[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6Q52AEHPZJ65SWMX934837G":3,"skill-same:01M6Q52AEHPZJ65SWMX934837G":81},{"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":17,"publishedAt":38,"linkOnly":39,"markdownContent":40,"markdownBody":41,"frontMatter":42,"refName":43,"commitSha":44,"skillDirectory":45,"forksCount":46,"repositoryHtmlUrl":47,"repositoryHomepageUrl":6,"ownerHtmlUrl":48,"ownerBlogUrl":6,"ownerDescription":49,"updatedAt":50,"install":51,"resources":64},"01M6Q52AEHPZJ65SWMX934837G","decision-trees",null,"Decision tree analysis for complex decision-making across all domains. Use when user needs to evaluate multiple options with uncertain outcomes, assess risk\u002Freward scenarios, or structure choices systematically. Applicable to business, investment, personal decisions, operations, career choices, product strategy, and any situation requiring structured evaluation. Triggers include decision tree, should I, what if, evaluate options, compare alternatives, risk analysis.","Kernel8901\u002Fai-agent-skills-classification","Kernel8901","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F72647553?v=4","01M6KV388J2H5PNC8D8P940BJD","01M6KV38CS5B3AY63RKZB410YM",7,"MIT","https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F购物与电子商务\u002Fdecision-trees\u002FSKILL.md",[],[18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37],"trees","decision","decision trees","structure","alternatives","for","tree","assess","domains","triggers","decision-making","across","personal","options","investment","strategy","analysis","risk","Evaluation","product","2026-11-10 10:12:30",false,"---\nname: decision-trees\ndescription: Decision tree analysis for complex decision-making across all domains. Use when user needs to evaluate multiple options with uncertain outcomes, assess risk\u002Freward scenarios, or structure choices systematically. Applicable to business, investment, personal decisions, operations, career choices, product strategy, and any situation requiring structured evaluation. Triggers include decision tree, should I, what if, evaluate options, compare alternatives, risk analysis.\n---\n\n# Decision Trees — Structured Decision-Making\n\nDecision tree analysis: a visual tool for making decisions with probabilities and expected value.\n\n## When to Use\n\n✅ **Good for:**\n- Business decisions (investments, hiring, product launches)\n- Personal choices (career, relocation, purchases)\n- Trading & investing (position sizing, entry\u002Fexit)\n- Operational decisions (expansion, outsourcing)\n- Any situation with measurable consequences\n\n❌ **Not suitable for:**\n- Decisions with true uncertainty (black swans)\n- Fast tactical choices\n- Purely emotional\u002Fethical questions\n\n## Method\n\n**Decision tree** = tree-like structure where:\n- **Decision nodes** (squares) — your actions\n- **Chance nodes** (circles) — random events\n- **End nodes** (triangles) — final outcomes\n\n**Process:**\n1. **Define options** — all possible actions\n2. **Define outcomes** — what can happen after each action\n3. **Estimate probabilities** — how likely is each outcome (0-100%)\n4. **Estimate values** — utility\u002Freward for each outcome (money, points, utility units)\n5. **Calculate EV** — expected value = Σ (probability × value)\n6. **Choose** — option with highest EV\n\n## Formula\n\n```\nEV = Σ (probability_i × value_i)\n```\n\n**Example:**\n- Outcome A: 70% probability, +$100 → 0.7 × 100 = $70\n- Outcome B: 30% probability, -$50 → 0.3 × (-50) = -$15\n- **EV = $70 + (-$15) = $55**\n\n## Classic Example (from Wikipedia)\n\n**Decision:** Go to party or stay home?