[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6J0FJCYHAH6PWEG5DXZ1YYM":3,"skill-same:01M6J0FJCYHAH6PWEG5DXZ1YYM":64},{"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":29,"linkOnly":30,"markdownContent":31,"markdownBody":32,"frontMatter":33,"refName":41,"commitSha":42,"skillDirectory":43,"forksCount":44,"repositoryHtmlUrl":45,"repositoryHomepageUrl":6,"ownerHtmlUrl":46,"ownerBlogUrl":47,"ownerDescription":48,"updatedAt":49,"install":50,"resources":63},"01M6J0FJCYHAH6PWEG5DXZ1YYM","hypothesis-formulation",null,"Structured scientific hypothesis generation from observations. Use when formulating testable hypotheses, competing explanations, or experimental predictions.","enuno\u002Fhyperliquid-trading-firm","enuno","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6727752?v=4","01M6J09HF6Y74150F7PEMC446X","Elvis Nuno","01M6J09HKK0YMKE39JXT8JT8VM",1,"GPL-3.0","https:\u002F\u002Fgithub.com\u002Fenuno\u002Fhyperliquid-trading-firm\u002Fblob\u002Fmain\u002Fmulticlaw\u002FAutoResearchClaw\u002F.claude\u002Fskills\u002Fhypothesis-formulation\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28],"hypothesis","hypothesis formulation","hypothesis generation","use","experiment","structured","generation","predictions","scientific","observations","2026-11-10 10:12:30",false,"---\nname: hypothesis-formulation\ndescription: Structured scientific hypothesis generation from observations. Use when formulating testable hypotheses, competing explanations, or experimental predictions.\nmetadata:\n  category: experiment\n  trigger-keywords: \"hypothesis,prediction,mechanism,falsifiable,null,alternative,testable\"\n  applicable-stages: \"7,8,9\"\n  priority: \"3\"\n  version: \"1.0\"\n  author: researchclaw\n  references: \"adapted from K-Dense-AI\u002Fclaude-scientific-skills\"\n---\n\n## Hypothesis Formulation Best Practice\n\n### Structured Hypothesis Development\n1. Start with a clear observation or pattern that requires explanation\n2. Review existing literature for known mechanisms and prior explanations\n3. Identify what is already established vs. what remains uncertain\n4. Formulate the hypothesis as a specific, testable statement\n5. Ensure the hypothesis is falsifiable — define what outcome would refute it\n\n### Hypothesis Format\n1. **Null hypothesis (H0)**: There is no effect or no difference\n2. **Alternative hypothesis (H1)**: There is a specific, directional effect\n3. State both explicitly; design experiments to reject H0\n4. Use \"If... then... because...\" structure for mechanistic hypotheses:\n   - If [independent variable is manipulated], then [predicted outcome], because [proposed mechanism]\n\n### Generating Competing Hypotheses\n1. Propose at least 2-3 plausible explanations for the same observation\n2. For each, identify unique predictions that distinguish it from alternatives\n3. Rank hypotheses by parsimony, consistency with prior evidence, and testability\n4. Design experiments that can discriminate between competing hypotheses\n5. Consider confounding variables that could produce the same observation\n\n### Testable Predictions\n1. Derive specific, measurable predictions from each hypothesis\n2. Define expected effect direction AND approximate magnitude\n3. Specify what experimental conditions would confirm vs. refute the prediction\n4. Identify potential confounds and plan controls to address them\n5. Ensure predictions are achievable with available methods and resources\n\n### Aligning with Experimental Design\n1. Map each hypothesis to a concrete experimental condition or comparison\n2. Ensure sample size is adequate to detect the predicted effect (power analysis)\n3. Pre-register hypotheses and analysis plans when possible\n4. Distinguish confirmatory (hypothesis-testing) from exploratory analyses\n5. Plan for both positive and null results — what will you conclude in each case?