[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6DS71DCSTWMKM0X020N6AC5":3,"skill-same:01M6DS71DCSTWMKM0X020N6AC5":59},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":21,"linkOnly":22,"markdownContent":23,"markdownBody":24,"frontMatter":25,"refName":26,"commitSha":27,"skillDirectory":28,"forksCount":29,"repositoryHtmlUrl":30,"repositoryHomepageUrl":6,"ownerHtmlUrl":31,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":32,"install":33,"resources":46},"01M6DS71DCSTWMKM0X020N6AC5","performance-profiler",null,"Performance Profiler","zhangzhang-111-i\u002Fclaude-skills111","zhangzhang-111-i","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F251344927?v=4","01M6DRBP4NCM1ZCHTT0FW0SQHD","lixiying","01M6DRBPAMFDJSGQ69XPK9KM9J",3,"MIT","https:\u002F\u002Fgithub.com\u002Fzhangzhang-111-i\u002Fclaude-skills111\u002Fblob\u002Fmain\u002Fengineering\u002Fperformance-profiler\u002FSKILL.md",[],[19,20],"performance","performance profiler","2026-11-10 10:12:30",false,"---\nname: \"performance-profiler\"\ndescription: \"Performance Profiler\"\n---\n\n# Performance Profiler\n\n**Tier:** POWERFUL  \n**Category:** Engineering  \n**Domain:** Performance Engineering  \n\n---\n\n## Overview\n\nSystematic performance profiling for Node.js, Python, and Go applications. Identifies CPU, memory, and I\u002FO bottlenecks; generates flamegraphs; analyzes bundle sizes; optimizes database queries; detects memory leaks; and runs load tests with k6 and Artillery. Always measures before and after.\n\n## Core Capabilities\n\n- **CPU profiling** — flamegraphs for Node.js, py-spy for Python, pprof for Go\n- **Memory profiling** — heap snapshots, leak detection, GC pressure\n- **Bundle analysis** — webpack-bundle-analyzer, Next.js bundle analyzer\n- **Database optimization** — EXPLAIN ANALYZE, slow query log, N+1 detection\n- **Load testing** — k6 scripts, Artillery scenarios, ramp-up patterns\n- **Before\u002Fafter measurement** — establish baseline, profile, optimize, verify\n\n---\n\n## When to Use\n\n- App is slow and you don't know where the bottleneck is\n- P99 latency exceeds SLA before a release\n- Memory usage grows over time (suspected leak)\n- Bundle size increased after adding dependencies\n- Preparing for a traffic spike (load test before launch)\n- Database queries taking >100ms\n\n---\n\n## Quick Start\n\n```bash\n# Analyze a project for performance risk indicators\npython3 scripts\u002Fperformance_profiler.py \u002Fpath\u002Fto\u002Fproject\n\n# JSON output for CI integration\npython3 scripts\u002Fperformance_profiler.py \u002Fpath\u002Fto\u002Fproject --json\n\n# Custom large-file threshold\npython3 scripts\u002Fperformance_profiler.py \u002Fpath\u002Fto\u002Fproject --large-file-threshold-kb 256\n```\n\n---\n\n## Golden Rule: Measure First\n\n```bash\n# Establish baseline BEFORE any optimization\n# Record: P50, P95, P99 latency | RPS | error rate | memory usage\n\n# Wrong: \"I think the N+1 query is slow, let me fix it\"\n# Right: Profile → confirm bottleneck → fix → measure again → verify improvement\n```\n\n---\n\n## Node.js Profiling\n→ See references\u002Fprofiling-recipes.md for details\n\n## Before\u002FAfter Measurement Template\n\n```markdown\n## Performance Optimization: [What You Fixed]\n\n**Date:** 2026-03-01  \n**Engineer:** @username  \n**Ticket:** PROJ-123  \n\n### Problem\n[1-2 sentences: what was slow, how was it observed]\n\n### Root Cause\n[What the profiler revealed]\n\n### Baseline (Before)\n| Metric | Value |\n|--------|-------|\n| P50 latency | 480ms |\n| P95 latency | 1,240ms |\n| P99 latency | 3,100ms |\n| RPS @ 50 VUs | 42 |\n| Error rate | 0.8% |\n| DB