[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6HV8QZSPFMBPKH2SKWYMSXS":3,"skill-same:01M6HV8QZSPFMBPKH2SKWYMSXS":50},{"skillNo":4,"name":5,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":7,"ownerLogin":8,"ownerAvatarUrl":9,"ownerAccountNo":10,"ownerDisplayName":11,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":22,"publishedAt":25,"linkOnly":26,"markdownContent":27,"markdownBody":27,"frontMatter":28,"refName":29,"commitSha":30,"skillDirectory":31,"forksCount":32,"repositoryHtmlUrl":33,"repositoryHomepageUrl":6,"ownerHtmlUrl":34,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":35,"install":36,"resources":49},"01M6HV8QZSPFMBPKH2SKWYMSXS","self-improving-agent",null,"Silex-Research\u002FDontPanic","Silex-Research","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F130925891?v=4","01M6HV5K48NKSZT37SQKSV639D","Silex Research","01M6HV5K8R2281GSJB6S9A5TF8",5,"Apache-2.0","https:\u002F\u002Fgithub.com\u002FSilex-Research\u002FDontPanic\u002Fblob\u002Fmain\u002Fclaude\u002Fskills\u002Fself-improving-agent\u002FSKILL.md",[17],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[23,24],"self improving agent","self","2026-11-10 10:12:30",false,"# Self-Improving Agent Skill\n\n## Overview\n\nEnables agents to analyze their own performance, identify improvement opportunities, and modify behavior or suggest changes to enhance outcomes over time.\n\n## When to Use\n\n- Trading strategy optimization based on P&L patterns\n- Content performance analysis (what hooks work best)\n- Response quality improvement\n- Error pattern detection and prevention\n- Efficiency optimization (speed, cost, accuracy)\n\n## Core Loop\n\n```\nExecute → Measure → Analyze → Propose → (Approve) → Implement → Validate\n```\n\n## Components\n\n### 1. Performance Measurement\n\n```javascript\n\u002F\u002F Define success metrics per domain\nconst metrics = {\n  trading: {\n    win_rate: \"wins \u002F total_trades\",\n    expectancy: \"avg_win * win_rate - avg_loss * loss_rate\",\n    sharpe: \"return \u002F volatility\",\n    max_drawdown: \"peak_to_trough decline\"\n  },\n  content: {\n    engagement_rate: \"(likes + comments + shares) \u002F views\",\n    conversion_rate: \"clicks \u002F views\",\n    viral_coefficient: \"shares \u002F views\",\n    production_time: \"minutes per video\"\n  },\n  research: {\n    relevance_score: \"user engagement with findings\",\n    action_rate: \"% of research that leads to action\",\n    time_to_insight: \"hours from query to conclusion\"\n  }\n};\n```\n\n### 2. Pattern Detection\n\n```javascript\n\u002F\u002F Identify what works\nfunction analyzeTradingPerformance() {\n  const trades = getTradeHistory();\n  \n  \u002F\u002F Pattern: Time of day\n  const byHour = groupBy(trades, t => t.entryHour);\n  const bestHours = byHour\n    .filter(h => h.trades.length > 5)\n    .sort((a, b) => b.winRate - a.winRate);\n  \n  \u002F\u002F Pattern: Signal type\n  const bySignal = groupBy(trades, t => t.taSignal);\n  const bestSignals = bySignal\n    .sort((a, b) => b.expectancy - a.expectancy);\n  \n  \u002F\u002F Pattern: Underlying\n  const bySymbol = groupBy(trades, t => t.symbol);\n  const bestSymbols = bySymbol\n    .sort((a, b) => b.profitFactor - a.profitFactor);\n  \n  return {\n    recommendation: \"Focus on \" + bestSignals[0].name + \" signals\",\n    evidence: \"Expectancy: \" + bestSignals[0].expectancy,\n    confidence: calculateConfidence(bestSignals[0].n)\n  };\n}\n```\n\n### 3. Hypothesis Generation\n\n```javascript\n\u002F\u002F Propose improvements\nfunction generateImprovements(analysis) {\n  const hypotheses = [];\n  \n  \u002F\u002F Hypothesis 1: Time-based\n  if (analysis.bestHours[0].winRate > analysis.avgWinRate * 1.2) {\n    hypotheses.push({\n      type: \"parameter_change\",\n      target: \"trading_hours\",\n      current: \"9:30-16:00\",\n      proposed: analysis.bestHours[0].range,\n      expected: `+${((analysis.bestHours[0].winRate - analysis.avgWinRate) * 100).toFixed(1)}% win rate`,\n      test: \"Paper trade new hours for 20 trades\"\n    });\n  }\n  \n  \u002F\u002F Hypothesis 2: Signal refinement\n  if (analysis.bestSignals[0].n > 10) {\n    hypotheses.push({\n      type: \"filter_change\",\n      