\n","## Hypothesis Formulation Best Practice\n\n### Structured Hypothesis Development\n1. Start with a clear observation or pattern that requires explanation\n2. Review existing literature for known mechanisms and prior explanations\n3. Identify what is already established vs. what remains uncertain\n4. Formulate the hypothesis as a specific, testable statement\n5. Ensure the hypothesis is falsifiable — define what outcome would refute it\n\n### Hypothesis Format\n1. **Null hypothesis (H0)**: There is no effect or no difference\n2. **Alternative hypothesis (H1)**: There is a specific, directional effect\n3. State both explicitly; design experiments to reject H0\n4. Use \"If... then... because...\" structure for mechanistic hypotheses:\n   - If [independent variable is manipulated], then [predicted outcome], because [proposed mechanism]\n\n### Generating Competing Hypotheses\n1. Propose at least 2-3 plausible explanations for the same observation\n2. For each, identify unique predictions that distinguish it from alternatives\n3. Rank hypotheses by parsimony, consistency with prior evidence, and testability\n4. Design experiments that can discriminate between competing hypotheses\n5. Consider confounding variables that could produce the same observation\n\n### Testable Predictions\n1. Derive specific, measurable predictions from each hypothesis\n2. Define expected effect direction AND approximate magnitude\n3. Specify what experimental conditions would confirm vs. refute the prediction\n4. Identify potential confounds and plan controls to address them\n5. Ensure predictions are achievable with available methods and resources\n\n### Aligning with Experimental Design\n1. Map each hypothesis to a concrete experimental condition or comparison\n2. Ensure sample size is adequate to detect the predicted effect (power analysis)\n3. Pre-register hypotheses and analysis plans when possible\n4. Distinguish confirmatory (hypothesis-testing) from exploratory analyses\n5. Plan for both positive and null results — what will you conclude in each case?\n",{"name":5,"metadata":34,"description":7},{"author":35,"version":36,"category":23,"priority":37,"references":38,"trigger-keywords":39,"applicable-stages":40},"researchclaw","1.0","3","adapted from K-Dense-AI\u002Fclaude-scientific-skills","hypothesis,prediction,mechanism,falsifiable,null,alternative,testable","7,8,9","main","cc32fdacf303e9c64064829787a5e690bb29cd47","multiclaw\u002FAutoResearchClaw\u002F.claude\u002Fskills\u002Fhypothesis-formulation",0,"https:\u002F\u002Fgithub.com\u002Fenuno\u002Fhyperliquid-trading-firm","https:\u002F\u002Fgithub.com\u002Fenuno","https:\u002F\u002Fenuno.github.io\u002FDynamicCareerCanvas","Digital Nomad, Blockchain Evangel, and Digital Solutions Architect. \r\nCreator of TerraHash Stack.\r\nCEO of Ryno Crypto Mining Services.","2026-07-02 12:46:02",{"source":8,"skillName":5,"commands":51,"prompt":61,"zipAvailable":62},[52,55,58],{"tool":53,"command":54},"npx","npx skills add enuno\u002Fhyperliquid-trading-firm --skill hypothesis-formulation",{"tool":56,"command":57},"bunx","bunx skills add enuno\u002Fhyperliquid-trading-firm --skill hypothesis-formulation",{"tool":59,"command":60},"pnpm","pnpm dlx skills add enuno\u002Fhyperliquid-trading-firm --skill hypothesis-formulation","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fenuno\u002Fhyperliquid-trading-firm\u002Fblob\u002Fmain\u002Fmulticlaw\u002FAutoResearchClaw\u002F.claude\u002Fskills\u002Fhypothesis-formulation\u002FSKILL.md",true,[],[65,96,123,147,172,195],{"skillNo":66,"name":67,"nameZh":6,"description":68,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":69,"categories":70,"keywords":76,"publishedAt":29},"01M6J14V7SBT7MBPZ9VK8NRQR8","supermemory","Supermemory