queries\u002Freq | 23 (N+1) |\n\nProfiler evidence: [link to flamegraph or screenshot]\n\n### Fix Applied\n[What changed — code diff or description]\n\n### After\n| Metric | Before | After | Delta |\n|--------|--------|-------|-------|\n| P50 latency | 480ms | 48ms | -90% |\n| P95 latency | 1,240ms | 120ms | -90% |\n| P99 latency | 3,100ms | 280ms | -91% |\n| RPS @ 50 VUs | 42 | 380 | +804% |\n| Error rate | 0.8% | 0% | -100% |\n| DB queries\u002Freq | 23 | 1 | -96% |\n\n### Verification\nLoad test run: [link to k6 output]\n```\n\n---\n\n## Optimization Checklist\n\n### Quick wins (check these first)\n\n```\nDatabase\n□ Missing indexes on WHERE\u002FORDER BY columns\n□ N+1 queries (check query count per request)\n□ Loading all columns when only 2-3 needed (SELECT *)\n□ No LIMIT on unbounded queries\n□ Missing connection pool (creating new connection per request)\n\nNode.js\n□ Sync I\u002FO (fs.readFileSync) in hot path\n□ JSON.parse\u002Fstringify of large objects in hot loop\n□ Missing caching for expensive computations\n□ No compression (gzip\u002Fbrotli) on responses\n□ Dependencies loaded in request handler (move to module level)\n\nBundle\n□ Moment.js → dayjs\u002Fdate-fns\n□ Lodash (full) → lodash\u002Ffunction imports\n□ Static imports of heavy components → dynamic imports\n□ Images not optimized \u002F not using next\u002Fimage\n□ No code splitting on routes\n\nAPI\n□ No pagination on list endpoints\n□ No response caching (Cache-Control headers)\n□ Serial awaits that could be parallel (Promise.all)\n□ Fetching related data in a loop instead of JOIN\n```\n\n---\n\n## Common Pitfalls\n\n- **Optimizing without measuring** — you'll optimize the wrong thing\n- **Testing in development** — profile against production-like data volumes\n- **Ignoring P99** — P50 can look fine while P99 is catastrophic\n- **Premature optimization** — fix correctness first, then performance\n- **Not re-measuring** — always verify the fix actually improved things\n- **Load testing production** — use staging with production-size data\n\n---\n\n## Best Practices\n\n1. **Baseline first, always** — record metrics before touching anything\n2. **One change at a time** — isolate the variable to confirm causation\n3. **Profile with realistic data** — 10 rows in dev, millions in prod — different bottlenecks\n4. **Set performance budgets** — `p(95) \u003C 200ms` in CI thresholds with k6\n5. **Monitor continuously** — add Datadog\u002FPrometheus metrics for key paths\n6. **Cache invalidation strategy** — cache aggressively, invalidate precisely\n7. **Document the win** — before\u002Fafter in the PR description motivates the team\n","# Performance Profiler\n\n**Tier:** POWERFUL  \n**Category:** Engineering  \n**Domain:** Performance Engineering  \n\n---\n\n## Overview\n\nSystematic performance profiling for Node.js, Python, and Go applications. Identifies CPU, memory, and I\u002FO bottlenecks; generates flamegraphs; analyzes bundle sizes; optimizes database queries; detects memory leaks; and runs load tests with k6 and Artillery. Always measures before and after.\n\n## Core Capabilities\n\n- **CPU profiling** — flamegraphs for Node.js, py-spy for Python, pprof for Go\n- **Memory profiling** — heap snapshots, leak detection, GC pressure\n- **Bundle analysis** — webpack-bundle-analyzer, Next.js bundle analyzer\n- **Database optimization** — EXPLAIN ANALYZE, slow query log, N+1 detection\n- **Load testing** — k6 scripts, Artillery scenarios, ramp-up patterns\n- **Before\u002Fafter measurement** — establish baseline, profile, optimize, verify\n\n---\n\n## When to Use\n\n- App is slow and you don't know where the