[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71HA48K6CA4ZVASF18JB8CE":3,"skill-same:01M71HA48K6CA4ZVASF18JB8CE":113},{"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":75,"commitSha":76,"skillDirectory":77,"forksCount":78,"repositoryHtmlUrl":79,"repositoryHomepageUrl":6,"ownerHtmlUrl":80,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":81,"install":82,"resources":94},"01M71HA48K6CA4ZVASF18JB8CE","code-refinement",null,"Analyze and improve living code quality: duplication, algorithmic efficiency, clean code principles, architectural fit, anti-slop patterns, and error handling robustness. Use when improving code quality, reducing AI slop, refactoring for clarity, optimizing algorithms, applying clean code principles. Do not use when removing dead\u002Funused code (use conserve:bloat-detector). reviewing for bugs (use pensive:bug-review). selecting architecture paradigms (use archetypes skills). This skill actively improves living code, complementing bloat detection (dead code removal) with quality refinement (living code improvement).","JSGforever\u002Fskillguard","JSGforever","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F85541948?v=4","01M71FQD3E132VMR5F4TAYX3YS","01M71FQD4DYW0XJ4XQ4YQH75Y9",2,"GPL-3.0","https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__athola--code-refinement\u002FSKILL.md",[17],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42],"code refinement","refinement","code","review","architecture","analysis","Bash","when","critical","archetypes","unused","improve","clean","anti-slop","slop","advanced","architecture paradigms","error","modules","shared","2026-11-10 10:12:30",false,"---\nname: code-refinement\ndescription: 'Analyze and improve living code quality: duplication, algorithmic efficiency,\n  clean code principles, architectural fit, anti-slop patterns, and error handling\n  robustness. Use when improving code quality, reducing AI slop, refactoring for clarity,\n  optimizing algorithms, applying clean code principles. Do not use when removing\n  dead\u002Funused code (use conserve:bloat-detector). reviewing for bugs (use pensive:bug-review).\n  selecting architecture paradigms (use archetypes skills). This skill actively improves\n  living code, complementing bloat detection (dead code removal) with quality refinement\n  (living code improvement).'\ncategory: code-quality\ntags:\n- refactoring\n- clean-code\n- algorithms\n- duplication\n- anti-slop\n- craft\ntools:\n- Read\n- Grep\n- Glob\n- Bash\nusage_patterns:\n- code-quality-improvement\n- duplication-reduction\n- algorithm-optimization\n- clean-code-enforcement\ncomplexity: advanced\nestimated_tokens: 350\nprogressive_loading: true\ndependencies:\n- pensive:shared\n- pensive:safety-critical-patterns\n- imbue:proof-of-work\nmodules:\n- modules\u002Fduplication-analysis.md\n- modules\u002Falgorithm-efficiency.md\n- modules\u002Fclean-code-checks.md\n- modules\u002Farchitectural-fit.md\n---\n## Table of Contents\n\n- [Quick Start](#quick-start)\n- [When to Use](#when-to-use)\n- [Analysis Dimensions](#analysis-dimensions)\n- [Progressive Loading](#progressive-loading)\n- [Required TodoWrite Items](#required-todowrite-items)\n- [Workflow](#workflow)\n- [Tiered Analysis](#tiered-analysis)\n- [Cross-Plugin Dependencies](#cross-plugin-dependencies)\n\n# Code Refinement Workflow\n\nAnalyze and improve living code quality across six dimensions.\n\n## Quick Start\n\n```bash\n\u002Frefine-code\n\u002Frefine-code --level 2 --focus duplication\n\u002Frefine-code --level 3 --report refinement-plan.md\n```\n\n## When To Use\n\n- After rapid AI-assisted development sprints\n- Before major releases (quality gate)\n- When code \"works but smells\"\n- Refactoring existing modules for clarity\n- Reducing technical debt in living code\n\n## When NOT To Use\n\n- Removing\n  dead\u002Funused code (use conserve:bloat-detector)\n- Removing\n  dead\u002Funused code (use conserve:bloat-detector)\n\n## Analysis Dimensions\n\n| # | Dimension | Module | What It Catches |\n|---|-----------|--------|----------------|\n| 1 | Duplication & Redundancy | `duplication-analysis` | Near-identical blocks, similar functions, copy-paste |\n| 2 | Algorithmic Efficiency | `algorithm-efficiency` | O(n^2) where O(n) works, unnecessary iterations |\n| 3 | Clean Code Violations | `clean-code-checks` | Long methods, deep nesting, poor naming, magic values |\n| 4 | Architectural Fit | `architectural-fit` | Paradigm mismatches, coupling violations, leaky abstractions |\n| 5 | Anti-Slop Patterns | `clean-code-checks` | Premature abstraction, enterprise cosplay, hollow patterns |\n| 6 | Error Handling | `clean-code-checks` | Bare excepts, swallowed errors, happy-path-only |\n\n## Progressive Loading\n\nLoad modules based on refinement focus:\n\n- **`modules\u002Fduplication-analysis.md`** (~400 tokens): Duplication detection and consolidation\n- **`modules\u002Falgorithm-efficiency.md`** (~400 tokens): Complexity analysis and optimization\n- **`modules\u002Fclean-code-checks.md`** (~450 tokens): Clean code, anti-slop, error handling\n- **`modules\u002Farchitectural-fit.md`** (~400 tokens): Paradigm alignment and coupling\n\nLoad all for comprehensive refinement. For focused work, load only relevant modules.\n\n## Required TodoWrite Items\n\n1. `refine:context-established` — Scope, language, framework detection\n2. `refine:scan-complete` — Findings across all dimensions\n3. `refine:prioritized` — Findings ranked by impact and effort\n4. `refine:plan-generated` — Concrete refactoring plan with before\u002Fafter\n5. `refine:evidence-captured` — Evidence appendix per `imbue:proof-of-work`\n\n## Workflow\n\n### Step 1: Establish Context (`refine:context-established`)\n\nDetect project characteristics:\n```bash\n# Language detection\nfind . -name \"*.py\" -o -name \"*.ts\" -o -name \"*.rs\" -o -name \"*.go\" | head -20\n\n# Framework detection\nls package.json pyproject.toml Cargo.toml go.mod 2>\u002Fdev\u002Fnull\n\n# Size assessment\nfind . -name \"*.py\" -o -name \"*.ts\" -o -name \"*.rs\" | xargs wc -l 2>\u002Fdev\u002Fnull | tail -1\n```\n\n### Step 2: Dimensional Scan (`refine:scan-complete`)\n\nLoad relevant modules and execute analysis per tier level.\n\n### Step 3: Prioritize (`refine:prioritized`)\n\nRank findings by:\n- **Impact**: How much quality improves (HIGH\u002FMEDIUM\u002FLOW)\n- **Effort**: Lines changed, files touched (SMALL\u002FMEDIUM\u002FLARGE)\n- **Risk**: Likelihood of introducing bugs (LOW\u002FMEDIUM\u002FHIGH)\n\nPriority = HIGH impact + SMALL effort + LOW risk first.\n\n### Step 4: Generate Plan (`refine:plan-generated`)\n\nFor each finding, produce:\n- File path and line range\n- Current code snippet\n- Proposed improvement\n- Rationale (which principle\u002Fdimension)\n- Estimated effort\n\n### Step 5: Evidence Capture (`refine:evidence-captured`)\n\nDocument with `imbue:proof-of-work` (if available):\n- `[E1]`, `[E2]` references for each finding\n- Metrics before\u002Fafter where measurable\n- Principle violations cited\n\n**Fallback**: If `imbue` is not installed, capture evidence inline in the report using the same `[E1]` reference format without TodoWrite integration.\n\n## Tiered Analysis\n\n| Tier | Time | Scope |\n|------|------|-------|\n| **1: Quick** (default) | 2-5 min | Complexity hotspots, obvious duplication, naming, magic values |\n| **2: Targeted** | 10-20 min | Algorithm analysis, full duplication scan, architectural alignment |\n| **3: Deep** | 30-60 min | All above + cross-module coupling, paradigm fitness, comprehensive plan |\n\n## Cross-Plugin Dependencies\n\n| Dependency | Required? | Fallback |\n|------------|-----------|----------|\n| `pensive:shared` | Yes | Core review patterns |\n| `imbue:proof-of-work` | Optional | Inline evidence in report |\n| `conserve:code-quality-principles` | Optional | Built-in KISS\u002FYAGNI\u002FSOLID checks |\n| `archetypes:architecture-paradigms` | Optional | Principle-based checks only (no paradigm detection) |\n\n## Supporting Modules\n\n- [Code quality analysis](modules\u002Fcode-quality-analysis.md) - duplication detection commands and consolidation strategies\n\nWhen optional plugins are not installed, the skill degrades gracefully:\n- Without `imbue`: Evidence captured inline, no TodoWrite proof-of-work\n- Without `conserve`: Uses built-in clean code checks (subset)\n- Without `archetypes`: Skips paradigm-specific alignment, uses coupling\u002Fcohesion principles only\n","## Table of Contents\n\n- [Quick Start](#quick-start)\n- [When to Use](#when-to-use)\n- [Analysis Dimensions](#analysis-dimensions)\n- [Progressive Loading](#progressive-loading)\n- [Required TodoWrite Items](#required-todowrite-items)\n- [Workflow](#workflow)\n- [Tiered Analysis](#tiered-analysis)\n- [Cross-Plugin Dependencies](#cross-plugin-dependencies)\n\n# Code Refinement Workflow\n\nAnalyze and improve living code quality across six dimensions.