[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71GX5TTYSD5ZX150CA47GGY":3,"skill-same:01M71GX5TTYSD5ZX150CA47GGY":67},{"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":27,"publishedAt":34,"linkOnly":35,"markdownContent":36,"markdownBody":37,"frontMatter":38,"refName":40,"commitSha":41,"skillDirectory":42,"forksCount":43,"repositoryHtmlUrl":44,"repositoryHomepageUrl":6,"ownerHtmlUrl":45,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":46,"install":47,"resources":60},"01M71GX5TTYSD5ZX150CA47GGY","analytics",null,"Performance attribution, trade analytics, and strategy optimization","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__alsk1992--analytics\u002FSKILL.md",[17,22],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000013","分析与可视化","SCENARIO","使用场景",{"categoryNo":23,"categoryName":24,"taxonomyType":25,"taxonomyTypeDesc":26},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[5,28,29,30,31,32,33],"Optimization","performance","attribution","strategy","trade","performance attribution","2026-11-10 10:12:30",false,"---\nname: analytics\ndescription: \"Performance attribution, trade analytics, and strategy optimization\"\nemoji: \"📊\"\n---\n\n# Analytics - Complete API Reference\n\nAnalyze trading performance with attribution by edge source, time-of-day analysis, and optimization insights.\n\n---\n\n## Chat Commands\n\n### Performance Overview\n\n```\n\u002Fanalytics                          Performance summary\n\u002Fanalytics today                    Today's performance\n\u002Fanalytics week                     Weekly breakdown\n\u002Fanalytics month                    Monthly breakdown\n```\n\n### Attribution\n\n```\n\u002Fanalytics attribution              P&L by edge source\n\u002Fanalytics by-platform              P&L by platform\n\u002Fanalytics by-category              P&L by market category\n\u002Fanalytics by-strategy              P&L by strategy\n```\n\n### Time Analysis\n\n```\n\u002Fanalytics best-times               Best trading hours\n\u002Fanalytics by-hour                  Hourly performance\n\u002Fanalytics by-day                   Day of week analysis\n```\n\n### Edge Analysis\n\n```\n\u002Fanalytics edge-decay               How edge decays over time\n\u002Fanalytics edge-buckets             Performance by edge size\n\u002Fanalytics liquidity                Performance by liquidity\n```\n\n---\n\n## TypeScript API Reference\n\n### Create Analytics Service\n\n```typescript\nimport { createAnalyticsService } from 'clodds\u002Fanalytics';\n\nconst analytics = createAnalyticsService({\n  \u002F\u002F Data source\n  tradesDb: '.\u002Ftrades.db',\n\n  \u002F\u002F Time zone\n  timezone: 'America\u002FNew_York',\n});\n```\n\n### Performance Summary\n\n```typescript\nconst summary = await analytics.getSummary({\n  period: 'month',\n  \u002F\u002F or: from: '2024-01-01', to: '2024-01-31'\n});\n\nconsole.log('=== Performance ===');\nconsole.log(`Total P&L: $${summary.totalPnl}`);\nconsole.log(`Win Rate: ${summary.winRate}%`);\nconsole.log(`Profit Factor: ${summary.profitFactor}`);\nconsole.log(`Sharpe Ratio: ${summary.sharpeRatio}`);\nconsole.log(`Total Trades: ${summary.totalTrades}`);\nconsole.log(`Avg Trade: $${summary.avgTrade}`);\nconsole.log(`Best Trade: $${summary.bestTrade}`);\nconsole.log(`Worst Trade: $${summary.worstTrade}`);\n```\n\n### Attribution by Edge Source\n\n```typescript\nconst attribution = await analytics.getAttribution('edgeSource');\n\nfor (const source of attribution) {\n  console.log(`${source.name}:`);\n  console.log(`  P&L: $${source.pnl}`);\n  console.log(`  Trades: ${source.trades}`);\n  console.log(`  Win Rate: ${source.winRate}%`);\n  console.log(`  Contribution: ${source.contribution}%`);\n}\n\n\u002F\u002F Example sources:\n\u002F\u002F - price_lag (stale prices)\n\u002F\u002F - liquidity_gap (thin orderbooks)\n\u002F\u002F - information (news\u002Fevents)\n\u002F\u002F - model_edge (external models)\n\u002F\u002F - combinatorial (arbitrage)\n```\n\n### Time-of-Day Analysis\n\n```typescript\nconst hourly = await analytics.getHourlyPerformance();\n\nconsole.log('Best Hours:');\nfor (const hour of hourly.slice(0, 3)) {\n  console.log(`  ${hour.hour}:00 - Win: ${hour.winRate}%, Avg: $${hour.avgPnl}`);\n}\n\nconsole.log('Worst Hours:');\nfor (const hour of hourly.slice(-3)) {\n  console.log(`  ${hour.hour}:00 - Win: ${hour.winRate}%, Avg: $${hour.avgPnl}`);\n}\n```\n\n### Day-of-Week Analysis\n\n```typescript\nconst daily = await analytics.getDayOfWeekPerformance();\n\nfor (const day of daily) {\n  console.log(`${day.name}: $${day.pnl} (${day.trades} trades, ${day.winRate}% win)`);\n}\n```\n\n### Edge Decay Analysis\n\n```typescript\nconst decay = await analytics.getEdgeDecay();\n\nconsole.log('Edge Decay (how fast edge disappears):');\nfor (const bucket of decay) {\n  console.log(`  ${bucket.holdTime}: ${bucket.avgReturn}% return`);\n}\n\u002F\u002F Shows optimal hold time before edge decays\n```\n\n### Edge Size Buckets\n\n```typescript\nconst edgeBuckets = await analytics.getEdgeBuckets();\n\nfor (const bucket of edgeBuckets) {\n  console.log(`Edge ${bucket.min}-${bucket.max}%:`);\n  console.log(`  Trades: ${bucket.trades}`);\n  console.log(`  Win Rate: ${bucket.winRate}%`);\n  console.log(`  Avg P&L: $${bucket.avgPnl}`);\n  console.log(`  Realized Edge: ${bucket.realizedEdge}%`);\n}\n```\n\n### Liquidity Analysis\n\n```typescript\nconst liquidity = await analytics.getLiquidityAnalysis();\n\nfor (const bucket of liquidity) {\n  console.log(`${bucket.name} liquidity:`);\n  console.log(`  Trades: ${bucket.trades}`);\n  console.log(`  Avg Slippage: ${bucket.avgSlippage}%`);\n  console.log(`  Fill Rate: ${bucket.fillRate}%`);\n  console.log(`  Avg P&L: $${bucket.avgPnl}`);\n}\n```\n\n### Execution Quality\n\n```typescript\nconst execution = await analytics.getExecutionQuality();\n\nconsole.log('=== Execution Quality ===');\nconsole.log(`Avg Slippage: ${execution.avgSlippage}%`);\nconsole.log(`Fill Rate: ${execution.fillRate}%`);\nconsole.log(`Avg Fill Time: ${execution.avgFillTimeMs}ms`);\nconsole.log(`Partial Fills: ${execution.partialFillRate}%`);\nconsole.log(`Rejected Orders: ${execution.rejectionRate}%`);\n```\n\n### Platform Comparison\n\n```typescript\nconst platforms = await analytics.getPlatformComparison();\n\nfor (const platform of platforms) {\n  console.log(`${platform.name}:`);\n  console.log(`  P&L: $${platform.pnl}`);\n  console.log(`  Win Rate: ${platform.winRate}%`);\n  console.log(`  Avg Slippage: ${platform.avgSlippage}%`);\n  console.log(`  Best For: ${platform.strengths.join(', ')}`);\n}\n```\n\n### Export Report\n\n```typescript\n\u002F\u002F Generate PDF report\nawait analytics.exportReport({\n  format: 'pdf',\n  period: 'month',\n  include: ['summary', 'attribution', 'charts'],\n  outputPath: '.\u002Freports\u002Fjanuary-2024.pdf',\n});\n\n\u002F\u002F Export raw data\nawait analytics.exportData({\n  format: 'csv',\n  period: 'month',\n  outputPath: '.\u002Fdata\u002Fjanuary-trades.csv',\n});\n```\n\n---\n\n## Attribution Categories\n\n| Category | Description |\n|----------|-------------|\n| **Edge Source** | Where the edge came from |\n| **Platform** | Which platform traded on |\n| **Category** | Market category (politics, crypto) |\n| **Strategy** | Which strategy generated trade |\n| **Time** | Hour\u002Fday of trade |\n| **Size** | Trade size bucket |\n\n---\n\n## Key Metrics\n\n| Metric | Good Value | Description |\n|--------|------------|-------------|\n| **Win Rate** | > 50% | Percent of winning trades |\n| **Profit Factor** | > 1.5 | Gross profit \u002F gross loss |\n| **Sharpe Ratio** | > 1.0 | Risk-adjusted returns |\n| **Realized Edge** | > 0 | Actual vs expected edge |\n| **Fill Rate** | > 95% | Orders fully filled |\n\n---\n\n## Best Practices\n\n1. **Review weekly** — Catch problems early\n2. **Track attribution** — Know where profits come from\n3. **Optimize timing** — Trade your best hours\n4. **Monitor edge decay** — Don't hold too long\n5. **Check execution** — Slippage kills edge\n","# Analytics - Complete API Reference\n\nAnalyze trading performance with attribution by edge source, time-of-day analysis, and optimization insights.