bottleneck is\n- P99 latency exceeds SLA before a release\n- Memory usage grows over time (suspected leak)\n- Bundle size increased after adding dependencies\n- Preparing for a traffic spike (load test before launch)\n- Database queries taking >100ms\n\n---\n\n## Quick Start\n\n```bash\n# Analyze a project for performance risk indicators\npython3 scripts\u002Fperformance_profiler.py \u002Fpath\u002Fto\u002Fproject\n\n# JSON output for CI integration\npython3 scripts\u002Fperformance_profiler.py \u002Fpath\u002Fto\u002Fproject --json\n\n# Custom large-file threshold\npython3 scripts\u002Fperformance_profiler.py \u002Fpath\u002Fto\u002Fproject --large-file-threshold-kb 256\n```\n\n---\n\n## Golden Rule: Measure First\n\n```bash\n# Establish baseline BEFORE any optimization\n# Record: P50, P95, P99 latency | RPS | error rate | memory usage\n\n# Wrong: \"I think the N+1 query is slow, let me fix it\"\n# Right: Profile → confirm bottleneck → fix → measure again → verify improvement\n```\n\n---\n\n## Node.js Profiling\n→ See references\u002Fprofiling-recipes.md for details\n\n## Before\u002FAfter Measurement Template\n\n```markdown\n## Performance Optimization: [What You Fixed]\n\n**Date:** 2026-03-01  \n**Engineer:** @username  \n**Ticket:** PROJ-123  \n\n### Problem\n[1-2 sentences: what was slow, how was it observed]\n\n### Root Cause\n[What the profiler revealed]\n\n### Baseline (Before)\n| Metric | Value |\n|--------|-------|\n| P50 latency | 480ms |\n| P95 latency | 1,240ms |\n| P99 latency | 3,100ms |\n| RPS @ 50 VUs | 42 |\n| Error rate | 0.8% |\n| DB queries\u002Freq | 23 (N+1) |\n\nProfiler evidence: [link to flamegraph or screenshot]\n\n### Fix Applied\n[What changed — code diff or description]\n\n### After\n| Metric | Before | After | Delta |\n|--------|--------|-------|-------|\n| P50 latency | 480ms | 48ms | -90% |\n| P95 latency | 1,240ms | 120ms | -90% |\n| P99 latency | 3,100ms | 280ms | -91% |\n| RPS @ 50 VUs | 42 | 380 | +804% |\n| Error rate | 0.8% | 0% | -100% |\n| DB queries\u002Freq | 23 | 1 | -96% |\n\n### Verification\nLoad test run: [link to k6 output]\n```\n\n---\n\n## Optimization Checklist\n\n### Quick wins (check these first)\n\n```\nDatabase\n□ Missing indexes on WHERE\u002FORDER BY columns\n□ N+1 queries (check query count per request)\n□ Loading all columns when only 2-3 needed (SELECT *)\n□ No LIMIT on unbounded queries\n□ Missing connection pool (creating new connection per request)\n\nNode.js\n□ Sync I\u002FO (fs.readFileSync) in hot path\n□ JSON.parse\u002Fstringify of large objects in hot loop\n□ Missing caching for expensive computations\n□ No compression (gzip\u002Fbrotli) on responses\n□ Dependencies loaded in request handler (move to module level)\n\nBundle\n□ Moment.js → dayjs\u002Fdate-fns\n□ Lodash (full) → lodash\u002Ffunction imports\n□ Static imports of heavy components → dynamic imports\n□ Images not optimized \u002F not using next\u002Fimage\n□ No code splitting on routes\n\nAPI\n□ No pagination on list endpoints\n□ No response caching (Cache-Control headers)\n□ Serial awaits that could be parallel (Promise.all)\n□ Fetching related data in a loop instead of JOIN\n```\n\n---\n\n## Common Pitfalls\n\n- **Optimizing without measuring** — you'll optimize the wrong thing\n- **Testing in development** — profile against production-like data volumes\n- **Ignoring P99** — P50 can look fine while P99 is catastrophic\n- **Premature optimization** — fix correctness first, then performance\n- **Not re-measuring** — always verify the fix actually improved things\n- **Load testing production** — use staging with production-size data\n\n---\n\n## Best