[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6X8DKTP13GGVP0352N4BTYM":3,"skill-same:01M6X8DKTP13GGVP0352N4BTYM":84},{"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":31,"publishedAt":50,"linkOnly":51,"markdownContent":52,"markdownBody":53,"frontMatter":54,"refName":61,"commitSha":62,"skillDirectory":63,"forksCount":64,"repositoryHtmlUrl":65,"repositoryHomepageUrl":6,"ownerHtmlUrl":66,"ownerBlogUrl":67,"ownerDescription":68,"updatedAt":69,"install":70,"resources":83},"01M6X8DKTP13GGVP0352N4BTYM","statistical-reporting",null,"Statistical test selection, assumption checking, and APA-formatted reporting. Use when analyzing experimental results or writing results sections.","enuno\u002Fcrewless-capital","enuno","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6727752?v=4","01M6J09HF6Y74150F7PEMC446X","Elvis Nuno","01M6X86WGZG9RH09ACTSTJ2383",1,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fenuno\u002Fcrewless-capital\u002Fblob\u002Fmain\u002Fmigrate-hyperliquid-firm\u002Fmulticlaw\u002FAutoResearchClaw\u002F.claude\u002Fskills\u002Fstatistical-reporting\u002FSKILL.md",[18,23,26],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":27,"categoryName":28,"taxonomyType":29,"taxonomyTypeDesc":30},"01JBSEEDCAT000000000000036","内容创作者","PROFESSION","适用职业",[32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49],"reporting","statistical reporting","selection","when","value","researchclaw","size","scientific","t-test","ANOVA","writing","use","results","effect","regression","hypothesis","analyzing","test","2026-11-10 10:12:30",false,"---\nname: statistical-reporting\ndescription: Statistical test selection, assumption checking, and APA-formatted reporting. Use when analyzing experimental results or writing results sections.\nmetadata:\n  category: writing\n  trigger-keywords: \"statistic,hypothesis test,p-value,regression,ANOVA,t-test,effect size,confidence interval\"\n  applicable-stages: \"14,17\"\n  priority: \"3\"\n  version: \"1.0\"\n  author: researchclaw\n  references: \"adapted from K-Dense-AI\u002Fclaude-scientific-skills\"\n---\n\n## Statistical Reporting Best Practice\n\n### Test Selection Quick Reference\n1. **Comparing two groups (independent, normal)**: Independent t-test\n2. **Comparing two groups (independent, non-normal)**: Mann-Whitney U test\n3. **Comparing two groups (paired, normal)**: Paired t-test\n4. **Comparing two groups (paired, non-normal)**: Wilcoxon signed-rank test\n5. **Comparing 3+ groups (independent, normal)**: One-way ANOVA + post-hoc\n6. **Comparing 3+ groups (non-normal)**: Kruskal-Wallis test\n7. **Relationship between continuous variables**: Pearson or Spearman correlation\n8. **Categorical outcomes**: Chi-square or Fisher's exact test\n9. **Predicting continuous outcome**: Linear regression\n10. **Predicting binary outcome**: Logistic regression\n\n### Assumption Checking\n1. **Normality**: Shapiro-Wilk test (n \u003C 50) or visual Q-Q plots\n2. **Homogeneity of variance**: Levene's test before t-tests and ANOVA\n3. **Independence**: Verify study design ensures independent observations\n4. **Linearity**: Scatter plots and residual plots for regression\n5. **Multicollinearity**: VIF \u003C 5 for multiple regression predictors\n6. When assumptions are violated, use non-parametric alternatives or robust methods\n\n### APA Reporting Format\n1. **t-test**: t(df) = X.XX, p = .XXX, d = X.XX\n2. **ANOVA**: F(df_between, df_within) = X.XX, p = .XXX, eta-squared = .XX\n3. **Correlation**: r(df) = .XX, p = .XXX [95% CI: .XX, .XX]\n4. **Chi-square**: chi-square(df, N = XXX) = X.XX, p = .XXX\n5. **Regression**: beta = X.XX, SE = X.XX, t = X.XX, p = .XXX\n6. Always report exact p-values (not \"p \u003C .05\") unless p \u003C .001\n7. Use leading zero for values that can exceed 1 (e.g., t = 0.50) but not for those bounded by 1 (e.g., p = .032, r = .45)\n\n### Effect Sizes\n1. ALWAYS report effect sizes alongside p-values\n2. Cohen's d for group comparisons: small = 0.2, medium = 0.5, large = 0.8\n3. Eta-squared for ANOVA: small = .01, medium = .06, large = .14\n4. R-squared for regression: report adjusted R-squared for multiple predictors\n5. Odds ratios for logistic regression with 95% confidence intervals\n6. Distinguish statistical significance