is a state-of-the-art memory and context infrastructure for AI agents. Use this skill when building applications that need persistent memory, user personalization, long-term context retention, or semantic search across knowledge bases. It provides Memory API for learned user context, User Profiles for static\u002Fdynamic facts, and RAG for semantic search. Perfect for chatbots, assistants, and knowledge-intensive applications.","https:\u002F\u002Fgithub.com\u002Fenuno\u002Fhyperliquid-trading-firm\u002Fblob\u002Fmain\u002Fskills\u002Fsupermemory\u002FSKILL.md",[71],{"categoryNo":72,"categoryName":73,"taxonomyType":74,"taxonomyTypeDesc":75},"01JBSEEDCAT000000000000010","后端与接口","SCENARIO","使用场景",[67,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95],"ai agents","memory","rag","semantic search","infrastructure","search","user context","applications","api","knowledge","context","Art","personalization","across","learned","retention","profiles","for","persistent memory",{"skillNo":97,"name":98,"nameZh":6,"description":99,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":100,"licenseFileUrl":6,"sourceHtmlUrl":101,"categories":102,"keywords":103,"publishedAt":29},"01M6J14PEXHE2QBAWM3YZ2GM63","wolverine-strategy","WOLVERINE v2.0 — HYPE alpha hunter. Entry-only scanner, DSL-exit-only architecture. v1.1 lost -22.7% because the scanner's thesis exit chopped 25\u002F27 trades before DSL could manage them. v2.0 removes thesis exit entirely. Scanner decides entries (score 8+, 4H\u002F1H aligned, SM consensus). DSL manages all exits (wide Phase 1 for HYPE volatility, trailing tiers starting at +15% ROE). Leverage lowered to 7x. Max 4 entries\u002Fday. 3-hour cooldown between entries. DSL exit managed by plugin runtime via runtime.yaml.","MIT","https:\u002F\u002Fgithub.com\u002Fenuno\u002Fhyperliquid-trading-firm\u002Fblob\u002Fmain\u002Fskills\u002Fsenpi-skills\u002Fwolverine\u002FSKILL.md",[],[104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,94],"strategy","wolverine strategy","architecture","alpha","Volatility","consensus","only","trading","scanner","manage","max","exit","hyperliquid","entries","thesis","runtime","leverage","hunter","at",{"skillNo":124,"name":125,"nameZh":6,"description":126,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":100,"licenseFileUrl":6,"sourceHtmlUrl":127,"categories":128,"keywords":132,"publishedAt":29},"01M6J14FQY9YG5QB45QK4D98AZ","wolf-strategy","WOLF v6.3 — Fully autonomous multi-strategy trading for Hyperliquid perps via Senpi MCP. Manages multiple strategies simultaneously, each with independent wallets, budgets, slots, and DSL configs. 5+N cron architecture: 5 shared wolf crons (Emerging Movers 3min, SM Flip 5min, Watchdog 5min, Risk Guardian 5min, Health Check 10min) plus one DSL v5.2 cron per strategy (native Hyperliquid SL sync via dsl-dynamic-stop-loss skill v5.3.1). Same asset can be traded in different strategies simultaneously. Enter early on first jumps, not at confirmed peaks. Dynamic risk-based leverage per strategy. Requires Senpi MCP connection, python3, mcporter CLI, OpenClaw cron system, and dsl-dynamic-stop-loss skill (provides dsl-cli.py + dsl-v5.py).","https:\u002F\u002Fgithub.com\u002Fenuno\u002Fhyperliquid-trading-firm\u002Fblob\u002Fmain\u002Fskills\u002Fsenpi-skills\u002Fwolf-strategy\u002FSKILL.md",[129],{"categoryNo":130,"categoryName":131,"taxonomyType":74,"taxonomyTypeDesc":75},"01JBSEEDCAT000000000000023","Skill与智能体开发",[104,133,134,135,136,137,106,138,139,111,116,140,141,142,120,143,144,145,122,146],"wolf