\n\n## Quick Start\n\n```bash\n\u002Frefine-code\n\u002Frefine-code --level 2 --focus duplication\n\u002Frefine-code --level 3 --report refinement-plan.md\n```\n\n## When To Use\n\n- After rapid AI-assisted development sprints\n- Before major releases (quality gate)\n- When code \"works but smells\"\n- Refactoring existing modules for clarity\n- Reducing technical debt in living code\n\n## When NOT To Use\n\n- Removing\n  dead\u002Funused code (use conserve:bloat-detector)\n- Removing\n  dead\u002Funused code (use conserve:bloat-detector)\n\n## Analysis Dimensions\n\n| # | Dimension | Module | What It Catches |\n|---|-----------|--------|----------------|\n| 1 | Duplication & Redundancy | `duplication-analysis` | Near-identical blocks, similar functions, copy-paste |\n| 2 | Algorithmic Efficiency | `algorithm-efficiency` | O(n^2) where O(n) works, unnecessary iterations |\n| 3 | Clean Code Violations | `clean-code-checks` | Long methods, deep nesting, poor naming, magic values |\n| 4 | Architectural Fit | `architectural-fit` | Paradigm mismatches, coupling violations, leaky abstractions |\n| 5 | Anti-Slop Patterns | `clean-code-checks` | Premature abstraction, enterprise cosplay, hollow patterns |\n| 6 | Error Handling | `clean-code-checks` | Bare excepts, swallowed errors, happy-path-only |\n\n## Progressive Loading\n\nLoad modules based on refinement focus:\n\n- **`modules\u002Fduplication-analysis.md`** (~400 tokens): Duplication detection and consolidation\n- **`modules\u002Falgorithm-efficiency.md`** (~400 tokens): Complexity analysis and optimization\n- **`modules\u002Fclean-code-checks.md`** (~450 tokens): Clean code, anti-slop, error handling\n- **`modules\u002Farchitectural-fit.md`** (~400 tokens): Paradigm alignment and coupling\n\nLoad all for comprehensive refinement. For focused work, load only relevant modules.\n\n## Required TodoWrite Items\n\n1. `refine:context-established` — Scope, language, framework detection\n2. `refine:scan-complete` — Findings across all dimensions\n3. `refine:prioritized` — Findings ranked by impact and effort\n4. `refine:plan-generated` — Concrete refactoring plan with before\u002Fafter\n5. `refine:evidence-captured` — Evidence appendix per `imbue:proof-of-work`\n\n## Workflow\n\n### Step 1: Establish Context (`refine:context-established`)\n\nDetect project characteristics:\n```bash\n# Language detection\nfind . -name \"*.py\" -o -name \"*.ts\" -o -name \"*.rs\" -o -name \"*.go\" | head -20\n\n# Framework detection\nls package.json pyproject.toml Cargo.toml go.mod 2>\u002Fdev\u002Fnull\n\n# Size assessment\nfind . -name \"*.py\" -o -name \"*.ts\" -o -name \"*.rs\" | xargs wc -l 2>\u002Fdev\u002Fnull | tail -1\n```\n\n### Step 2: Dimensional Scan (`refine:scan-complete`)\n\nLoad relevant modules and execute analysis per tier level.\n\n### Step 3: Prioritize (`refine:prioritized`)\n\nRank findings by:\n- **Impact**: How much quality improves (HIGH\u002FMEDIUM\u002FLOW)\n- **Effort**: Lines changed, files touched (SMALL\u002FMEDIUM\u002FLARGE)\n- **Risk**: Likelihood of introducing bugs (LOW\u002FMEDIUM\u002FHIGH)\n\nPriority = HIGH impact + SMALL effort + LOW risk first.\n\n### Step 4: Generate Plan (`refine:plan-generated`)\n\nFor each finding, produce:\n- File path and line range\n- Current code snippet\n- Proposed improvement\n- Rationale (which principle\u002Fdimension)\n- Estimated effort\n\n### Step 5: Evidence Capture (`refine:evidence-captured`)\n\nDocument with `imbue:proof-of-work` (if available):\n- `[E1]`, `[E2]` references for each finding\n- Metrics before\u002Fafter where measurable\n- Principle violations cited\n\n**Fallback**: If `imbue` is not installed, capture evidence inline in the report using the same `[E1]` reference format without TodoWrite integration.