n\n## 5. Cost Optimization Strategies\n\n### LLM Cost Management\n\n- Token usage tracking\n- Adaptive model selection\n- Caching and result reuse\n- Efficient prompt engineering\n\n### Cost Tracking Example\n\n```python\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```\n\n## 6. Latency Reduction Techniques\n\n### Performance Acceleration\n\n- Predictive caching\n- Pre-warming agent contexts\n- Intelligent result memoization\n- Reduced round-trip communication\n\n## 7. Quality vs Speed Tradeoffs\n\n### Optimization Spectrum\n\n- Performance thresholds\n- Acceptable degradation margins\n- Quality-aware optimization\n- Intelligent compromise selection\n\n## 8. Monitoring and Continuous Improvement\n\n### Observability Framework\n\n- Real-time performance dashboards\n- Automated optimization feedback loops\n- Machine learning-driven improvement\n- Adaptive optimization strategies\n\n## Reference Workflows\n\n### Workflow 1: E-Commerce Platform Optimization\n\n1. Initial performance profiling\n2. Agent-based optimization\n3. Cost and performance tracking\n4. Continuous improvement cycle\n\n### Workflow 2: Enterprise API Performance Enhancement\n\n1. Comprehensive system analysis\n2. Multi-layered agent optimization\n3. Iterative performance refinement\n4. Cost-efficient scaling strategy\n\n## Key Considerations\n\n- Always measure before and after optimization\n- Maintain system stability during optimization\n- Balance performance gains with resource consumption\n- Implement gradual, reversible changes\n\nTarget Optimization: $ARGUMENTS\n",{"name":5,"risk":37,"source":42,"description":7},"release\u002Fv4.2.11-panel-pro","a92ea02bcf7e2b3dbaf46d49696c29a3cb490d4f",".github\u002Fskills\u002Fagent-orchestration-multi-agent-optimize","https:\u002F\u002Fgithub.com\u002Fjaviertarazon\u002Fide-agente-free-jt7-","https:\u002F\u002Fgithub.com\u002Fjaviertarazon","2026-07-12 07:10:45",{"source":8,"skillName":5,"commands":55,"prompt":65,"zipAvailable":66},[56,59,62],{"tool":57,"command":58},"npx","npx skills add javiertarazon\u002Fide-agente-free-jt7- --skill agent-orchestration-multi-agent-optimize",{"tool":60,"command":61},"bunx","bunx skills add javiertarazon\u002Fide-agente-free-jt7- --skill agent-orchestration-multi-agent-optimize",{"tool":63,"command":64},"pnpm","pnpm dlx skills add javiertarazon\u002Fide-agente-free-jt7- --skill agent-orchestration-multi-agent-optimize","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fjaviertarazon\u002Fide-agente-free-jt7-\u002Fblob\u002Frelease\u002Fv4.2.11-panel-pro\u002F.github\u002Fskills\u002Fagent-orchestration-multi-agent-optimize\u002FSKILL.md",true,[],[69,94,119,139,154,176],{"skillNo":70,"name":71,"nameZh":6,"description":72,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":73,"categories":74,"keywords":78,"publishedAt":43},"01M71TQ7Q6Z10VWF3C2H5X1KW9","zustand-store-ts","Create Zustand stores with TypeScript, subscribeWithSelector middleware, and proper state\u002Faction separation. Use when building React state management, creating global stores, or implementing reacti...","https:\u002F\u002Fgithub.com\u002Fjaviertarazon\u002Fide-agente-free-jt7-\u002Fblob\u002Frelease\u002Fv4.2.11-panel-pro\u002F.github\u002Fskills\u002Fzustand-store-ts\u002FSKILL.md",[75],{"categoryNo":76,"categoryName":77,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000009","前端开发",[79,80,81,42,82,83,33,84,38,37,85,32,86,87,88,89,90,91,92,93],"zustand","store","zustand store ts","middleware","implementing","react state management","react","state","create","state management","typescript","action","management","separation","global",{"skillNo":95,"name":96,"nameZh":6,"description":97,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":98,"categories":99,"keywords":101,"publishedAt":43},"01M71TQ5EAR40G6W0MV5ZDVEZT","zoom-automation","Automate Zoom meeting creation, management, recordings, webinars, and participant tracking via Rube MCP (Composio). Always search tools first for current schemas.","https:\u002F\u002Fgithub.com\u002Fjaviertarazon\u002Fide-agente-free-jt7-\u002Fblob\u002Frelease\u002Fv4.2.11-panel-pro\u002F.github\u002Fskills\u002Fzoom-automation\u002FSKILL.md",[100],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},[102,103,104,42,105,106,107,108,109,37,110,111,112,113,114,115,116,117,91,118],"zoom automation","zoom","automation","creation","for","search","schemas","MCP","first","via","meeting","search tools","tracking","webinars","composio","recordings","Tools",{"skillNo":120,"name":121,"nameZh":6,"description":122,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":123,"categories":124,"keywords":126,"publishedAt":43},"01M71TQ29B1EYBCEAP1ZBGXQ1A","zoho-crm-automation","Automate Zoho CRM tasks via Rube MCP (Composio): create\u002Fupdate records, search contacts, manage leads, and convert leads. Always search tools first for current schemas.","https:\u002F\u002Fgithub.com\u002Fjaviertarazon\u002Fide-agente-free-jt7-\u002Fblob\u002Frelease\u002Fv4.2.11-panel-pro\u002F.github\u002Fskills\u002Fzoho-crm-automation\u002FSKILL.md",[125],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},[127,128,104,129,130,131,42,132,133,106,134,107,108,135,109,37,136,137,138,87],"crm automation","zoho crm automation","crm","zoho crm","zoho","leads","manage leads","manage","records","convert","tasks","contacts",{"skillNo":140,"name":141,"nameZh":6,"description":142,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":143,"categories":144,"keywords":146,"publishedAt":43},"01M71TPZPMN60DH0XV58KVHY23","zendesk-automation","Automate Zendesk tasks via Rube MCP (Composio): tickets, users, organizations, replies. Always search tools first for current schemas.","https:\u002F\u002Fgithub.com\u002Fjaviertarazon\u002Fide-agente-free-jt7-\u002Fblob\u002Frelease\u002Fv4.2.11-panel-pro\u002F.github\u002Fskills\u002Fzendesk-automation\u002FSKILL.md",[145],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},[147,104,148,42,106,107,108,109,149,37,137,150,151,110,111,152,113,116,118,153],"zendesk automation","zendesk","organizations","replies","users","tickets","automate",{"skillNo":155,"name":156,"nameZh":6,"description":157,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":158,"categories":159,"keywords":160,"publishedAt":43},"01M71TPXDWKHKG4N6TVEPXWD0N","zapier-make-patterns","No-code automation democratizes workflow building. Zapier and Make (formerly Integromat) let non-developers automate business processes without writing code. But no-code doesn't mean no-complexity ...","https:\u002F\u002Fgithub.com\u002Fjaviertarazon\u002Fide-agente-free-jt7-\u002Fblob\u002Frelease\u002Fv4.2.11-panel-pro\u002F.github\u002Fskills\u002Fzapier-make-patterns\u002FSKILL.md",[],[161,162,163,164,165,166,167,168,169,104,170,37,171,172,173,174,175,153],"zapier make patterns","zapier","patterns","make","No-Code","complexity","writing","spawner","business","workflow","code","integromat","apache","processes","but",{"skillNo":177,"name":178,"nameZh":6,"description":179,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":180,"categories":181,"keywords":185,"publishedAt":43},"01M71TPT5T10WXNKW9G7ZXZ27W","youtube-summarizer","Extract transcripts from YouTube videos and generate comprehensive, detailed summaries using intelligent analysis frameworks","https:\u002F\u002Fgithub.com\u002Fjaviertarazon\u002Fide-agente-free-jt7-\u002Fblob\u002Frelease\u002Fv4.2.11-panel-pro\u002F.github\u002Fskills\u002Fyoutube-summarizer\u002FSKILL.md",[182],{"categoryNo":183,"categoryName":184,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000016","图像与多媒体",[186,187,188,189,42,190,191,192,193,194,195,196,171,197,198,199,200,201,202,203],"summarizer","youtube","youtube summarizer","Video","content-analysis","frameworks","copilot","2025","github-copilot","extract transcripts","analysis","codex","cli","summaries","github","generate","summarization","2026"]