[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71NH03XFJCCV4Y385FZCCDC":3,"skill-same:01M71NH03XFJCCV4Y385FZCCDC":73},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":17,"publishedAt":38,"linkOnly":39,"markdownContent":40,"markdownBody":41,"frontMatter":42,"refName":51,"commitSha":52,"skillDirectory":53,"forksCount":54,"repositoryHtmlUrl":50,"repositoryHomepageUrl":55,"ownerHtmlUrl":56,"ownerBlogUrl":57,"ownerDescription":6,"updatedAt":58,"install":59,"resources":72},"01M71NH03XFJCCV4Y385FZCCDC","perf-analyzer",null,"Use when an org role acts as perf analyzer and must find where time, memory and coordination overhead go, separating real regressions from noise. Covers baselines, p50-p99 percentiles, cross-layer metrics, flame graphs, anomaly detection and before\u002Fafter re-measurement of fixes.","monoes\u002Fmonomind","monoes","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F276744696?v=4","01M71KWF37AJ2NJBP3KWBVSDGT","01M71KWF4CSF8ADH0M6HA09VD2",23,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fmonoes\u002Fmonomind\u002Fblob\u002Fmain\u002Fpackages\u002F@monomind\u002Fcli\u002Forg-skills\u002Fperf-analyzer\u002FSKILL.md",[],[18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37],"perf","analyzer","perf analyzer","measurement","observability","metrics","context","org","layer","go","monomind","overhead","coordination","query","when","impact","time","re","performance","memory","2026-11-10 10:56:23",false,"---\nname: perf-analyzer\ndescription: \"Use when an org role acts as perf analyzer and must find where time, memory and coordination overhead go, separating real regressions from noise. Covers baselines, p50-p99 percentiles, cross-layer metrics, flame graphs, anomaly detection and before\u002Fafter re-measurement of fixes.\"\ntags: [\"engineering\",\"performance\",\"observability\",\"debug\"]\ntools: [\"monograph_query\",\"monograph_context\",\"monograph_impact\"]\nlicense: Apache-2.0\nsource: https:\u002F\u002Fgithub.com\u002Fmonoes\u002Fmonomind\n---\n# Perf Analyzer — Best Practices\n\n## Focus\nDiagnoses where time, memory, and coordination overhead actually go in a running system — collecting metrics, detecting bottlenecks, and separating real regressions from noise.\n\n## Best practices\n- Always establish a baseline before judging any number — \"slow\" only means something relative to a prior measurement or an SLA.\n- Collect metrics across layers at once (CPU, memory, I\u002FO, network, queue depth, agent-level latency) so correlated causes aren't missed.\n- Use percentiles (p50\u002Fp90\u002Fp95\u002Fp99), not just averages — averages hide the tail latency that actually hurts users.\n- Correlate metrics across time windows before declaring a bottleneck; a single spike is not a pattern.\n- Prioritize findings by business\u002Fuser impact, not by which number is easiest to fix.\n- Distinguish symptom from root cause — e.g. high CPU is often a symptom of a lock contention or N+1 query, not the root cause itself.\n- Re-measure after every proposed fix; never accept a theoretical improvement without a before\u002Fafter comparison.\n- Track recurring bottleneck signatures over time — the same class of issue reappearing is more valuable data than a one-off spike.\n\n## Common pitfalls\n- Optimizing the first hot function found by a profiler instead of the one on the critical path.\n- Reporting raw averages that mask tail latency problems affecting a meaningful subset of requests.\n- Treating a single anomalous measurement as a trend without checking for confounding load or environment changes.\n- Chasing CPU\u002Fmemory numbers in isolation without checking coordination overhead (locking, queueing, cross-agent messaging) in multi-agent or distributed systems.\n- Declaring victory on a fix without a controlled before\u002Fafter comparison under equivalent load.\n\n## Tools & techniques\n- Flame graphs \u002F CPU sampling profilers to find real hotspots, not guessed ones.\n- 3-sigma or statistical-threshold anomaly detection over time series to flag genuine deviations.\n- SLA\u002FSLO-based evaluation (availability, response time, error rate, throughput) rather than raw metric thresholds.\n- Bottleneck pattern signatures (recurring type + component + root cause) tracked over time to catch systemic issues.\n- Resource utilization percentile breakdowns (p50\u002Fp90\u002Fp95\u002Fp99) per component to isolate tail-latency contributors.\n","# Perf Analyzer — Best Practices\n\n## Focus\nDiagnoses where time, memory, and coordination overhead actually go in a running system — collecting metrics, detecting bottlenecks, and separating real regressions from noise.\n\n## Best practices\n- Always establish a baseline before judging any number — \"slow\" only means something relative to a prior measurement or an SLA.