from practical significance\n\n### Common Mistakes to Avoid\n1. Never say \"the results were not significant, therefore there is no effect\"\n2. Do not confuse correlation with causation in observational data\n3. Apply multiple comparison corrections (Bonferroni, FDR) when running many tests\n4. Report confidence intervals, not just point estimates\n5. State whether tests are one-tailed or two-tailed and justify the choice\n","## Statistical Reporting Best Practice\n\n### Test Selection Quick Reference\n1. **Comparing two groups (independent, normal)**: Independent t-test\n2. **Comparing two groups (independent, non-normal)**: Mann-Whitney U test\n3. **Comparing two groups (paired, normal)**: Paired t-test\n4. **Comparing two groups (paired, non-normal)**: Wilcoxon signed-rank test\n5. **Comparing 3+ groups (independent, normal)**: One-way ANOVA + post-hoc\n6. **Comparing 3+ groups (non-normal)**: Kruskal-Wallis test\n7. **Relationship between continuous variables**: Pearson or Spearman correlation\n8. **Categorical outcomes**: Chi-square or Fisher's exact test\n9. **Predicting continuous outcome**: Linear regression\n10. **Predicting binary outcome**: Logistic regression\n\n### Assumption Checking\n1. **Normality**: Shapiro-Wilk test (n \u003C 50) or visual Q-Q plots\n2. **Homogeneity of variance**: Levene's test before t-tests and ANOVA\n3. **Independence**: Verify study design ensures independent observations\n4. **Linearity**: Scatter plots and residual plots for regression\n5. **Multicollinearity**: VIF \u003C 5 for multiple regression predictors\n6. When assumptions are violated, use non-parametric alternatives or robust methods\n\n### APA Reporting Format\n1. **t-test**: t(df) = X.XX, p = .XXX, d = X.XX\n2. **ANOVA**: F(df_between, df_within) = X.XX, p = .XXX, eta-squared = .XX\n3. **Correlation**: r(df) = .XX, p = .XXX [95% CI: .XX, .XX]\n4. **Chi-square**: chi-square(df, N = XXX) = X.XX, p = .XXX\n5. **Regression**: beta = X.XX, SE = X.XX, t = X.XX, p = .XXX\n6. Always report exact p-values (not \"p \u003C .05\") unless p \u003C .001\n7. Use leading zero for values that can exceed 1 (e.g., t = 0.50) but not for those bounded by 1 (e.g., p = .032, r = .45)\n\n### Effect Sizes\n1. ALWAYS report effect sizes alongside p-values\n2. Cohen's d for group comparisons: small = 0.2, medium = 0.5, large = 0.8\n3. Eta-squared for ANOVA: small = .01, medium = .06, large = .14\n4. R-squared for regression: report adjusted R-squared for multiple predictors\n5. Odds ratios for logistic regression with 95% confidence intervals\n6. Distinguish statistical significance from practical significance\n\n### Common Mistakes to Avoid\n1. Never say \"the results were not significant, therefore there is no effect\"\n2. Do not confuse correlation with causation in observational data\n3. Apply multiple comparison corrections (Bonferroni, FDR) when running many tests\n4. Report confidence intervals, not just point estimates\n5. State whether tests are one-tailed or two-tailed and justify the choice\n",{"name":5,"metadata":55,"description":7},{"author":37,"version":56,"category":42,"priority":57,"references":58,"trigger-keywords":59,"applicable-stages":60},"1.0","3","adapted from K-Dense-AI\u002Fclaude-scientific-skills","statistic,hypothesis test,p-value,regression,ANOVA,t-test,effect size,confidence interval","14,17","main","bfe4e4653cba696a8dc20f70c6006651b997fdef","migrate-hyperliquid-firm\u002Fmulticlaw\u002FAutoResearchClaw\u002F.claude\u002Fskills\u002Fstatistical-reporting",0,"https:\u002F\u002Fgithub.com\u002Fenuno\u002Fcrewless-capital","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 10:49:15",{"source":8,"skillName":5,"commands":71,"prompt":81,"zipAvailable":82},[72,75,78],{"tool":73,"command":74},"npx","npx skills add enuno\u002Fcrewless-capital --skill statistical-reporting",{"tool":76,"command":77},"bunx","bunx skills add enuno\u002Fcrewless-capital --skill statistical-reporting",{"tool":79,"command":80},"pnpm","pnpm dlx skills add enuno\u002Fcrewless-capital --skill