\n\n### Estimates:\n- Party: +9 utility (fun)\n- Home: +3 utility (comfort)\n- Carrying jacket unnecessarily: -2 utility\n- Being cold: -10 utility\n- Probability cold: 70%\n- Probability warm: 30%\n\n### Tree:\n\n```\nDecision\n├─ Go to party\n│  ├─ Take jacket\n│  │  ├─ Cold (70%) → 9 utility (party)\n│  │  └─ Warm (30%) → 9 - 2 = 7 utility (carried unnecessarily)\n│  │  EV = 0.7 × 9 + 0.3 × 7 = 8.4\n│  └─ Don't take jacket\n│     ├─ Cold (70%) → 9 - 10 = -1 utility (froze)\n│     └─ Warm (30%) → 9 utility (perfect)\n│     EV = 0.7 × (-1) + 0.3 × 9 = 2.0\n└─ Stay home\n   └─ EV = 3.0 (always)\n```\n\n**Conclusion:** Go and take jacket (EV = 8.4) > stay home (EV = 3.0) > go without jacket (EV = 2.0)\n\n## Business Example\n\n**Decision:** Launch new product?\n\n### Estimates:\n- Success probability: 40%\n- Failure probability: 60%\n- Profit if success: $500K\n- Loss if failure: $200K\n- Don't launch: $0\n\n### Tree:\n\n```\nLaunch product\n├─ Success (40%) → +$500K\n└─ Failure (60%) → -$200K\n\nEV = (0.4 × 500K) + (0.6 × -200K) = 200K - 120K = +$80K\n\nDon't launch\n└─ EV = $0\n```\n\n**Conclusion:** Launch (EV = +$80K) is better than not launching ($0).\n\n## Trading Example\n\n**Decision:** Enter position or wait?\n\n### Estimates:\n- Probability of rise: 60%\n- Probability of fall: 40%\n- Position size: $1000\n- Target: +10% ($100 profit)\n- Stop-loss: -5% ($50 loss)\n\n### Tree:\n\n```\nEnter position\n├─ Rise (60%) → +$100\n└─ Fall (40%) → -$50\n\nEV = (0.6 × 100) + (0.4 × -50) = 60 - 20 = +$40\n\nWait\n└─ No position → $0\n\nEV = $0\n```\n\n**Conclusion:** Entering position has positive EV (+$40), better than waiting ($0).\n\n## Method Limitations\n\n⚠️ **Critical points:**\n\n1. **Subjective estimates** — probabilities often \"finger in the air\"\n2. **Doesn't account for risk appetite** — ignores psychology (loss aversion)\n3. **Simplified model** — reality is more complex\n4. **Unstable** — small data changes can drastically alter the tree\n5. **May be inaccurate** — other methods exist that are more precise (random forests)\n\n**But:** The method is valuable for **structuring thinking**, even if numbers are approximate.\n\n## User Workflow\n\n### 1. Structuring\n\nAsk:\n- What are the action options?\n- What are possible outcomes?\n- What are values\u002Futility for each outcome?\n- How do we measure value? (money, utility units, happiness points)\n\n### 2. Probability Estimation\n\nHelp estimate through:\n- Historical data (if available)\n- Comparable situations\n- Expert judgment (user experience)\n- Subjective assessment (if no data)\n\n### 3. Visualization\n\nDraw tree in markdown:\n\n```\nDecision\n├─ Option A\n│  ├─ Outcome A1 (X%) → Value Y\n│  └─ Outcome A2 (Z%) → Value W\n└─ Option B\n   └─ Outcome B1 (100%) → Value V\n```\n\n### 4. EV Calculation\n\nFor each option:\n```\nEV_A = (X% × Y) + (Z% × W)\nEV_B = V\n```\n\n### 5. Recommendation\n\nOption with highest EV = best choice (rationally).\n\n**But add context:**\n- Risk tolerance (can user handle worst case)\n- Time horizon (when is result needed)\n- Other factors (reputational risk, emotions, ethics)\n\n## Application Examples by Domain\n\n### Trading & Investing\n\n**Position Sizing:**\n- Options: 5%, 10%, 20% of capital\n- Outcomes: Profit\u002Floss with different probabilities\n- Value: Absolute profit in $\n\n**Entry Timing:**\n- Options: Enter now, wait for -5%, wait for -10%\n- Outcomes: Price goes up\u002Fdown\n- Value: Opportunity cost vs better entry price\n\n### Business Strategy\n\n**Product Launch:**\n- Options: Launch \u002F don't