\n\n---\n\n## Chat Commands\n\n### Performance Overview\n\n```\n\u002Fanalytics                          Performance summary\n\u002Fanalytics today                    Today's performance\n\u002Fanalytics week                     Weekly breakdown\n\u002Fanalytics month                    Monthly breakdown\n```\n\n### Attribution\n\n```\n\u002Fanalytics attribution              P&L by edge source\n\u002Fanalytics by-platform              P&L by platform\n\u002Fanalytics by-category              P&L by market category\n\u002Fanalytics by-strategy              P&L by strategy\n```\n\n### Time Analysis\n\n```\n\u002Fanalytics best-times               Best trading hours\n\u002Fanalytics by-hour                  Hourly performance\n\u002Fanalytics by-day                   Day of week analysis\n```\n\n### Edge Analysis\n\n```\n\u002Fanalytics edge-decay               How edge decays over time\n\u002Fanalytics edge-buckets             Performance by edge size\n\u002Fanalytics liquidity                Performance by liquidity\n```\n\n---\n\n## TypeScript API Reference\n\n### Create Analytics Service\n\n```typescript\nimport { createAnalyticsService } from 'clodds\u002Fanalytics';\n\nconst analytics = createAnalyticsService({\n  \u002F\u002F Data source\n  tradesDb: '.\u002Ftrades.db',\n\n  \u002F\u002F Time zone\n  timezone: 'America\u002FNew_York',\n});\n```\n\n### Performance Summary\n\n```typescript\nconst summary = await analytics.getSummary({\n  period: 'month',\n  \u002F\u002F or: from: '2024-01-01', to: '2024-01-31'\n});\n\nconsole.log('=== Performance ===');\nconsole.log(`Total P&L: $${summary.totalPnl}`);\nconsole.log(`Win Rate: ${summary.winRate}%`);\nconsole.log(`Profit Factor: ${summary.profitFactor}`);\nconsole.log(`Sharpe Ratio: ${summary.sharpeRatio}`);\nconsole.log(`Total Trades: ${summary.totalTrades}`);\nconsole.log(`Avg Trade: $${summary.avgTrade}`);\nconsole.log(`Best Trade: $${summary.bestTrade}`);\nconsole.log(`Worst Trade: $${summary.worstTrade}`);\n```\n\n### Attribution by Edge Source\n\n```typescript\nconst attribution = await analytics.getAttribution('edgeSource');\n\nfor (const source of attribution) {\n  console.log(`${source.name}:`);\n  console.log(`  P&L: $${source.pnl}`);\n  console.log(`  Trades: ${source.trades}`);\n  console.log(`  Win Rate: ${source.winRate}%`);\n  console.log(`  Contribution: ${source.contribution}%`);\n}\n\n\u002F\u002F Example sources:\n\u002F\u002F - price_lag (stale prices)\n\u002F\u002F - liquidity_gap (thin orderbooks)\n\u002F\u002F - information (news\u002Fevents)\n\u002F\u002F - model_edge (external models)\n\u002F\u002F - combinatorial (arbitrage)\n```\n\n### Time-of-Day Analysis\n\n```typescript\nconst hourly = await analytics.getHourlyPerformance();\n\nconsole.log('Best Hours:');\nfor (const hour of hourly.slice(0, 3)) {\n  console.log(`  ${hour.hour}:00 - Win: ${hour.winRate}%, Avg: $${hour.avgPnl}`);\n}\n\nconsole.log('Worst Hours:');\nfor (const hour of hourly.slice(-3)) {\n  console.log(`  ${hour.hour}:00 - Win: ${hour.winRate}%, Avg: $${hour.avgPnl}`);\n}\n```\n\n### Day-of-Week Analysis\n\n```typescript\nconst daily = await analytics.getDayOfWeekPerformance();\n\nfor (const day of daily) {\n  console.log(`${day.name}: $${day.pnl} (${day.trades} trades, ${day.winRate}% win)`);\n}\n```\n\n### Edge Decay Analysis\n\n```typescript\nconst decay = await