[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6CZ5WQ9G79GZBGC0ZGYXKHF":3,"skill-same:01M6CZ5WQ9G79GZBGC0ZGYXKHF":65},{"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":25,"linkOnly":26,"markdownContent":27,"markdownBody":28,"frontMatter":29,"refName":30,"commitSha":31,"skillDirectory":32,"forksCount":33,"repositoryHtmlUrl":34,"repositoryHomepageUrl":6,"ownerHtmlUrl":35,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":36,"install":37,"resources":50},"01M6CZ5WQ9G79GZBGC0ZGYXKHF","stat-compare-two-groups",null,"Compare a continuous variable between two groups with automatic selection of t-test, Welch test, or Mann-Whitney U test based on data properties.","Scientific-Tooling\u002Fstructured-intelligence","Scientific-Tooling","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F267453265?v=4","01M6CZ1SE6GGD4F67EAQ0PVX38","Scientific Tooling","01M6CZ1SJKCFVGH10Z3A152K02",4,"MIT","https:\u002F\u002Fgithub.com\u002FScientific-Tooling\u002Fstructured-intelligence\u002Fblob\u002Fmain\u002Fskills\u002Fstat-compare-two-groups\u002FSKILL.md",[],[19,20,21,22,23,24],"stat compare two groups","test","data","properties","selection","t-test","2026-11-10 10:12:30",false,"---\nname: stat-compare-two-groups\ndescription: Compare a continuous variable between two groups with automatic selection of t-test, Welch test, or Mann-Whitney U test based on data properties.\n---\n\n# Skill: Two-Group Statistical Comparison\n\n## Use When\n\n- User wants to test whether a continuous measurement differs between two experimental groups (e.g., treated vs. control, knockout vs. wild-type)\n- User needs a p-value, effect size, and confidence interval for a two-group comparison\n- User is unsure which statistical test is appropriate and wants automatic method selection\n\n## Inputs\n\n- Required:\n  - Data table (CSV or TSV) with a numeric value column and a group column (exactly two group levels)\n- Optional:\n  - Group variable column name (default: auto-detected if only one non-numeric column)\n  - Value variable column name (default: auto-detected if only one numeric column)\n  - Paired comparison: `true` or `false` (default: `false`)\n  - Alternative hypothesis: `two-sided` (default), `greater`, or `less`\n  - Output directory (default: `.\u002Fcompare_output`)\n\n## Workflow\n\n1. Read data; validate that group column has exactly two levels.\n2. Run normality test (Shapiro-Wilk) on each group separately.\n3. If both groups are normal: run Levene's test for equal variance.\n   - Equal variance (p > 0.05): Student's t-test.\n   - Unequal variance (p ≤ 0.05): Welch's t-test.\n4. If either group is non-normal: Mann-Whitney U test (unpaired) or Wilcoxon signed-rank test (paired).\n5. Compute effect size: Cohen's d (parametric) or rank-biserial correlation r (non-parametric).\n6. Compute 95% confidence interval for the difference in means (parametric) or Hodges-Lehmann estimator (non-parametric).\n7. Generate a box plot with individual data points and significance annotation.\n8. Write a structured result and plot (PDF) to output directory.\n9. Report method used, test statistic, p-value, effect size, confidence interval, and interpretation.\n10. Save the executable analysis script (`analysis_code.py` or `analysis_code.R`) and software version manifest (`session_info.txt`) used to produce results.\n\n## Output Contract\n\n- Result (TSV): group1, group2, method_used, statistic, p_value, effect_size, effect_size_type, ci_lower, ci_upper, n_group1, n_group2\n- Box plot with data points (PDF)\n- Decision log explaining method selection (printed to stdout)\n- Reproducibility artifacts: `analysis_code.py` or `analysis_code.R`, plus `session_info.txt`\n\n## Limits\n\n- Requires exactly two groups; use `stat-compare-multiple-groups` for three or more groups.\n- Minimum recommended sample size: n ≥ 5 per group (results are unreliable below this).\n- Paired comparison requires equal sample sizes in both groups.\n- Common failure: group column contains more than two levels — filter data before running.