[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6CBSFDTGY477MHJD4GSKFRJ":3,"skill-same:01M6CBSFDTGY477MHJD4GSKFRJ":59},{"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":17,"publishedAt":23,"linkOnly":24,"markdownContent":25,"markdownBody":26,"frontMatter":27,"refName":28,"commitSha":29,"skillDirectory":30,"forksCount":31,"repositoryHtmlUrl":32,"repositoryHomepageUrl":6,"ownerHtmlUrl":33,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":34,"install":35,"resources":48},"01M6CBSFDTGY477MHJD4GSKFRJ","cpu-profiling",null,"Profile CPU usage to identify hot spots and bottlenecks. Optimize code paths consuming most CPU time for better performance and resource efficiency.","ray-manaloto\u002Fmacos-development-environment","ray-manaloto","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F227192835?v=4","01M6CBR7J13ZA01X5YB1A1QRJP","01M6CBR7P0S3NS8ZV81JYNST14",1,"MIT","https:\u002F\u002Fgithub.com\u002Fray-manaloto\u002Fmacos-development-environment\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fcpu-profiling\u002FSKILL.md",[],[18,19,20,21,22],"profiling","cpu profiling","performance","code","profile","2026-11-10 10:12:30",false,"---\nname: cpu-profiling\ndescription: Profile CPU usage to identify hot spots and bottlenecks. Optimize code paths consuming most CPU time for better performance and resource efficiency.\n---\n\n# CPU Profiling\n\n## Overview\n\nCPU profiling identifies which functions consume most CPU time, enabling targeted optimization of expensive code paths.\n\n## When to Use\n\n- High CPU usage\n- Slow execution\n- Performance regression\n- Before optimization\n- Production monitoring\n\n## Instructions\n\n### 1. **Profiling Tools**\n\n```yaml\nBrowser Profiling:\n\nChrome DevTools:\n  Steps:\n    1. DevTools → Performance\n    2. Click record\n    3. Perform action\n    4. Stop recording\n    5. Analyze flame chart\n  Metrics:\n    - Function call duration\n    - Call frequency\n    - Total time vs self time\n\nFirefox Profiler:\n  - Built-in performance profiler\n  - Flame graphs\n  - Timeline view\n  - Export and share\n\nReact Profiler:\n  - DevTools → Profiler\n  - Component render times\n  - Phase: render vs commit\n  - Why component re-rendered\n\n---\n\nNode.js Profiling:\n\nnode --prof app.js\nnode --prof-process isolate-*.log > profile.txt\n\nClinic.js:\n  clinic doctor -- node app.js\n  clinic flame -- node app.js\n  Shows: functions, memory, delays\n\nV8 Inspector:\n  node --inspect app.js\n  Open chrome:\u002F\u002Finspect\n  Profiler tab\n  Take CPU profile\n```\n\n### 2. **Analysis & Interpretation**\n\n```javascript\n\u002F\u002F Understanding profiles\n\nFlame Graph Reading:\n- Wider = more time spent\n- Taller = deeper call stack\n- Hot path = wide tall bars\n- Idle = gaps\n\nSelf Time vs Total Time:\n- Self: time in function itself\n- Total: self + children\n- Example:\n  main() calls work() for 1s\n  work() itself = 0.5s (self)\n  work() itself + children = 1s (total)\n\nHot Spots Identification:\n- Find widest bars (most time)\n- Check if avoidable\n- Check if optimizable\n- Profile before\u002Fafter changes\n\nExample (V8 Analysis):\nFunction: dataProcessing\n  Self time: 500ms (50%)\n  Total time: 1000ms\n  Calls: 1000 times\n  Time per call: 0.5ms\n  Optimization: Reduce call frequency\n```\n\n### 3. **Optimization Process**\n\n```yaml\nSteps:\n\n1. Establish Baseline\n  - Profile current behavior\n  - Note hottest functions\n  - Record total time\n  - Check system resources\n\n2. Identify Bottlenecks\n  - Find top 5 time consumers\n  - Analyze call frequency\n  - Understand what they do\n  - Check if necessary\n\n3. Create Hypothesis\n  - Why is function slow?