launch\n- Outcomes: Success \u002F failure\n- Value: Revenue, market share, costs\n\n**Hiring Decision:**\n- Options: Hire candidate A \u002F candidate B \u002F don't hire\n- Outcomes: Successful onboarding \u002F quit after X months\n- Value: Productivity, costs, opportunity cost\n\n### Personal Decisions\n\n**Career Change:**\n- Options: Stay \u002F change job \u002F start business\n- Outcomes: Success \u002F failure in new role\n- Value: Salary, satisfaction, growth, risk\n\n**Real Estate:**\n- Options: Buy house A \u002F house B \u002F continue renting\n- Outcomes: Price increase \u002F decrease \u002F personal situation changes\n- Value: Net worth, monthly costs, quality of life\n\n### Operations\n\n**Capacity Planning:**\n- Options: Expand production \u002F outsource \u002F status quo\n- Outcomes: Demand increases \u002F decreases\n- Value: Profit, utilization, fixed costs\n\n**Vendor Selection:**\n- Options: Vendor A \u002F Vendor B \u002F in-house\n- Outcomes: Quality, reliability, failures\n- Value: Total cost of ownership\n\n## Calculator Script\n\nUse `scripts\u002Fdecision_tree.py` for automated EV calculations:\n\n```bash\npython3 scripts\u002Fdecision_tree.py --interactive\n```\n\nOr via JSON:\n\n```bash\npython3 scripts\u002Fdecision_tree.py --json tree.json\n```\n\nJSON format:\n\n```json\n{\n  \"decision\": \"Launch product?\",\n  \"options\": [\n    {\n      \"name\": \"Launch\",\n      \"outcomes\": [\n        {\"name\": \"Success\", \"probability\": 0.4, \"value\": 500000},\n        {\"name\": \"Failure\", \"probability\": 0.6, \"value\": -200000}\n      ]\n    },\n    {\n      \"name\": \"Don't launch\",\n      \"outcomes\": [\n        {\"name\": \"Status quo\", \"probability\": 1.0, \"value\": 0}\n      ]\n    }\n  ]\n}\n```\n\nOutput:\n\n```\n? Decision Tree Analysis\n\nDecision: Launch product?\n\nOption 1: Launch\n  └─ EV = $80,000.00\n     ├─ Success (40.0%) → +$500,000.00\n     └─ Failure (60.0%) → -$200,000.00\n\nOption 2: Don't launch\n  └─ EV = $0.00\n     └─ Status quo (100.0%) → $0.00\n\n✅ Recommendation: Launch (EV: $80,000.00)\n```\n\n## Final Checklist\n\nBefore giving recommendation, ensure:\n\n- ✅ All options covered\n- ✅ Probabilities sum to 100% for each branch\n- ✅ Values are realistic (not fantasies)\n- ✅ Worst case scenario is clear to user\n- ✅ Risk\u002Freward ratio is explicit\n- ✅ Method limitations mentioned\n- ✅ Qualitative context added (not just EV)\n\n## Method Advantages\n\n✅ **Simple** — people understand trees intuitively\n✅ **Visual** — clear structure\n✅ **Works with little data** — can use expert estimates\n✅ **White box** — transparent logic\n✅ **Worst\u002Fbest case** — extreme scenarios visible\n✅ **Multiple decision-makers** — can account for different interests\n\n## Method Disadvantages\n\n❌ **Unstable** — small data changes → large tree changes\n❌ **Inaccurate** — often more precise methods exist\n❌ **Subjective** — probability estimates \"from the head\"\n❌ **Complex** — becomes unwieldy with many outcomes\n❌ **Doesn't account for risk preference** — assumes risk neutrality\n\n## Important\n\nThe method is valuable for **structuring thinking**, but numbers are often taken from thin air.\n\nWhat matters more is the process — **forcing yourself to think through all branches** and explicitly evaluate consequences.\n\nDon't sell the decision as \"scientifically proven\" — it's just a framework for conscious choice.