\n\n## Tiered Analysis\n\n| Tier | Time | Scope |\n|------|------|-------|\n| **1: Quick** (default) | 2-5 min | Complexity hotspots, obvious duplication, naming, magic values |\n| **2: Targeted** | 10-20 min | Algorithm analysis, full duplication scan, architectural alignment |\n| **3: Deep** | 30-60 min | All above + cross-module coupling, paradigm fitness, comprehensive plan |\n\n## Cross-Plugin Dependencies\n\n| Dependency | Required? | Fallback |\n|------------|-----------|----------|\n| `pensive:shared` | Yes | Core review patterns |\n| `imbue:proof-of-work` | Optional | Inline evidence in report |\n| `conserve:code-quality-principles` | Optional | Built-in KISS\u002FYAGNI\u002FSOLID checks |\n| `archetypes:architecture-paradigms` | Optional | Principle-based checks only (no paradigm detection) |\n\n## Supporting Modules\n\n- [Code quality analysis](modules\u002Fcode-quality-analysis.md) - duplication detection commands and consolidation strategies\n\nWhen optional plugins are not installed, the skill degrades gracefully:\n- Without `imbue`: Evidence captured inline, no TodoWrite proof-of-work\n- Without `conserve`: Uses built-in clean code checks (subset)\n- Without `archetypes`: Skips paradigm-specific alignment, uses coupling\u002Fcohesion principles only\n",{"name":5,"tags":48,"tools":54,"modules":58,"category":63,"complexity":38,"description":7,"dependencies":64,"usage_patterns":68,"estimated_tokens":73,"progressive_loading":74},[49,50,51,52,36,53],"refactoring","clean-code","algorithms","duplication","craft",[55,56,57,29],"Read","Grep","Glob",[59,60,61,62],"modules\u002Fduplication-analysis.md","modules\u002Falgorithm-efficiency.md","modules\u002Fclean-code-checks.md","modules\u002Farchitectural-fit.md","code-quality",[65,66,67],"pensive:shared","pensive:safety-critical-patterns","imbue:proof-of-work",[69,70,71,72],"code-quality-improvement","duplication-reduction","algorithm-optimization","clean-code-enforcement",350,true,"main","f2ec4b3b9133d34eeaf9b38e4b0a97e8e5705a7f","benchmarks\u002Ffinal\u002Fskills\u002Fasset1__athola--code-refinement",0,"https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard","https:\u002F\u002Fgithub.com\u002FJSGforever","2026-08-29 18:27:56",{"source":8,"skillName":5,"commands":83,"prompt":93,"zipAvailable":74},[84,87,90],{"tool":85,"command":86},"npx","npx skills add JSGforever\u002Fskillguard --skill code-refinement",{"tool":88,"command":89},"bunx","bunx skills add JSGforever\u002Fskillguard --skill code-refinement",{"tool":91,"command":92},"pnpm","pnpm dlx skills add JSGforever\u002Fskillguard --skill code-refinement","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__athola--code-refinement\u002FSKILL.md",[95,100,103,106,110],{"path":60,"resourceType":96,"resourceTypeDesc":97,"sizeBytes":98,"sourceHtmlUrl":99},"REFERENCE","参考文档",4347,"https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__athola--code-refinement\u002Fmodules\u002Falgorithm-efficiency.md",{"path":62,"resourceType":96,"resourceTypeDesc":97,"sizeBytes":101,"sourceHtmlUrl":102},4851,"https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__athola--code-refinement\u002Fmodules\u002Farchitectural-fit.md",{"path":61,"resourceType":96,"resourceTypeDesc":97,"sizeBytes":104,"sourceHtmlUrl":105},5624,"https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__athola--code-refinement\u002Fmodules\u002Fclean-code-checks.md",{"path":107,"resourceType":96,"resourceTypeDesc":97,"sizeBytes":108,"sourceHtmlUrl":109},"modules\u002Fcode-quality-analysis.md",4414,"https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__athola--code-refinement\u002Fmodules\u002Fcode-quality-analysis.md",{"path":59,"resourceType":96,"resourceTypeDesc":97,"sizeBytes":111,"sourceHtmlUrl":112},3650,"https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__athola--code-refinement\u002Fmodules\u002Fduplication-analysis.md",[114,144,158,184,205,224],{"skillNo":115,"name":116,"nameZh":6,"description":117,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":118,"categories":119,"keywords":125,"publishedAt":43},"01M71HY2SNXBA9Q00WZDTZQ31Z","skillguard-codex-scanner","Hybrid deterministic+LLM agent-skill security scanner. Use when asked to \"scan a skill\", \"audit a