\n  - Can algorithm improve?\n  - Can we cache results?\n  - Can we parallelize?\n\n4. Implement Changes\n  - Single change at a time\n  - Measure impact\n  - Profile after change\n  - Compare flame graphs\n\n5. Verify Improvement\n  - Baseline: 1s\n  - After optimization: 500ms\n  - Confirmed 50% improvement\n\n---\n\nCommon Optimizations:\n\nAlgorithm Improvement:\n  Before: O(n²) nested loop = 100ms for 1000 items\n  After: O(n log n) with sort+search = 10ms\n  Impact: 10x faster\n\nCaching:\n  Before: Recalculate each call\n  After: Cache result, return instantly\n  Impact: 1000x faster for repeated calls\n\nMemoization:\n  Before: fib(40) recalculates each branch\n  After: Cache computed values\n  Impact: Exponential to linear\n\nLazy Evaluation:\n  Before: Calculate all values upfront\n  After: Calculate only needed values\n  Impact: 90%+ reduction for partial results\n\nParallelization:\n  Before: Sequential processing, 1000ms\n  After: 4 cores, 250ms\n  Impact: 4x faster (8 cores = 8x)\n```\n\n### 4. **Monitoring & Best Practices**\n\n```yaml\nMonitoring:\n\nProduction Profiling:\n  - Lightweight sampling profiler\n  - 1-5% overhead typical\n  - Tools: New Relic, DataDog, Clinic\n  - Alert on CPU spikes\n\nKey Metrics:\n  - CPU usage % per function\n  - Call frequency\n  - Time per call\n  - GC pause times\n  - P95\u002FP99 latency\n\n---\n\nBest Practices:\n\nBefore Optimizing:\n  [ ] Profile to find actual bottleneck\n  [ ] Don't guess (verify with data)\n  [ ] Establish baseline\n  [ ] Measure improvement\n\nDuring Optimization:\n  [ ] Change one thing at a time\n  [ ] Profile after each change\n  [ ] Verify improvement\n  [ ] Don't prematurely optimize\n\nPremature Optimization:\n  - Profile first\n  - Hot path only (80\u002F20 rule)\n  - Measure impact\n  - Consider readability\n\n---\n\nTools Summary:\n\nFramework: Chrome DevTools, Firefox, Node Profiler\nAnalysis: Flame graphs, Call trees, Timeline\nMonitoring: APM tools, Clinic.js\nComparison: Before\u002Fafter profiles\n\n---\n\nRed Flags:\n\n- Unexpected high CPU\n- GC pauses >100ms\n- Function called 1M times per request\n- Deep call stacks\n- Synchronous I\u002FO in loops\n- Repeated calculations\n- Memory allocation in hot loop\n```\n\n## Key Points\n\n- Profile before optimizing (measure, not guess)\n- Look for wide\u002Ftall bars in flame graphs\n- Distinguish self time vs total time\n- Optimize top bottlenecks first\n- Verify improvements with measurement\n- Consider caching and memoization\n- Use production profiling for real issues\n- Algorithm improvements beat micro-optimizations\n- Measure before and after\n- Focus on hot paths (80\u002F20 rule)\n","# CPU Profiling\n\n## Overview\n\nCPU profiling identifies which functions consume most CPU time, enabling targeted optimization of expensive code paths.\n\n## When to Use\n\n- High CPU usage\n- Slow execution\n- Performance regression\n- Before optimization\n- Production monitoring\n\n## Instructions\n\n### 1. **Profiling Tools**\n\n```yaml\nBrowser Profiling:\n\nChrome DevTools:\n  Steps:\n    1. DevTools → Performance\n    2. Click record\n    3. Perform action\n    4. Stop recording\n    5. Analyze flame chart\n  Metrics:\n    - Function call duration\n    - Call frequency\n    - Total time vs self time\n\nFirefox Profiler:\n  - Built-in performance profiler\n  - Flame graphs\n  - Timeline view\n  - Export and share\n\nReact Profiler:\n  - DevTools → Profiler\n  - Component render times\n  - Phase: render vs commit\n  - Why component re-rendered\n\n---\n\nNode.js Profiling:\n\nnode --prof app.js\nnode --prof-process isolate-*.log > profile.txt\n\nClinic.js:\n  clinic doctor -- node app.js\n  