Practices\n\n1. **Baseline first, always** — record metrics before touching anything\n2. **One change at a time** — isolate the variable to confirm causation\n3. **Profile with realistic data** — 10 rows in dev, millions in prod — different bottlenecks\n4. **Set performance budgets** — `p(95) \u003C 200ms` in CI thresholds with k6\n5. **Monitor continuously** — add Datadog\u002FPrometheus metrics for key paths\n6. **Cache invalidation strategy** — cache aggressively, invalidate precisely\n7. **Document the win** — before\u002Fafter in the PR description motivates the 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device risk management specialist implementing ISO 14971 throughout product lifecycle. Provides risk analysis, risk evaluation, risk control, and post-production information analysis. Use when user mentions risk management, ISO 14971, risk analysis, FMEA, fault tree analysis, hazard identification, risk control, risk matrix, benefit-risk analysis, residual risk, risk acceptability, or post-market risk.","https:\u002F\u002Fgithub.com\u002Fzhangzhang-111-i\u002Fclaude-skills111\u002Fblob\u002Fmain\u002Fra-qm-team\u002Frisk-management-specialist\u002FSKILL.md",[],[67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86],"risk","risk management specialist","management","risk management","risk analysis","risk evaluation","product","Evaluation","Production","lifecycle","analysis","user","use","risk matrix","Market","device","control","iso","medical","iso 14971",{"skillNo":88,"name":89,"nameZh":6,"description":90,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":91,"categories":92,"keywords":93,"publishedAt":21},"01M6DSNSRBGVZWS23TK0ZQ52QC","regulatory-affairs-head","Senior Regulatory Affairs Manager for HealthTech and MedTech companies. Prepares FDA 510(k), De Novo, and PMA submission packages; analyzes regulatory pathways for new medical devices; drafts responses to FDA deficiency letters and Notified Body queries; develops CE marking technical documentation under EU MDR 2017\u002F745; coordinates multi-market approval strategies across FDA, EU, Health Canada, PMDA, and NMPA; and maintains regulatory intelligence on evolving standards. Use when users need to plan or execute FDA submissions, navigate 510(k) or PMA approval processes, achieve CE marking, prepare pre-submission meeting materials, write regulatory strategy documents, respond to agency queries, or manage compliance documentation for medical device market access.","https:\u002F\u002Fgithub.com\u002Fzhangzhang-111-i\u002Fclaude-skills111\u002Fblob\u002Fmain\u002Fra-qm-team\u002Fregulatory-affairs-head\u002FSKILL.md",[],[94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,85,112],"regulatory affairs head","regulatory","head","documentation","documents","packages","planning","devices","plan","access","write","strategy","evidence","standards","manage","intelligence","across","strategies","clinical",{"skillNo":114,"name":115,"nameZh":6,"description":116,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":117,"categories":118,"keywords":129,"publishedAt":21},"01M6DSNN7GNR2HZE66S4D31EP8","quality-manager-qms-iso13485","ISO 13485 Quality Management System implementation and maintenance for medical device organizations. Provides QMS design, documentation control, internal auditing, CAPA management, and certification support. Use when working with medical device quality systems, preparing for ISO 13485 audits, managing regulatory compliance documentation, setting up corrective actions, or building audit preparation programs. Useful for quality management, audit preparation, regulatory compliance, medical