statistical-reporting","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fenuno\u002Fcrewless-capital\u002Fblob\u002Fmain\u002Fmigrate-hyperliquid-firm\u002Fmulticlaw\u002FAutoResearchClaw\u002F.claude\u002Fskills\u002Fstatistical-reporting\u002FSKILL.md",true,[],[85,113,140,165,190,211],{"skillNo":86,"name":87,"nameZh":6,"description":88,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":89,"categories":90,"keywords":94,"publishedAt":50},"01M6X94YFR1XNANZ321FS661NG","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\u002Fcrewless-capital\u002Fblob\u002Fmain\u002Fmigrate-hyperliquid-firm\u002Fskills\u002Fsupermemory\u002FSKILL.md",[91],{"categoryNo":92,"categoryName":93,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",[87,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,35],"ai agents","memory","rag","semantic search","infrastructure","search","applications","api","knowledge","context","Art","personalization","across","learned","retention","for","persistent memory","static",{"skillNo":114,"name":115,"nameZh":6,"description":116,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":117,"licenseFileUrl":6,"sourceHtmlUrl":118,"categories":119,"keywords":120,"publishedAt":50},"01M6X94S3K97CSK7VFF5H85ED2","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\u002Fcrewless-capital\u002Fblob\u002Fmain\u002Fmigrate-hyperliquid-firm\u002Fskills\u002Fsenpi-skills\u002Fwolverine\u002FSKILL.md",[],[121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,110,137,138,139],"strategy","wolverine strategy","architecture","alpha","Volatility","consensus","only","trading","scanner","manage","max","hyperliquid","runtime","leverage","hunter","at","managed","dsl","phase",{"skillNo":141,"name":142,"nameZh":6,"description":143,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":117,"licenseFileUrl":6,"sourceHtmlUrl":144,"categories":145,"keywords":149,"publishedAt":50},"01M6X94HN3CHBM37GM2C84KT26","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\u002Fcrewless-capital\u002Fblob\u002Fmain\u002Fmigrate-hyperliquid-firm\u002Fskills\u002Fsenpi-skills\u002Fwolf-strategy\u002FSKILL.md",[146],{"categoryNo":147,"categoryName":148,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[121,150,151,152,123,153,154,155,156,157,158,159,160,134,161,162,136,163,164,110],"wolf strategy","MCP","openclaw","health","system","python3","check","health check","stop","strategies","shared","connection","wallets","stop-loss","hyperliquid perps",{"skillNo":166,"name":167,"nameZh":6,"description":168,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":117,"licenseFileUrl":6,"sourceHtmlUrl":169,"categories":170,"keywords":172,"publishedAt":50},"01M6X949FBGWWGY3YZ8N3WB3CP","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\u002Fcrewless-capital\u002Fblob\u002Fmain\u002Fmigrate-hyperliquid-firm\u002Fskills\u002Fsenpi-skills\u002Fwolf-howl\u002FSKILL.md",[171],{"categoryNo":147,"categoryName":148,"taxonomyType":21,"taxonomyTypeDesc":22},[173,151,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,138,189,35],"wolf howl","review","performance","learn","analysis","loop","analyze","short","autonomous","up","optimize","drift","self","factor","loops","profit","fdr",{"skillNo":191,"name":192,"nameZh":6,"description":193,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":194,"categories":195,"keywords":196,"publishedAt":50},"01M6X9437CQ55N5AYHCPF71JWG","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.","https:\u002F\u002Fgithub.com\u002Fenuno\u002Fcrewless-capital\u002Fblob\u002Fmain\u002Fmigrate-hyperliquid-firm\u002Fskills\u002Fsenpi-skills\u002Fwhale-index\u002FSKILL.md",[],[197,198,199,175,200,201,202,43,203,182,204,205,159,206,207,208,209,110,210,35],"index","whale","whale index","auth","monitoring","discovery","auto","daily","time","with","consistency","top","mirror","pnl",{"skillNo":212,"name":213,"nameZh":6,"description":214,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":117,"licenseFileUrl":6,"sourceHtmlUrl":215,"categories":216,"keywords":217,"publishedAt":50},"01M6X93YTRGNPYNHZRTW9XNH1E","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\u002Fcrewless-capital\u002Fblob\u002Fmain\u002Fmigrate-hyperliquid-firm\u002Fskills\u002Fsenpi-skills\u002Fvixen\u002FSKILL.md",[],[121,218,219,220,221,222,129,223,224,225,206,226,227,228,229,230,231,232,138,139],"vixen strategy","roi","data","first","confirmation","volume","filter","asset","explosion","mode","high","v1","modes","emerging movers","entry"]