clinic flame -- node app.js\n  Shows: functions, memory, delays\n\nV8 Inspector:\n  node --inspect app.js\n  Open chrome:\u002F\u002Finspect\n  Profiler tab\n  Take CPU profile\n```\n\n### 2. **Analysis & Interpretation**\n\n```javascript\n\u002F\u002F Understanding profiles\n\nFlame Graph Reading:\n- Wider = more time spent\n- Taller = deeper call stack\n- Hot path = wide tall bars\n- Idle = gaps\n\nSelf Time vs Total Time:\n- Self: time in function itself\n- Total: self + children\n- Example:\n  main() calls work() for 1s\n  work() itself = 0.5s (self)\n  work() itself + children = 1s (total)\n\nHot Spots Identification:\n- Find widest bars (most time)\n- Check if avoidable\n- Check if optimizable\n- Profile before\u002Fafter changes\n\nExample (V8 Analysis):\nFunction: dataProcessing\n  Self time: 500ms (50%)\n  Total time: 1000ms\n  Calls: 1000 times\n  Time per call: 0.5ms\n  Optimization: Reduce call frequency\n```\n\n### 3. **Optimization Process**\n\n```yaml\nSteps:\n\n1. Establish Baseline\n  - Profile current behavior\n  - Note hottest functions\n  - Record total time\n  - Check system resources\n\n2. Identify Bottlenecks\n  - Find top 5 time consumers\n  - Analyze call frequency\n  - Understand what they do\n  - Check if necessary\n\n3. Create Hypothesis\n  - Why is function slow?\n  - Can algorithm improve?\n  - Can we cache results?\n  - Can we parallelize?\n\n4. Implement Changes\n  - Single change at a time\n  - Measure impact\n  - Profile after change\n  - Compare flame graphs\n\n5. Verify Improvement\n  - Baseline: 1s\n  - After optimization: 500ms\n  - Confirmed 50% improvement\n\n---\n\nCommon Optimizations:\n\nAlgorithm Improvement:\n  Before: O(n²) nested loop = 100ms for 1000 items\n  After: O(n log n) with sort+search = 10ms\n  Impact: 10x faster\n\nCaching:\n  Before: Recalculate each call\n  After: Cache result, return instantly\n  Impact: 1000x faster for repeated calls\n\nMemoization:\n  Before: fib(40) recalculates each branch\n  After: Cache computed values\n  Impact: Exponential to linear\n\nLazy Evaluation:\n  Before: Calculate all values upfront\n  After: Calculate only needed values\n  Impact: 90%+ reduction for partial results\n\nParallelization:\n  Before: Sequential processing, 1000ms\n  After: 4 cores, 250ms\n  Impact: 4x faster (8 cores = 8x)\n```\n\n### 4. **Monitoring & Best Practices**\n\n```yaml\nMonitoring:\n\nProduction Profiling:\n  - Lightweight sampling profiler\n  - 1-5% overhead typical\n  - Tools: New Relic, DataDog, Clinic\n  - Alert on CPU spikes\n\nKey Metrics:\n  - CPU usage % per function\n  - Call frequency\n  - Time per call\n  - GC pause times\n  - P95\u002FP99 latency\n\n---\n\nBest Practices:\n\nBefore Optimizing:\n  [ ] Profile to find actual bottleneck\n  [ ] Don't guess (verify with data)\n  [ ] Establish baseline\n  [ ] Measure improvement\n\nDuring Optimization:\n  [ ] Change one thing at a time\n  [ ] Profile after each change\n  [ ] Verify improvement\n  [ ] Don't prematurely optimize\n\nPremature Optimization:\n  - Profile first\n  - Hot path only (80\u002F20 rule)\n  - Measure impact\n  - Consider readability\n\n---\n\nTools Summary:\n\nFramework: Chrome DevTools, Firefox, Node Profiler\nAnalysis: Flame graphs, Call trees, Timeline\nMonitoring: APM tools, Clinic.js\nComparison: Before\u002Fafter profiles\n\n---\n\nRed Flags:\n\n- Unexpected high CPU\n- GC pauses >100ms\n- Function called 1M times per request\n- Deep call stacks\n- Synchronous I\u002FO in loops\n- Repeated calculations\n- Memory allocation in hot loop\n```\n\n## Key Points\n\n- Profile before optimizing (measure, not guess)\n- Look for wide\u002Ftall bars in flame graphs\n- Distinguish