\n\n## Further Reading\n\n- Decision trees in operations research\n- Influence diagrams (more compact for complex decisions)\n- Utility functions (accounting for risk aversion)\n- Monte Carlo simulation (for greater accuracy)\n- Real options analysis (for strategic decisions)\n","# Decision Trees — Structured Decision-Making\n\nDecision tree analysis: a visual tool for making decisions with probabilities and expected value.\n\n## When to Use\n\n✅ **Good for:**\n- Business decisions (investments, hiring, product launches)\n- Personal choices (career, relocation, purchases)\n- Trading & investing (position sizing, entry\u002Fexit)\n- Operational decisions (expansion, outsourcing)\n- Any situation with measurable consequences\n\n❌ **Not suitable for:**\n- Decisions with true uncertainty (black swans)\n- Fast tactical choices\n- Purely emotional\u002Fethical questions\n\n## Method\n\n**Decision tree** = tree-like structure where:\n- **Decision nodes** (squares) — your actions\n- **Chance nodes** (circles) — random events\n- **End nodes** (triangles) — final outcomes\n\n**Process:**\n1. **Define options** — all possible actions\n2. **Define outcomes** — what can happen after each action\n3. **Estimate probabilities** — how likely is each outcome (0-100%)\n4. **Estimate values** — utility\u002Freward for each outcome (money, points, utility units)\n5. **Calculate EV** — expected value = Σ (probability × value)\n6. **Choose** — option with highest EV\n\n## Formula\n\n```\nEV = Σ (probability_i × value_i)\n```\n\n**Example:**\n- Outcome A: 70% probability, +$100 → 0.7 × 100 = $70\n- Outcome B: 30% probability, -$50 → 0.3 × (-50) = -$15\n- **EV = $70 + (-$15) = $55**\n\n## Classic Example (from Wikipedia)\n\n**Decision:** Go to party or stay home?\n\n### Estimates:\n- Party: +9 utility (fun)\n- Home: +3 utility (comfort)\n- Carrying jacket unnecessarily: -2 utility\n- Being cold: -10 utility\n- Probability cold: 70%\n- Probability warm: 30%\n\n### Tree:\n\n```\nDecision\n├─ Go to party\n│  ├─ Take jacket\n│  │  ├─ Cold (70%) → 9 utility (party)\n│  │  └─ Warm (30%) → 9 - 2 = 7 utility (carried unnecessarily)\n│  │  EV = 0.7 × 9 + 0.3 × 7 = 8.4\n│  └─ Don't take jacket\n│     ├─ Cold (70%) → 9 - 10 = -1 utility (froze)\n│     └─ Warm (30%) → 9 utility (perfect)\n│     EV = 0.7 × (-1) + 0.3 × 9 = 2.0\n└─ Stay home\n   └─ EV = 3.0 (always)\n```\n\n**Conclusion:** Go and take jacket (EV = 8.4) > stay home (EV = 3.0) > go without jacket (EV = 2.0)\n\n## Business Example\n\n**Decision:** Launch new product?\n\n### Estimates:\n- Success probability: 40%\n- Failure probability: 60%\n- Profit if success: $500K\n- Loss if failure: $200K\n- Don't launch: $0\n\n### Tree:\n\n```\nLaunch product\n├─ Success (40%) → +$500K\n└─ Failure (60%) → -$200K\n\nEV = (0.4 × 500K) + (0.6 × -200K) = 200K - 120K = +$80K\n\nDon't launch\n└─ EV = $0\n```\n\n**Conclusion:** Launch (EV = +$80K) is better than not launching ($0).\n\n## Trading Example\n\n**Decision:** Enter position or wait?\n\n### Estimates:\n- Probability of rise: 60%\n- Probability of fall: 40%\n- Position size: $1000\n- Target: +10% ($100 profit)\n- Stop-loss: -5% ($50 loss)\n\n### Tree:\n\n```\nEnter position\n├─ Rise (60%) → +$100\n└─ Fall (40%) → -$50\n\nEV = (0.6 × 100) + (0.4 × -50) = 60 - 20 = +$40\n\nWait\n└─ No position → $0\n\nEV = $0\n```\n\n**Conclusion:** Entering position has positive EV (+$40), better than waiting ($0).\n\n## Method Limitations\n\n⚠️ **Critical points:**\n\n1. **Subjective estimates** — probabilities often \"finger in the air\"\n2. **Doesn't account for risk appetite** — ignores psychology (loss aversion)\n3. **Simplified model** — reality is more complex\n4. **Unstable** — small data changes can drastically alter the tree\n5. **May be inaccurate** — other methods exist that are more precise (random forests)\n\n**But:** The method is valuable for **structuring thinking**, even if numbers are approximate.