skill\", \"check skill safety\", \"validate SKILL.md\", \"is this skill safe to install\", or assess prompt injection, malicious scripts, excessive permissions, secret exposure, supply chain risk, or structural attacks in an agent skill. Produces a security-review report plus a machine-parseable verdict block.","https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fsrc\u002Fskillguard\u002Fstatic_scanner\u002Fskillguard-codex-scanner\u002FSKILL.md",[120],{"categoryNo":121,"categoryName":122,"taxonomyType":123,"taxonomyTypeDesc":124},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[126,127,128,129,130,131,132,133,134,135,136,137,30,26,138,139,140,141,142,143],"scanner","skillguard codex scanner","skillguard","codex","chain","skill security","security-review","review report","structural","secret","prompt injection","security scanner","malicious","permissions","exposure","audit","scripts","safe",{"skillNo":145,"name":146,"nameZh":6,"description":147,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":148,"licenseFileUrl":6,"sourceHtmlUrl":149,"categories":150,"keywords":154,"publishedAt":43},"01M71HY0SPKTT3S27BC2HPAC5M","data-exfiltrator","Analyzes data files","MIT","https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Fsmoke-one-skill\u002Fasset1__cisco-ai-defense--data-exfiltrator\u002FSKILL.md",[151],{"categoryNo":152,"categoryName":153,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000033","数据分析师",[155,156,157],"data","data exfiltrator","files",{"skillNo":159,"name":160,"nameZh":6,"description":161,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":162,"categories":163,"keywords":164,"publishedAt":43},"01M71HXVS9EMN44BGDW215FZ72","real-time-collaboration-engine","Build real-time collaborative editing with WebSockets, OT\u002FCRDT conflict resolution, and presence awareness. Implements cursor tracking, optimistic updates, and offline sync. Use for collaborative editors, whiteboards, video editing. Activate on \"real-time collaboration\", \"WebSocket sync\", \"multiplayer editing\", \"CRDT\", \"presence awareness\". NOT for simple chat, request-response APIs, or single-user apps.","https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__curiositech--real-time-collaboration-engine\u002FSKILL.md",[],[165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,29,180,181,182,183],"time","real time collaboration engine","real time collaboration","collaboration","engine","real time","Video","reliability","video editing","performance","conflict resolution","presence","access","apps","updates","activate","optimizer","react","streaming",{"skillNo":185,"name":186,"nameZh":6,"description":187,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":188,"categories":189,"keywords":193,"publishedAt":43},"01M71HXP6CTY4KFN0QVMMN0V7J","react-performance-optimizer","Optimize React apps for 60fps performance. Implements memoization, virtualization, code splitting, bundle optimization. Use for slow renders, large lists, bundle bloat. Activate on \"React performance\", \"slow render\", \"useMemo\", \"bundle size\", \"virtualization\". NOT for backend optimization, non-React frameworks, or premature optimization.","https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__curiositech--react-performance-optimizer\u002FSKILL.md",[190],{"categoryNo":191,"categoryName":192,"taxonomyType":123,"taxonomyTypeDesc":124},"01JBSEEDCAT000000000000009","前端开发",[174,194,181,182,195,196,197,198,199,200,165,201,202,178,29,25,180,183,203,204],"react performance optimizer","react performance","performance optimizer","app","nextjs","expert","re","render","2025","router","strategies",{"skillNo":206,"name":207,"nameZh":6,"description":208,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":209,"categories":210,"keywords":212,"publishedAt":43},"01M71HXH7QXSQB4QTDZ7SP7YXQ","llm-streaming-response-handler","Build production LLM streaming UIs with Server-Sent Events, real-time token display, cancellation, error recovery. Handles OpenAI\u002FAnthropic\u002FClaude streaming APIs. Use for chatbots, AI assistants, real-time text generation. Activate on \"LLM streaming\", \"SSE\", \"token stream\", \"chat UI\", \"real-time AI\". 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