self time vs total time\n- Optimize top bottlenecks first\n- Verify improvements with measurement\n- Consider caching and memoization\n- Use production profiling for real issues\n- Algorithm improvements beat micro-optimizations\n- Measure before and after\n- Focus on hot paths (80\u002F20 rule)\n",{"name":5,"description":7},"main","716b17df3ef6719d88aeac180015008b7ddfff6f",".agents\u002Fskills\u002Fcpu-profiling",0,"https:\u002F\u002Fgithub.com\u002Fray-manaloto\u002Fmacos-development-environment","https:\u002F\u002Fgithub.com\u002Fray-manaloto","2026-08-03 16:55:50",{"source":8,"skillName":5,"commands":36,"prompt":46,"zipAvailable":47},[37,40,43],{"tool":38,"command":39},"npx","npx skills add ray-manaloto\u002Fmacos-development-environment --skill cpu-profiling",{"tool":41,"command":42},"bunx","bunx skills add ray-manaloto\u002Fmacos-development-environment --skill cpu-profiling",{"tool":44,"command":45},"pnpm","pnpm dlx skills add ray-manaloto\u002Fmacos-development-environment --skill cpu-profiling","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fray-manaloto\u002Fmacos-development-environment\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fcpu-profiling\u002FSKILL.md",true,[49,55],{"path":50,"resourceType":51,"resourceTypeDesc":52,"sizeBytes":53,"sourceHtmlUrl":54},"SOURCE.md","REFERENCE","参考文档",188,"https:\u002F\u002Fgithub.com\u002Fjiayaoqijia\u002Fcryptoskill\u002Fblob\u002Fmain\u002Fskills\u002Fdefi\u002Fconvex-yield-agent\u002FSOURCE.md",{"path":56,"resourceType":51,"resourceTypeDesc":52,"sizeBytes":57,"sourceHtmlUrl":58},"TRUST.auto.yaml",2217,"https:\u002F\u002Fgithub.com\u002Fjiayaoqijia\u002Fcryptoskill\u002Fblob\u002Fmain\u002Fskills\u002Fdefi\u002Fconvex-yield-agent\u002FTRUST.auto.yaml",[60,83,101,120,140,154],{"skillNo":61,"name":62,"nameZh":6,"description":63,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":64,"categories":65,"keywords":71,"publishedAt":23},"01M6CC8DXNJ39YWERYTWCGGCGG","next-workflow-step","Use this skill when the user wants to transition to the next workflow step, wrap up a session cleanly, prepare a handoff, or says things like \"next step\", \"continue the workflow\", \"wrap up\", \"hand off\", \"move to the next phase\", \"what's next in embrace\", or \"prepare for \u002Fclear\". Also triggers when the current octo:embrace phase appears complete and no explicit next action is set.","https:\u002F\u002Fgithub.com\u002Fray-manaloto\u002Fmacos-development-environment\u002Fblob\u002Fmain\u002Frsm-subagents\u002Fplugins\u002Fworkflow-toolkit\u002Fskills\u002Fnext-workflow-step\u002FSKILL.md",[66],{"categoryNo":67,"categoryName":68,"taxonomyType":69,"taxonomyTypeDesc":70},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[72,73,74,75,76,77,78,79,80,81,82],"next workflow step","workflow","up","use","session","user","wrap up","continue","handoff","prepare","SET",{"skillNo":84,"name":85,"nameZh":6,"description":86,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":87,"categories":88,"keywords":89,"publishedAt":23},"01M6CC8BRZBQHEBXGMKPDM422P","source-discovery","This skill should be used when the user asks to \"find sources\", \"discover URLs\", \"catalog a source\", \"classify a URL\", \"search for libraries\", \"check what exists\", or mentions source discovery, URL cataloging, source classification, or research source management.","https:\u002F\u002Fgithub.com\u002Fray-manaloto\u002Fmacos-development-environment\u002Fblob\u002Fmain\u002Frsm-subagents\u002Fplugins\u002Fresearch-review-toolkit\u002Fskills\u002Fsource-discovery\u002FSKILL.md",[],[90,91,92,93,94,95,96,97,98,77,99,100],"discovery","source discovery","research","libraries","search","classification","find","check","management","catalog","find