\n","# Skill: Two-Group Statistical Comparison\n\n## Use When\n\n- User wants to test whether a continuous measurement differs between two experimental groups (e.g., treated vs. control, knockout vs. wild-type)\n- User needs a p-value, effect size, and confidence interval for a two-group comparison\n- User is unsure which statistical test is appropriate and wants automatic method selection\n\n## Inputs\n\n- Required:\n  - Data table (CSV or TSV) with a numeric value column and a group column (exactly two group levels)\n- Optional:\n  - Group variable column name (default: auto-detected if only one non-numeric column)\n  - Value variable column name (default: auto-detected if only one numeric column)\n  - Paired comparison: `true` or `false` (default: `false`)\n  - Alternative hypothesis: `two-sided` (default), `greater`, or `less`\n  - Output directory (default: `.\u002Fcompare_output`)\n\n## Workflow\n\n1. Read data; validate that group column has exactly two levels.\n2. Run normality test (Shapiro-Wilk) on each group separately.\n3. If both groups are normal: run Levene's test for equal variance.\n   - Equal variance (p > 0.05): Student's t-test.\n   - Unequal variance (p ≤ 0.05): Welch's t-test.\n4. If either group is non-normal: Mann-Whitney U test (unpaired) or Wilcoxon signed-rank test (paired).\n5. Compute effect size: Cohen's d (parametric) or rank-biserial correlation r (non-parametric).\n6. Compute 95% confidence interval for the difference in means (parametric) or Hodges-Lehmann estimator (non-parametric).\n7. Generate a box plot with individual data points and significance annotation.\n8. Write a structured result and plot (PDF) to output directory.\n9. Report method used, test statistic, p-value, effect size, confidence interval, and interpretation.\n10. Save the executable analysis script (`analysis_code.py` or `analysis_code.R`) and software version manifest (`session_info.txt`) used to produce results.\n\n## Output Contract\n\n- Result (TSV): group1, group2, method_used, statistic, p_value, effect_size, effect_size_type, ci_lower, ci_upper, n_group1, n_group2\n- Box plot with data points (PDF)\n- Decision log explaining method selection (printed to stdout)\n- Reproducibility artifacts: `analysis_code.py` or `analysis_code.R`, plus `session_info.txt`\n\n## Limits\n\n- Requires exactly two groups; use `stat-compare-multiple-groups` for three or more groups.\n- Minimum recommended sample size: n ≥ 5 per group (results are unreliable below this).\n- Paired comparison requires equal sample sizes in both groups.\n- Common failure: group column contains more than two levels — filter data before running.\n",{"name":5,"description":7},"main","5118e7fda890e9aa819cb71e003cd243d3a31970","skills\u002Fstat-compare-two-groups",1,"https:\u002F\u002Fgithub.com\u002FScientific-Tooling\u002Fstructured-intelligence","https:\u002F\u002Fgithub.com\u002FScientific-Tooling","2026-11-03 00:01:40",{"source":8,"skillName":5,"commands":38,"prompt":48,"zipAvailable":49},[39,42,45],{"tool":40,"command":41},"npx","npx skills add Scientific-Tooling\u002Fstructured-intelligence --skill stat-compare-two-groups",{"tool":43,"command":44},"bunx","bunx skills add Scientific-Tooling\u002Fstructured-intelligence --skill stat-compare-two-groups",{"tool":46,"command":47},"pnpm","pnpm dlx skills add Scientific-Tooling\u002Fstructured-intelligence --skill 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or update repository skills that conform to local templates, provider metadata requirements, registry rules, and validation workflows.","https:\u002F\u002Fgithub.com\u002FScientific-Tooling\u002Fstructured-intelligence\u002Fblob\u002Fmain\u002Fskills\u002Fstructured-intelligence-skill-creator\u002FSKILL.md",[72],{"categoryNo":73,"categoryName":74,"taxonomyType":75,"taxonomyTypeDesc":76},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93],"structured intelligence skill creator","skill 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