[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6CZ5GGP5GGAQAAGR1RNHYSW":3,"skill-same:01M6CZ5GGP5GGAQAAGR1RNHYSW":72},{"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":23,"publishedAt":32,"linkOnly":33,"markdownContent":34,"markdownBody":35,"frontMatter":36,"refName":37,"commitSha":38,"skillDirectory":39,"forksCount":40,"repositoryHtmlUrl":41,"repositoryHomepageUrl":6,"ownerHtmlUrl":42,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":43,"install":44,"resources":57},"01M6CZ5GGP5GGAQAAGR1RNHYSW","stat-assess-data-quality",null,"Assess data quality by reporting missing values, outliers, sample size, and variance structure for each variable in a table.","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-assess-data-quality\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[24,25,26,27,28,29,30,31],"data","data quality","quality","stat assess data quality","reporting","table","structure","sample","2026-11-10 10:12:30",false,"---\nname: stat-assess-data-quality\ndescription: Assess data quality by reporting missing values, outliers, sample size, and variance structure for each variable in a table.\n---\n\n# Skill: Statistical Data Quality Assessment\n\n## Use When\n\n- User wants a quick data quality report before any statistical analysis\n- User needs to identify columns with high missingness or extreme outliers\n- User wants to understand variance structure and flag zero-variance or near-zero-variance columns\n- User is preparing data for modeling and wants a pre-flight check\n\n## Inputs\n\n- Required:\n  - Data table (CSV or TSV)\n- Optional:\n  - Outlier detection method: `iqr` (default) or `zscore`\n  - IQR multiplier for outlier threshold (default: `1.5`)\n  - Z-score threshold (default: `3.0`)\n  - Output directory (default: `.\u002Fqc_output`)\n\n## Workflow\n\n1. Read the data file; record total rows (n) and total columns.\n2. For each column, report: data type, non-missing count, missing count, missing rate (%).\n3. For numeric columns: compute mean, SD, min, max; detect outliers using IQR fence or Z-score; report outlier count and outlier rate (%).\n4. Flag columns with: missing rate > 20%, outlier rate > 5%, zero variance, near-zero variance (coefficient of variation \u003C 0.01).\n5. Report sample size adequacy: flag if n \u003C 30 (small sample warning).\n6. Produce a missingness heatmap (rows = samples, columns = variables) if any missing values exist.\n7. Write a quality report (TSV) and missingness plot (PDF) to output directory.\n8. Summarize flags: list columns requiring attention and recommended actions (imputation, removal, transformation).\n9. 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- Quality report table (TSV): column, dtype, n_non_missing, n_missing, missing_rate, n_outliers, outlier_rate, mean, sd, min, max, flags\n- Missingness heatmap (PDF, only if missing values present)\n- Summary of flagged columns (printed to stdout)\n- Reproducibility artifacts: `analysis_code.py` or `analysis_code.R`, plus `session_info.txt`\n\n## Limits\n\n- IQR-based outlier detection assumes roughly unimodal distributions; may flag many values in skewed data.\n- Does not impute or remove data — assessment only.\n- Very large datasets (> 1M rows) may be slow; consider sampling first.\n- Common failure: mixed-type columns (e.g., numeric with \"N\u002FA\" strings) require preprocessing.\n","# Skill: Statistical Data Quality Assessment\n\n## Use When\n\n- User wants a quick data quality report before any statistical analysis\n- User needs to identify columns with high missingness or extreme outliers\n- User wants to understand variance structure and flag zero-variance or near-zero-variance columns\n- User is preparing data for modeling and wants a pre-flight check\n\n## Inputs\n\n- Required:\n  - Data table (CSV or TSV)\n- Optional:\n  - Outlier detection method: `iqr` (default) or `zscore`\n  - IQR multiplier for outlier threshold (default: `1.5`)\n  - Z-score threshold (default: `3.0`)\n  - Output directory (default: `.\u002Fqc_output`)\n\n## Workflow\n\n1. Read the data file; record total rows (n) and total columns.\n2. For each column, report: data type, non-missing count, missing count, missing rate (%).\n3. For numeric columns: compute mean, SD, min, max; detect outliers using IQR fence or Z-score; report outlier count and outlier rate (%).\n4. Flag columns with: missing rate > 20%, outlier rate > 5%, zero variance, near-zero variance (coefficient of variation \u003C 0.01).\n5. Report sample size adequacy: flag if n \u003C 30 (small sample warning).\n6. Produce a missingness heatmap (rows = samples, columns = variables) if any missing values exist.\n7. Write a quality report (TSV) and missingness plot (PDF) to output directory.\n8. Summarize flags: list columns requiring attention and recommended actions (imputation, removal, transformation).\n9. 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- Quality report table (TSV): column, dtype, n_non_missing, n_missing, missing_rate, n_outliers, outlier_rate, mean, sd, min, max, flags\n- Missingness heatmap (PDF, only if missing values present)\n- Summary of flagged columns (printed to stdout)\n- Reproducibility artifacts: `analysis_code.py` or `analysis_code.R`, plus `session_info.txt`\n\n## Limits\n\n- IQR-based outlier detection assumes roughly unimodal distributions; may flag many values in skewed data.\n- Does not impute or remove data — assessment only.\n- Very large datasets (> 1M rows) may be slow; consider sampling first.\n- Common failure: mixed-type columns (e.g., numeric with \"N\u002FA\" strings) require preprocessing.\n",{"name":5,"description":7},"main","5118e7fda890e9aa819cb71e003cd243d3a31970","skills\u002Fstat-assess-data-quality",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":45,"prompt":55,"zipAvailable":56},[46,49,52],{"tool":47,"command":48},"npx","npx skills add Scientific-Tooling\u002Fstructured-intelligence --skill stat-assess-data-quality",{"tool":50,"command":51},"bunx","bunx skills add Scientific-Tooling\u002Fstructured-intelligence --skill stat-assess-data-quality",{"tool":53,"command":54},"pnpm","pnpm dlx skills add Scientific-Tooling\u002Fstructured-intelligence --skill 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