analytics.getEdgeDecay();\n\nconsole.log('Edge Decay (how fast edge disappears):');\nfor (const bucket of decay) {\n  console.log(`  ${bucket.holdTime}: ${bucket.avgReturn}% return`);\n}\n\u002F\u002F Shows optimal hold time before edge decays\n```\n\n### Edge Size Buckets\n\n```typescript\nconst edgeBuckets = await analytics.getEdgeBuckets();\n\nfor (const bucket of edgeBuckets) {\n  console.log(`Edge ${bucket.min}-${bucket.max}%:`);\n  console.log(`  Trades: ${bucket.trades}`);\n  console.log(`  Win Rate: ${bucket.winRate}%`);\n  console.log(`  Avg P&L: $${bucket.avgPnl}`);\n  console.log(`  Realized Edge: ${bucket.realizedEdge}%`);\n}\n```\n\n### Liquidity Analysis\n\n```typescript\nconst liquidity = await analytics.getLiquidityAnalysis();\n\nfor (const bucket of liquidity) {\n  console.log(`${bucket.name} liquidity:`);\n  console.log(`  Trades: ${bucket.trades}`);\n  console.log(`  Avg Slippage: ${bucket.avgSlippage}%`);\n  console.log(`  Fill Rate: ${bucket.fillRate}%`);\n  console.log(`  Avg P&L: $${bucket.avgPnl}`);\n}\n```\n\n### Execution Quality\n\n```typescript\nconst execution = await analytics.getExecutionQuality();\n\nconsole.log('=== Execution Quality ===');\nconsole.log(`Avg Slippage: ${execution.avgSlippage}%`);\nconsole.log(`Fill Rate: ${execution.fillRate}%`);\nconsole.log(`Avg Fill Time: ${execution.avgFillTimeMs}ms`);\nconsole.log(`Partial Fills: ${execution.partialFillRate}%`);\nconsole.log(`Rejected Orders: ${execution.rejectionRate}%`);\n```\n\n### Platform Comparison\n\n```typescript\nconst platforms = await analytics.getPlatformComparison();\n\nfor (const platform of platforms) {\n  console.log(`${platform.name}:`);\n  console.log(`  P&L: $${platform.pnl}`);\n  console.log(`  Win Rate: ${platform.winRate}%`);\n  console.log(`  Avg Slippage: ${platform.avgSlippage}%`);\n  console.log(`  Best For: ${platform.strengths.join(', ')}`);\n}\n```\n\n### Export Report\n\n```typescript\n\u002F\u002F Generate PDF report\nawait analytics.exportReport({\n  format: 'pdf',\n  period: 'month',\n  include: ['summary', 'attribution', 'charts'],\n  outputPath: '.\u002Freports\u002Fjanuary-2024.pdf',\n});\n\n\u002F\u002F Export raw data\nawait analytics.exportData({\n  format: 'csv',\n  period: 'month',\n  outputPath: '.\u002Fdata\u002Fjanuary-trades.csv',\n});\n```\n\n---\n\n## Attribution Categories\n\n| Category | Description |\n|----------|-------------|\n| **Edge Source** | Where the edge came from |\n| **Platform** | Which platform traded on |\n| **Category** | Market category (politics, crypto) |\n| **Strategy** | Which strategy generated trade |\n| **Time** | Hour\u002Fday of trade |\n| **Size** | Trade size bucket |\n\n---\n\n## Key Metrics\n\n| Metric | Good Value | Description |\n|--------|------------|-------------|\n| **Win Rate** | > 50% | Percent of winning trades |\n| **Profit Factor** | > 1.5 | Gross profit \u002F gross loss |\n| **Sharpe Ratio** | > 1.0 | Risk-adjusted returns |\n| **Realized Edge** | > 0 | Actual vs expected edge |\n| **Fill Rate** | > 95% | Orders fully filled |\n\n---\n\n## Best Practices\n\n1. **Review weekly** — Catch problems early\n2. **Track attribution** — Know where profits come from\n3. **Optimize timing** — Trade your best hours\n4. **Monitor edge decay** — Don't hold too long\n5. **Check execution** — Slippage kills edge\n",{"name":5,"emoji":39,"description":7},"📊","main","f2ec4b3b9133d34eeaf9b38e4b0a97e8e5705a7f","benchmarks\u002Ffinal\u002Fskills\u002Fasset1__alsk1992--analytics",0,"https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard","https:\u002F\u002Fgithub.com\u002FJSGforever","2026-08-29 18:27:56",{"source":8,"skillName":5,"commands":48,"prompt":58,"zipAvailable":59},[49,52,55],{"tool":50,"command":51},"npx","npx skills add JSGforever\u002Fskillguard --skill analytics",{"tool":53,"command":54},"bunx","bunx skills add