device documentation, and corrective action workflows.","https:\u002F\u002Fgithub.com\u002Fzhangzhang-111-i\u002Fclaude-skills111\u002Fblob\u002Fmain\u002Fra-qm-team\u002Fquality-manager-qms-iso13485\u002FSKILL.md",[119,124],{"categoryNo":120,"categoryName":121,"taxonomyType":122,"taxonomyTypeDesc":123},"01JBSEEDCAT000000000000015","视觉设计","SCENARIO","使用场景",{"categoryNo":125,"categoryName":126,"taxonomyType":127,"taxonomyTypeDesc":128},"01JBSEEDCAT000000000000032","设计师","PROFESSION","适用职业",[130,131,132,133,134,135,97,136,137,138,139,140,79,95,141,142,143,83,84,85],"quality manager qms iso13485","quality","design","review","support","workflows","audit","actions","compliance","system","process","up","maintenance","document",{"skillNo":145,"name":146,"nameZh":6,"description":147,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":148,"categories":149,"keywords":153,"publishedAt":21},"01M6DSNE5PQM106PM4EDGPBWGZ","quality-manager-qmr","Senior Quality Manager Responsible Person (QMR) for HealthTech and MedTech companies. Provides quality system governance, management review leadership, regulatory compliance oversight, and quality performance monitoring per ISO 13485 Clause 5.5.2.","https:\u002F\u002Fgithub.com\u002Fzhangzhang-111-i\u002Fclaude-skills111\u002Fblob\u002Fmain\u002Fra-qm-team\u002Fquality-manager-qmr\u002FSKILL.md",[150],{"categoryNo":151,"categoryName":152,"taxonomyType":127,"taxonomyTypeDesc":128},"01JBSEEDCAT000000000000037","律师与法务",[154,131,133,155,19,156,157,138,158,139,69,159,95,160,161,162,84],"quality manager qmr","policy","governance","monitoring","regulatory compliance","oversight","cost","culture","leadership",{"skillNo":164,"name":165,"nameZh":6,"description":166,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":167,"categories":168,"keywords":173,"publishedAt":21},"01M6DSN7MYXB6X42TB1GH2TNSB","quality-documentation-manager","Document control system management for medical device QMS. Covers document numbering, version control, change management, and 21 CFR Part 11 compliance. Use for document control procedures, change control workflow, document numbering, version management, electronic signature compliance, or regulatory documentation review.","https:\u002F\u002Fgithub.com\u002Fzhangzhang-111-i\u002Fclaude-skills111\u002Fblob\u002Fmain\u002Fra-qm-team\u002Fquality-documentation-manager\u002FSKILL.md",[169,172],{"categoryNo":170,"categoryName":171,"taxonomyType":122,"taxonomyTypeDesc":123},"01JBSEEDCAT000000000000002","文档处理",{"categoryNo":151,"categoryName":152,"taxonomyType":127,"taxonomyTypeDesc":128},[97,174,131,133,175,176,136,76,177,139,178,179,79,180,181,182,85,183,184,185],"quality documentation manager","workflow","change","document lifecycle","change management","list","master","system management","documentation review","cfr","21 cfr part 11","change control",{"skillNo":187,"name":188,"nameZh":6,"description":189,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":190,"categories":191,"keywords":192,"publishedAt":21},"01M6DSN3R749H3R20K15QE8X8W","qms-audit-expert","ISO 13485 internal audit expertise for medical device QMS. Covers audit planning, execution, nonconformity classification, and CAPA verification. Use for internal audit planning, audit execution, finding classification, external audit preparation, or audit program management.","https:\u002F\u002Fgithub.com\u002Fzhangzhang-111-i\u002Fclaude-skills111\u002Fblob\u002Fmain\u002Fra-qm-team\u002Fqms-audit-expert\u002FSKILL.md",[],[136,193,194,100,195,196,197,198,69,79,199,82,84,85],"qms audit expert","expert","verification","execution","classification","schedule","checklist"]