\n- Collect metrics across layers at once (CPU, memory, I\u002FO, network, queue depth, agent-level latency) so correlated causes aren't missed.\n- Use percentiles (p50\u002Fp90\u002Fp95\u002Fp99), not just averages — averages hide the tail latency that actually hurts users.\n- Correlate metrics across time windows before declaring a bottleneck; a single spike is not a pattern.\n- Prioritize findings by business\u002Fuser impact, not by which number is easiest to fix.\n- Distinguish symptom from root cause — e.g. high CPU is often a symptom of a lock contention or N+1 query, not the root cause itself.\n- Re-measure after every proposed fix; never accept a theoretical improvement without a before\u002Fafter comparison.\n- Track recurring bottleneck signatures over time — the same class of issue reappearing is more valuable data than a one-off spike.\n\n## Common pitfalls\n- Optimizing the first hot function found by a profiler instead of the one on the critical path.\n- Reporting raw averages that mask tail latency problems affecting a meaningful subset of requests.\n- Treating a single anomalous measurement as a trend without checking for confounding load or environment changes.\n- Chasing CPU\u002Fmemory numbers in isolation without checking coordination overhead (locking, queueing, cross-agent messaging) in multi-agent or distributed systems.\n- Declaring victory on a fix without a controlled before\u002Fafter comparison under equivalent load.\n\n## Tools & techniques\n- Flame graphs \u002F CPU sampling profilers to find real hotspots, not guessed ones.\n- 3-sigma or statistical-threshold anomaly detection over time series to flag genuine deviations.\n- SLA\u002FSLO-based evaluation (availability, response time, error rate, throughput) rather than raw metric thresholds.\n- Bottleneck pattern signatures (recurring type + component + root cause) tracked over time to catch systemic issues.\n- Resource utilization percentile breakdowns (p50\u002Fp90\u002Fp95\u002Fp99) per component to isolate tail-latency contributors.\n",{"name":5,"tags":43,"tools":46,"source":50,"license":14,"description":7},[44,36,22,45],"engineering","debug",[47,48,49],"monograph_query","monograph_context","monograph_impact","https:\u002F\u002Fgithub.com\u002Fmonoes\u002Fmonomind","main","f7cd4595d0f8959822163e14f9787117765c931a","packages\u002F@monomind\u002Fcli\u002Forg-skills\u002Fperf-analyzer",2,"https:\u002F\u002Fmonoes.github.io\u002Fmonomind\u002F","https:\u002F\u002Fgithub.com\u002Fmonoes","https:\u002F\u002Fmonoes.me","2026-11-10 01:53:48",{"source":8,"skillName":5,"commands":60,"prompt":70,"zipAvailable":71},[61,64,67],{"tool":62,"command":63},"npx","npx skills add monoes\u002Fmonomind --skill perf-analyzer",{"tool":65,"command":66},"bunx","bunx skills add monoes\u002Fmonomind --skill perf-analyzer",{"tool":68,"command":69},"pnpm","pnpm dlx skills add monoes\u002Fmonomind --skill perf-analyzer","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fmonoes\u002Fmonomind\u002Fblob\u002Fmain\u002Fpackages\u002F@monomind\u002Fcli\u002Forg-skills\u002Fperf-analyzer\u002FSKILL.md",true,[],[74,98,133,159,184,211],{"skillNo":75,"name":76,"nameZh":6,"description":77,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":78,"categories":79,"keywords":80,"publishedAt":97},"01M71PB3SHWKMNJM6MD65VX8D6","zk-steward","Org role guidance for a ZK steward: engineer zero-knowledge proof systems with circuit design, SNARK vs STARK choice and security review. This is zero-knowledge cryptography, not Zettelkasten note-taking.","https:\u002F\u002Fgithub.com\u002Fmonoes\u002Fmonomind\u002Fblob\u002Fmain\u002Fpackages\u002F@monomind\u002Fcli\u002Forg-skills\u002Fzk-steward\u002FSKILL.md",[],[81,82,83,84,85,86,87,88,33,31,89,90,28,91,92,93,94,95,25,96],"zk","zk steward","steward","zero-knowledge","engineer","choice","design","knowledge","snark","review","security review","stark","zettelkasten","cryptography","security","blockchain","2026-11-10 13:02:02",{"skillNo":99,"name":100,"nameZh":6,"description":101,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":102,"licenseFileUrl":103,"sourceHtmlUrl":104,"categories":105,"keywords":116,"publishedAt":97},"01M71PB18SDG28WBKPKJNB814K","writing-plans","Use when you have a spec or requirements for a multi-step coding task and must write an implementation plan before touching code. Produces bite-sized TDD tasks with exact file paths, code and commands for an engineer with zero context.","MIT","https:\u002F\u002Fgithub.com\u002Fmonoes\u002Fmonomind\u002Fblob\u002Fmain\u002Fpackages\u002F@monomind\u002Fcli\u002Forg-skills\u002Fwriting-plans\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fmonoes\u002Fmonomind\u002Fblob\u002Fmain\u002Fpackages\u002F@monomind\u002Fcli\u002Forg-skills\u002Fwriting-plans\u002FSKILL.md",[106,111],{"categoryNo":107,"categoryName":108,"taxonomyType":109,"taxonomyTypeDesc":110},"01JBSEEDCAT000000000000018","写作与文案","SCENARIO","使用场景",{"categoryNo":112,"categoryName":113,"taxonomyType":114,"taxonomyTypeDesc":115},"01JBSEEDCAT000000000000036","内容创作者","PROFESSION","适用职业",[117,118,119,85,120,121,32,122,31,123,124,125,126,127,128,129,130,131,24,132],"writing","writing