JSGforever\u002Fskillguard --skill analytics",{"tool":56,"command":57},"pnpm","pnpm dlx skills add JSGforever\u002Fskillguard --skill analytics","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__alsk1992--analytics\u002FSKILL.md",true,[61],{"path":62,"resourceType":63,"resourceTypeDesc":64,"sizeBytes":65,"sourceHtmlUrl":66},"index.ts","SCRIPT","脚本",13991,"https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__alsk1992--analytics\u002Findex.ts",[68,98,110,136,158,179],{"skillNo":69,"name":70,"nameZh":6,"description":71,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":72,"categories":73,"keywords":77,"publishedAt":34},"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",[74],{"categoryNo":75,"categoryName":76,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000023","Skill与智能体开发",[78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97],"scanner","skillguard codex scanner","skillguard","codex","chain","skill security","security-review","review report","structural","secret","prompt injection","security scanner","when","review","malicious","permissions","exposure","audit","scripts","safe",{"skillNo":99,"name":100,"nameZh":6,"description":101,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":102,"licenseFileUrl":6,"sourceHtmlUrl":103,"categories":104,"keywords":106,"publishedAt":34},"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",[105],{"categoryNo":23,"categoryName":24,"taxonomyType":25,"taxonomyTypeDesc":26},[107,108,109],"data","data exfiltrator","files",{"skillNo":111,"name":112,"nameZh":6,"description":113,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":114,"categories":115,"keywords":116,"publishedAt":34},"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",[],[117,118,119,120,121,122,123,124,125,29,126,127,128,129,130,131,132,133,134,135],"time","real time collaboration engine","real time collaboration","collaboration","engine","real time","Video","reliability","video editing","conflict resolution","presence","access","apps","updates","Bash","activate","optimizer","react","streaming",{"skillNo":137,"name":138,"nameZh":6,"description":139,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":140,"categories":141,"keywords":145,"publishedAt":34},"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",[142],{"categoryNo":143,"categoryName":144,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000009","前端开发",[29,146,133,134,147,148,149,150,151,152,117,153,154,129,131,155,132,135,156,157],"react performance optimizer","react performance","performance optimizer","app","nextjs","expert","re","render","2025","code","router","strategies",{"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":165,"publishedAt":34},"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\". NOT for batch processing, non-streaming APIs, or WebSocket bidirectional chat.","https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__curiositech--llm-streaming-response-handler\u002FSKILL.md",[164],{"categoryNo":143,"categoryName":144,"taxonomyType":20,"taxonomyTypeDesc":21},[166,135,167,168,169,170,29,117,171,172,173,131,132,133,174,134,175,176,177,178],"llm streaming response handler","handler","error","processing","retry","chat ui","error recovery","critical","batch","display","text","server","read",{"skillNo":180,"name":181,"nameZh":6,"description":182,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":183,"categories":184,"keywords":192,"publishedAt":34},"01M71HXCB0E1YRKYW1Y8T0Y106","github-actions-pipeline-builder","Build production CI\u002FCD pipelines with GitHub Actions. Implements matrix builds, caching, deployments, testing, security scanning. Use for automated testing, deployments, release workflows. Activate on \"GitHub Actions\", \"CI\u002FCD\", \"workflow\", \"deployment pipeline\", \"automated testing\". 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