\n\n## User Workflow\n\n### 1. Structuring\n\nAsk:\n- What are the action options?\n- What are possible outcomes?\n- What are values\u002Futility for each outcome?\n- How do we measure value? (money, utility units, happiness points)\n\n### 2. Probability Estimation\n\nHelp estimate through:\n- Historical data (if available)\n- Comparable situations\n- Expert judgment (user experience)\n- Subjective assessment (if no data)\n\n### 3. Visualization\n\nDraw tree in markdown:\n\n```\nDecision\n├─ Option A\n│  ├─ Outcome A1 (X%) → Value Y\n│  └─ Outcome A2 (Z%) → Value W\n└─ Option B\n   └─ Outcome B1 (100%) → Value V\n```\n\n### 4. EV Calculation\n\nFor each option:\n```\nEV_A = (X% × Y) + (Z% × W)\nEV_B = V\n```\n\n### 5. Recommendation\n\nOption with highest EV = best choice (rationally).\n\n**But add context:**\n- Risk tolerance (can user handle worst case)\n- Time horizon (when is result needed)\n- Other factors (reputational risk, emotions, ethics)\n\n## Application Examples by Domain\n\n### Trading & Investing\n\n**Position Sizing:**\n- Options: 5%, 10%, 20% of capital\n- Outcomes: Profit\u002Floss with different probabilities\n- Value: Absolute profit in $\n\n**Entry Timing:**\n- Options: Enter now, wait for -5%, wait for -10%\n- Outcomes: Price goes up\u002Fdown\n- Value: Opportunity cost vs better entry price\n\n### Business Strategy\n\n**Product Launch:**\n- Options: Launch \u002F don't launch\n- Outcomes: Success \u002F failure\n- Value: Revenue, market share, costs\n\n**Hiring Decision:**\n- Options: Hire candidate A \u002F candidate B \u002F don't hire\n- Outcomes: Successful onboarding \u002F quit after X months\n- Value: Productivity, costs, opportunity cost\n\n### Personal Decisions\n\n**Career Change:**\n- Options: Stay \u002F change job \u002F start business\n- Outcomes: Success \u002F failure in new role\n- Value: Salary, satisfaction, growth, risk\n\n**Real Estate:**\n- Options: Buy house A \u002F house B \u002F continue renting\n- Outcomes: Price increase \u002F decrease \u002F personal situation changes\n- Value: Net worth, monthly costs, quality of life\n\n### Operations\n\n**Capacity Planning:**\n- Options: Expand production \u002F outsource \u002F status quo\n- Outcomes: Demand increases \u002F decreases\n- Value: Profit, utilization, fixed costs\n\n**Vendor Selection:**\n- Options: Vendor A \u002F Vendor B \u002F in-house\n- Outcomes: Quality, reliability, failures\n- Value: Total cost of ownership\n\n## Calculator Script\n\nUse `scripts\u002Fdecision_tree.py` for automated EV calculations:\n\n```bash\npython3 scripts\u002Fdecision_tree.py --interactive\n```\n\nOr via JSON:\n\n```bash\npython3 scripts\u002Fdecision_tree.py --json tree.json\n```\n\nJSON format:\n\n```json\n{\n  \"decision\": \"Launch product?\",\n  \"options\": [\n    {\n      \"name\": \"Launch\",\n      \"outcomes\": [\n        {\"name\": \"Success\", \"probability\": 0.4, \"value\": 500000},\n        {\"name\": \"Failure\", \"probability\": 0.6, \"value\": -200000}\n      ]\n    },\n    {\n      \"name\": \"Don't launch\",\n      \"outcomes\": [\n        {\"name\": \"Status quo\", \"probability\": 1.0, \"value\": 0}\n      ]\n    }\n  ]\n}\n```\n\nOutput:\n\n```\n? Decision Tree Analysis\n\nDecision: Launch product?