strategy","MCP","openclaw","cli","sync","watchdog","autonomous","perps","strategies","shared","asset","native","wallets","hyperliquid perps",{"skillNo":148,"name":149,"nameZh":6,"description":150,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":100,"licenseFileUrl":6,"sourceHtmlUrl":151,"categories":152,"keywords":154,"publishedAt":29},"01M6J14A20TMQ772M3H80XNB2T","wolf-howl","HOWL v2 — Hunt, Optimize, Win, Learn. Nightly self-improvement loop for the WOLF autonomous trading strategy. Runs once per day (via cron) to review all trades from the last 24 hours, compute win rates, analyze signal quality correlation, evaluate DSL tier performance, identify missed opportunities, and produce concrete improvement suggestions for the wolf-strategy skill. v2 adds fee drag ratio (FDR) analysis, holding period bucketing, LONG vs SHORT regime detection, rotation cost tracking, cumulative drift detection, and gross vs net profit factor separation. Use when setting up daily trade review automation, analyzing trading performance, or improving an autonomous trading strategy through data-driven feedback loops. Requires Senpi MCP connection, mcporter CLI, and OpenClaw cron system.","https:\u002F\u002Fgithub.com\u002Fenuno\u002Fhyperliquid-trading-firm\u002Fblob\u002Fmain\u002Fskills\u002Fsenpi-skills\u002Fwolf-howl\u002FSKILL.md",[153],{"categoryNo":130,"categoryName":131,"taxonomyType":74,"taxonomyTypeDesc":75},[155,134,156,136,157,158,159,160,161,162,139,163,164,165,166,167,168,169,170,171],"wolf howl","review","performance","learn","analysis","loop","analyze","short","up","optimize","drift","missed","self","factor","loops","profit","data-driven",{"skillNo":173,"name":174,"nameZh":6,"description":175,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":176,"licenseFileUrl":6,"sourceHtmlUrl":177,"categories":178,"keywords":179,"publishedAt":29},"01M6J1469QYZMD7E6JDP8Z9X1V","whale-index","Auto-mirror top Discovery traders on Hyperliquid. Scans top 50 traders, scores on PnL rank (35%), win rate (25%), consistency (20%), hold time (10%), drawdown (10%). Creates 2-5 mirror strategies with overlap checks. Daily rebalance with 2-day watch period before swaps. Use when setting up trader mirroring, copy trading, or portfolio auto-rebalancing based on Discovery leaderboard performance.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fenuno\u002Fhyperliquid-trading-firm\u002Fblob\u002Fmain\u002Fskills\u002Fsenpi-skills\u002Fwhale-index\u002FSKILL.md",[],[180,181,182,157,183,184,185,22,186,187,163,188,189,141,190,191,192,193,94,194],"index","whale","whale index","auth","monitoring","discovery","auto","portfolio","daily","time","with","consistency","top","mirror","pnl",{"skillNo":196,"name":197,"nameZh":6,"description":198,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":100,"licenseFileUrl":6,"sourceHtmlUrl":199,"categories":200,"keywords":201,"publishedAt":29},"01M6J141MJ2JMM7X3BGEEW30J9","vixen-strategy","VIXEN v1.0 — Dual-mode emerging movers scanner. Built from FOX v1.6 live trading data (+34.5% ROI). Two entry modes: STALKER (steady accumulation, score 6+, catches the SM buildup BEFORE the explosion) and STRIKER (violent FIRST_JUMP, score 9+, with raw volume confirmation to filter blow-off tops). 2-hour per-asset cooldown after Phase 1 exits. DSL High Water Mode mandatory.","https:\u002F\u002Fgithub.com\u002Fenuno\u002Fhyperliquid-trading-firm\u002Fblob\u002Fmain\u002Fskills\u002Fsenpi-skills\u002Fvixen\u002FSKILL.md",[],[104,202,203,204,205,206,111,112,116,207,208,209,143,190,210,211,212,213,214,215],"vixen strategy","roi","data","first","confirmation","volume","live","filter","explosion","mode","high","v1","modes","emerging movers"]