target: \"entry_criteria\",\n      current: \"RSI \u003C 30\",\n      proposed: `RSI \u003C ${analysis.bestSignals[0].optimalRSI}`,\n      expected: \"Higher expectancy\",\n      test: \"Backtest on last 100 signals\"\n    });\n  }\n  \n  return hypotheses;\n}\n```\n\n### 4. Controlled Experimentation\n\n```javascript\n\u002F\u002F A\u002FB testing framework\nfunction runExperiment(hypothesis) {\n  const experiment = {\n    id: generateId(),\n    hypothesis: hypothesis,\n    startDate: new Date(),\n    duration: \"20_trades\",\n    control: currentStrategy,\n    treatment: applyChange(currentStrategy, hypothesis),\n    sampleSize: 20\n  };\n  \n  \u002F\u002F Run parallel\n  while (experiment.trades \u003C experiment.sampleSize) {\n    if (Math.random() > 0.5) {\n      executeWithStrategy(control);\n    } else {\n      executeWithStrategy(treatment);\n    }\n  }\n  \n  \u002F\u002F Analyze results\n  const result = comparePerformance(control, treatment);\n  \n  if (result.significant && result.improvement > 0.1) {\n    proposeAdoption(hypothesis, result);\n  }\n}\n```\n\n### 5. User Approval Workflow\n\n```javascript\n\u002F\u002F Never change without approval\nfunction proposeAdoption(hypothesis, results) {\n  const proposal = {\n    type: \"self_improvement\",\n    domain: \"trading\",\n    change: hypothesis,\n    evidence: results,\n    impact: \"high\",\n    rollback_plan: \"Restore previous strategy config\"\n  };\n  \n  \u002F\u002F Queue for approval\n  queueForApproval(proposal);\n  notifyUser(`Proposed improvement: ${hypothesis.description}`);\n}\n```\n\n## Implementation Areas\n\n### Trading Strategy Self-Improvement\n\n**Current State:** RSI \u003C 30 + near SMA entry\n\n**Self-Improvement Loop:**\n1. Track every trade with full context (RSI, SMA distance, VIX, time, symbol)\n2. Weekly analysis: Which parameters had highest expectancy?\n3. Propose: \"Change RSI threshold to 35\" if data supports\n4. Run 20-trade experiment (control vs proposed)\n5. If significant improvement → Request approval to adopt\n\n### Content Performance Self-Improvement\n\n**Current State:** Script templates with trending hooks\n\n**Self-Improvement Loop:**\n1. Track: Hook type, format, audio, CTA, post time\n2. Analyze: Which combinations had highest engagement?\n3. Propose: \"Use 'POV:' hooks for Styln, questions for Spin & Dine\"\n4. Generate 10 videos with new approach\n5. If engagement +20% → Request approval to update templates\n\n### Code Quality Self-Improvement\n\n**Current State:** Fixed implementations\n\n**Self-Improvement Loop:**\n1. Track: Bugs, latency, complexity, user feedback\n2. Analyze: Which functions cause most issues?\n3. Propose: Refactor or optimization\n4. Test in isolation\n5. If improvement confirmed → Request merge\n\n## Safety Guardrails\n\n### 1. Gradual Rollout\n```\nStage 1: Paper\u002Fsimulation (validate)\nStage 2: Small sample (20 trades\u002Fposts)\nStage 3: User approval\nStage 4: Full adoption\nStage 5: Monitor for regression\n```\n\n### 2. Rollback Capability\nEvery change:\n- Version controlled\n- Previous state saved\n- One-command rollback\n- Automatic rollback on critical failure\n\n### 3. Human Oversight\n- All changes queued for approval\n- Explanation of reasoning required\n- Evidence presented clearly\n- User can reject without penalty\n\n## Success Metrics\n\n- Improvement rate: % of proposals that improve performance\n- Time to improvement: Days from observation to validated change\n- False positive rate: Changes that didn't help or hurt\n- User adoption rate: % of proposals user approves\n\n---\n\n**Status:** Framework defined, awaiting trade\u002Fcontent data for first improvement cycle\n",{},"main","14ffaf54d57448d72d9f32efca397086f3de0e64","claude\u002Fskills\u002Fself-improving-agent",1,"https:\u002F\u002Fgithub.com\u002FSilex-Research\u002FDontPanic","https:\u002F\u002Fgithub.com\u002FSilex-Research","2026-11-12 20:39:31",{"source":7,"skillName":5,"commands":37,"prompt":47,"zipAvailable":48},[38,41,44],{"tool":39,"command":40},"npx","npx skills add Silex-Research\u002FDontPanic --skill self-improving-agent",{"tool":42,"command":43},"bunx","bunx skills add 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