plans","plans","superpowers","implementation plan","you","code","tasks","plan","product","write","commands","paths","suggest","coding","ZeRO",{"skillNo":134,"name":135,"nameZh":6,"description":136,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":137,"sourceHtmlUrl":138,"categories":139,"keywords":143,"publishedAt":97},"01M71PAXFM488X9ARXK0T04TF5","write-spec","Write a feature spec or PRD from a problem statement or feature idea. Use when turning a vague idea or user request into a structured document, scoping a feature with goals and non-goals, defining success metrics and acceptance criteria, or breaking a big ask into a phased spec.","https:\u002F\u002Fgithub.com\u002Fmonoes\u002Fmonomind\u002Fblob\u002Fmain\u002Fpackages\u002F@monomind\u002Fcli\u002Forg-skills\u002Fwrite-spec\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fmonoes\u002Fmonomind\u002Fblob\u002Fmain\u002Fpackages\u002F@monomind\u002Fcli\u002Forg-skills\u002Fwrite-spec\u002FSKILL.md",[140],{"categoryNo":141,"categoryName":142,"taxonomyType":114,"taxonomyTypeDesc":115},"01JBSEEDCAT000000000000031","产品经理",[127,144,145,146,147,88,148,32,149,150,151,126,152,153,154,23,155,156,157,158],"write spec","spec","user request","problem","acceptance","ask","vague","work","request","scoping","idea","feature","problem statement","use","document",{"skillNo":160,"name":161,"nameZh":6,"description":162,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":163,"sourceHtmlUrl":164,"categories":165,"keywords":169,"publishedAt":97},"01M71PASSZY800C0RPEARBS918","write-query","Write optimized SQL for your dialect with best practices. Use when translating a natural-language data need into SQL, building a multi-CTE query with joins and aggregations, optimizing a query against a large partitioned table, or getting dialect-specific syntax for Snowflake, BigQuery, Postgres, etc.","https:\u002F\u002Fgithub.com\u002Fmonoes\u002Fmonomind\u002Fblob\u002Fmain\u002Fpackages\u002F@monomind\u002Fcli\u002Forg-skills\u002Fwrite-query\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fmonoes\u002Fmonomind\u002Fblob\u002Fmain\u002Fpackages\u002F@monomind\u002Fcli\u002Forg-skills\u002Fwrite-query\u002FSKILL.md",[166],{"categoryNo":167,"categoryName":168,"taxonomyType":114,"taxonomyTypeDesc":115},"01JBSEEDCAT000000000000033","数据分析师",[31,127,170,171,172,173,88,157,32,174,175,176,177,151,178,179,180,181,182,183],"write query","postgres","for","snowflake","best practices","natural-language","with","multi","github","sql","https","syntax","plugins","data",{"skillNo":185,"name":186,"nameZh":6,"description":187,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":188,"categories":189,"keywords":196,"publishedAt":97},"01M71PAPTX0T3PF6ETFVR96SJJ","workflow-automation","Org role guidance for workflow automation: build and maintain CI\u002FCD definitions in GitHub Actions or equivalent: triggers, jobs, matrices, caching. Covers path filters, dependency caching, parallel jobs, timeouts and fail-fast ordering.","https:\u002F\u002Fgithub.com\u002Fmonoes\u002Fmonomind\u002Fblob\u002Fmain\u002Fpackages\u002F@monomind\u002Fcli\u002Forg-skills\u002Fworkflow-automation\u002FSKILL.md",[190,193],{"categoryNo":191,"categoryName":192,"taxonomyType":109,"taxonomyTypeDesc":110},"01JBSEEDCAT000000000000011","运维与部署",{"categoryNo":194,"categoryName":195,"taxonomyType":109,"taxonomyTypeDesc":110},"01JBSEEDCAT000000000000023","Skill与智能体开发",[197,198,199,200,172,201,202,203,204,33,205,206,207,31,28,178,208,209,210,180],"Workflow Automation","automation","workflow","parallel","actions","ci","triggers","github actions","maintain","filters","caching","build","dependency","devops",{"skillNo":212,"name":213,"nameZh":6,"description":214,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":215,"categories":216,"keywords":218,"publishedAt":97},"01M71PAM19Z1XTV9NPCFSE2MY8","workflow-architect","Org role guidance for a workflow architect: map full workflow trees for systems, user journeys and agents, covering every branch and failure. Covers discovering workflows from code, handoff contracts, timeouts, recovery paths and observable state.","https:\u002F\u002Fgithub.com\u002Fmonoes\u002Fmonomind\u002Fblob\u002Fmain\u002Fpackages\u002F@monomind\u002Fcli\u002Forg-skills\u002Fworkflow-architect\u002FSKILL.md",[217],{"categoryNo":194,"categoryName":195,"taxonomyType":109,"taxonomyTypeDesc":110},[199,219,220,221,222,223,224,172,225,226,123,227,228,229,28,178,230,231,180,232],"workflow architect","architect","trees","user journeys","branch","planning","map","workflows","architecture","state","leadership","recovery","operations","systems"]