sources",{"skillNo":102,"name":103,"nameZh":6,"description":104,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":105,"categories":106,"keywords":107,"publishedAt":23},"01M6CC89CGJP69A0PS22G2H1FV","review-synthesis","This skill should be used when the user asks to \"summarize review findings\", \"prioritize fixes\", \"rank review issues\", \"combine reviewer feedback\", \"create action items from review\", or mentions review synthesis, finding prioritization, severity ranking, or review result summarization.","https:\u002F\u002Fgithub.com\u002Fray-manaloto\u002Fmacos-development-environment\u002Fblob\u002Fmain\u002Frsm-subagents\u002Fplugins\u002Fresearch-review-toolkit\u002Fskills\u002Freview-synthesis\u002FSKILL.md",[],[108,109,110,111,112,113,114,77,115,116,117,118,119],"review","review synthesis","Synthesis","prioritize","create action","feedback","reviewer","summarize","prioritization","create","issues","severity",{"skillNo":121,"name":122,"nameZh":6,"description":123,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":124,"categories":125,"keywords":129,"publishedAt":23},"01M6CC872EZ9GRJY0HRCG0RT2M","research-scoring","This skill should be used when the user asks to \"score research\", \"check research quality\", \"evaluate coverage\", \"measure improvement\", \"assess research completeness\", or mentions research scoring, quality metrics, coverage gaps, improvement baseline, or research pipeline status.","https:\u002F\u002Fgithub.com\u002Fray-manaloto\u002Fmacos-development-environment\u002Fblob\u002Fmain\u002Frsm-subagents\u002Fplugins\u002Fresearch-review-toolkit\u002Fskills\u002Fresearch-scoring\u002FSKILL.md",[126],{"categoryNo":127,"categoryName":128,"taxonomyType":69,"taxonomyTypeDesc":70},"01JBSEEDCAT000000000000021","资料检索与研究",[130,92,131,132,133,134,135,136,97,77,137,138,139],"research scoring","status","coverage gaps","metrics","pipeline","baseline","quality","coverage","pipeline status","research quality",{"skillNo":141,"name":142,"nameZh":6,"description":143,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":144,"categories":145,"keywords":146,"publishedAt":23},"01M6CC852HSDS1FE4EJAT0RHGR","notebooklm-integration","This skill should be used when the user asks to \"ingest into NotebookLM\", \"query NotebookLM\", \"add sources to notebook\", \"cross-source synthesis\", \"check stale sources\", or mentions NotebookLM, notebook integration, source ingestion, or knowledge consolidation.","https:\u002F\u002Fgithub.com\u002Fray-manaloto\u002Fmacos-development-environment\u002Fblob\u002Fmain\u002Frsm-subagents\u002Fplugins\u002Fresearch-review-toolkit\u002Fskills\u002Fnotebooklm-integration\u002FSKILL.md",[],[147,148,149,150,97,77,151,152,153,110],"notebooklm integration","notebooklm","integration","knowledge","ingestion","ingest","stale",{"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":161,"publishedAt":23},"01M6CC82Z4V3205KCJWQZPS2VG","evidence-synthesis","This skill should be used when the user asks to \"synthesize findings\", \"combine research\", \"cross-reference sources\", \"summarize evidence\", \"write a deep review\", or mentions evidence synthesis, finding consolidation, cross-source analysis, or research summarization.","https:\u002F\u002Fgithub.com\u002Fray-manaloto\u002Fmacos-development-environment\u002Fblob\u002Fmain\u002Frsm-subagents\u002Fplugins\u002Fresearch-review-toolkit\u002Fskills\u002Fevidence-synthesis\u002FSKILL.md",[160],{"categoryNo":127,"categoryName":128,"taxonomyType":69,"taxonomyTypeDesc":70},[162,110,163,115,92,164,165,166,77,167,108],"evidence synthesis","evidence","deep review","analysis","write","reference"]