\n\nOption 1: Launch\n  └─ EV = $80,000.00\n     ├─ Success (40.0%) → +$500,000.00\n     └─ Failure (60.0%) → -$200,000.00\n\nOption 2: Don't launch\n  └─ EV = $0.00\n     └─ Status quo (100.0%) → $0.00\n\n✅ Recommendation: Launch (EV: $80,000.00)\n```\n\n## Final Checklist\n\nBefore giving recommendation, ensure:\n\n- ✅ All options covered\n- ✅ Probabilities sum to 100% for each branch\n- ✅ Values are realistic (not fantasies)\n- ✅ Worst case scenario is clear to user\n- ✅ Risk\u002Freward ratio is explicit\n- ✅ Method limitations mentioned\n- ✅ Qualitative context added (not just EV)\n\n## Method Advantages\n\n✅ **Simple** — people understand trees intuitively\n✅ **Visual** — clear structure\n✅ **Works with little data** — can use expert estimates\n✅ **White box** — transparent logic\n✅ **Worst\u002Fbest case** — extreme scenarios visible\n✅ **Multiple decision-makers** — can account for different interests\n\n## Method Disadvantages\n\n❌ **Unstable** — small data changes → large tree changes\n❌ **Inaccurate** — often more precise methods exist\n❌ **Subjective** — probability estimates \"from the head\"\n❌ **Complex** — becomes unwieldy with many outcomes\n❌ **Doesn't account for risk preference** — assumes risk neutrality\n\n## Important\n\nThe method is valuable for **structuring thinking**, but numbers are often taken from thin air.\n\nWhat matters more is the process — **forcing yourself to think through all branches** and explicitly evaluate consequences.\n\nDon't sell the decision as \"scientifically proven\" — it's just a framework for conscious choice.\n\n## Further Reading\n\n- Decision trees in operations research\n- Influence diagrams (more compact for complex decisions)\n- Utility functions (accounting for risk aversion)\n- Monte Carlo simulation (for greater accuracy)\n- Real options analysis (for strategic decisions)\n",{"name":5,"description":7},"main","f4cb2d49f7667e79ef7804f50396d5aaffdf1f56","购物与电子商务\u002Fdecision-trees",1,"https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification","https:\u002F\u002Fgithub.com\u002FKernel8901","a little little little dobe in life but science geek","2026-04-05 16:43:56",{"source":8,"skillName":5,"commands":52,"prompt":62,"zipAvailable":63},[53,56,59],{"tool":54,"command":55},"npx","npx skills add Kernel8901\u002Fai-agent-skills-classification --skill decision-trees",{"tool":57,"command":58},"bunx","bunx skills add Kernel8901\u002Fai-agent-skills-classification --skill decision-trees",{"tool":60,"command":61},"pnpm","pnpm dlx skills add Kernel8901\u002Fai-agent-skills-classification --skill decision-trees","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F购物与电子商务\u002Fdecision-trees\u002FSKILL.md",true,[65,71,75],{"path":66,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":69,"sourceHtmlUrl":70},"_meta.json","REFERENCE","参考文档",280,"https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F购物与电子商务\u002Fdecision-trees\u002F_meta.json",{"path":72,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":73,"sourceHtmlUrl":74},"README.md",4685,"https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F购物与电子商务\u002Fdecision-trees\u002FREADME.md",{"path":76,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":79,"sourceHtmlUrl":80},"scripts\u002Fdecision_tree.py","SCRIPT","脚本",5014,"https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F购物与电子商务\u002Fdecision-trees\u002Fscripts\u002Fdecision_tree.py",[82,106,141,152,162,191],{"skillNo":83,"name":84,"nameZh":6,"description":85,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":86,"categories":87,"keywords":88,"publishedAt":38},"01M6Q5R6WZ06RRB9RQTW54B34B","zyfai","Earn yield on any Ethereum wallet on Base, Arbitrum, and Plasma. Use when a user wants passive DeFi yield on their funds. 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