[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6CZ5DXCZAB8THYDX7KFF246":3,"skill-same:01M6CZ5DXCZAB8THYDX7KFF246":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":24,"linkOnly":25,"markdownContent":26,"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},"01M6CZ5DXCZAB8THYDX7KFF246","stat-analyze-distribution",null,"Characterize the distribution of one or more numeric variables with summary statistics, normality tests, and diagnostic plots.","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-analyze-distribution\u002FSKILL.md",[],[19,20,21,22,23],"analyze","stat analyze distribution","summary","statistics","tests","2026-11-10 10:12:30",false,"---\nname: stat-analyze-distribution\ndescription: Characterize the distribution of one or more numeric variables with summary statistics, normality tests, and diagnostic plots.\n---\n\n# Skill: Statistical Distribution Analysis\n\n## Use When\n\n- User wants to understand the distribution of a numeric variable before choosing a statistical test\n- User needs to check whether data is approximately normally distributed\n- User wants summary statistics (mean, median, variance, skewness, kurtosis) for one or more columns\n- User is preparing an EDA (exploratory data analysis) report\n\n## Inputs\n\n- Required:\n  - Data table (CSV or TSV) with at least one numeric column\n- Optional:\n  - Column(s) to analyze (default: all numeric columns)\n  - Normality test method: `shapiro` (default, n ≤ 5000) or `anderson` (Anderson-Darling, recommended for larger samples)\n  - Output directory (default: `.\u002Fdist_output`)\n\n## Workflow\n\n1. Read the data file; identify numeric columns to analyze (or use user-specified subset).\n2. For each target column, compute summary statistics: mean, median, standard deviation, variance, min, max, 25th\u002F75th percentiles, skewness, kurtosis.\n3. Run normality test:\n   - Shapiro-Wilk when n ≤ 5000\n   - Anderson-Darling when n > 5000\n   Report test statistic, p-value (or significance level where applicable), and sample size.\n4. Classify each variable as `approximately_normal` or `non_normal` using both test output and Q-Q plot inspection (do not classify from hypothesis-test p-value alone).\n5. Generate plots for each variable: histogram with density overlay, Q-Q plot.\n6. If multiple variables, produce a summary table across all variables.\n7. Write summary statistics table (TSV) and plots (PDF) to output directory.\n8. Save an executable analysis script (`analysis_code.py` or `analysis_code.R`) and software version manifest (`session_info.txt`) used to produce results.\n9. Report which variables appear normally distributed and suggest appropriate downstream tests.\n\n## Output Contract\n\n- Summary statistics table (TSV): variable, n, mean, median, sd, variance, skewness, kurtosis, normality_test, test_statistic, p_value_or_significance, normality_call\n- Histogram + density plot per variable (PDF)\n- Q-Q plot per variable (PDF)\n- Reproducibility artifacts: `analysis_code.py` or `analysis_code.R`, plus `session_info.txt`\n\n## Limits\n\n- Shapiro-Wilk test is unreliable for very large n; use Anderson-Darling and visual diagnostics instead.\n- Avoid one-sample KS tests against a fitted normal distribution unless a corrected procedure is explicitly implemented.\n- Normality tests have low power for small samples (n \u003C 20); visual inspection of Q-Q plot is recommended.\n- Does not handle categorical or date columns; these are silently skipped.\n- Common failure: non-numeric columns passed explicitly cause an error — pre-filter if needed.\n","# Skill: Statistical Distribution Analysis\n\n## Use When\n\n- User wants to understand the distribution of a numeric variable before choosing a statistical test\n- User needs to check whether data is approximately normally distributed\n- User wants summary statistics (mean, median, variance, skewness, kurtosis) for one or more columns\n- User is preparing an EDA (exploratory data analysis) report\n\n## Inputs\n\n- Required:\n  - Data table (CSV or TSV) with at least one numeric column\n- Optional:\n  - Column(s) to analyze (default: all numeric columns)\n  - Normality test method: `shapiro` (default, n ≤ 5000) or `anderson` (Anderson-Darling, recommended for larger samples)\n  - Output directory (default: `.\u002Fdist_output`)\n\n## Workflow\n\n1. Read the data file; identify numeric columns to analyze (or use user-specified subset).\n2. For each target column, compute summary statistics: mean, median, standard deviation, variance, min, max, 25th\u002F75th percentiles, skewness, kurtosis.\n3. Run normality test:\n   - Shapiro-Wilk when n ≤ 5000\n   - Anderson-Darling when n > 5000\n   Report test statistic, p-value (or significance level where applicable), and sample size.\n4. Classify each variable as `approximately_normal` or `non_normal` using both test output and Q-Q plot inspection (do not classify from hypothesis-test p-value alone).\n5. Generate plots for each variable: histogram with density overlay, Q-Q plot.\n6. If multiple variables, produce a summary table across all variables.\n7. Write summary statistics table (TSV) and plots (PDF) to output directory.\n8. Save an executable analysis script (`analysis_code.py` or `analysis_code.R`) and software version manifest (`session_info.txt`) used to produce results.\n9. Report which variables appear normally distributed and suggest appropriate downstream tests.\n\n## Output Contract\n\n- Summary statistics table (TSV): variable, n, mean, median, sd, variance, skewness, kurtosis, normality_test, test_statistic, p_value_or_significance, normality_call\n- Histogram + density plot per variable (PDF)\n- Q-Q plot per variable (PDF)\n- Reproducibility artifacts: `analysis_code.py` or `analysis_code.R`, plus `session_info.txt`\n\n## Limits\n\n- Shapiro-Wilk test is unreliable for very large n; use Anderson-Darling and visual diagnostics instead.\n- Avoid one-sample KS tests against a fitted normal distribution unless a corrected procedure is explicitly implemented.\n- Normality tests have low power for small samples (n \u003C 20); visual inspection of Q-Q plot is recommended.\n- Does not handle categorical or date columns; these are silently skipped.\n- Common failure: non-numeric columns passed explicitly cause an error — pre-filter if needed.\n",{"name":5,"description":7},"main","5118e7fda890e9aa819cb71e003cd243d3a31970","skills\u002Fstat-analyze-distribution",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":37,"prompt":47,"zipAvailable":48},[38,41,44],{"tool":39,"command":40},"npx","npx skills add Scientific-Tooling\u002Fstructured-intelligence --skill stat-analyze-distribution",{"tool":42,"command":43},"bunx","bunx skills add Scientific-Tooling\u002Fstructured-intelligence --skill stat-analyze-distribution",{"tool":45,"command":46},"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",[71],{"categoryNo":72,"categoryName":73,"taxonomyType":74,"taxonomyTypeDesc":75},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92],"structured intelligence skill creator","skill 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