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Systematic exploratory data analysis. Activate when a dataset needs profiling — structure check, nulls, outliers, distributions, correlations — before deeper analysis begins.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
Track and document analytical assumptions and decisions. Use when making analytical choices, documenting trade-offs, ensuring transparency, or creating audit trails for analytical work.
Structured, reproducible analysis documentation. Use when documenting analysis findings, creating analysis notebooks, ensuring reproducibility, or building analysis archives for future reference.
Create standardized metadata for data assets. Use when documenting new datasets, building data catalogs, improving data discoverability, or creating data dictionaries for teams.
Trace and resolve discrepancies when the same metric shows different values in two or more sources. Use before reporting, after pipeline changes, or when stakeholders question a number.
Document column-level mappings between source and target schemas. Use when integrating data from multiple systems, designing ETL transformations, or documenting how raw fields become analytical assets.
Translate SQL queries into plain language business logic. Use when documenting queries, explaining analysis to non-technical stakeholders, code reviewing for correctness, or building a query catalog.
| name | programmatic-eda |
| description | Systematic exploratory data analysis. Activate when a dataset needs profiling — structure check, nulls, outliers, distributions, correlations — before deeper analysis begins. |
scripts/data_overview.py to get row count, dtypes, memory usage, and a sample. Confirm grain (what one row represents).scripts/null_profiler.py; compare output against thresholds in references/quality_thresholds.md and flag columns above limits.scripts/outlier_detector.py (IQR + z-score) on numeric columns; document flagged values and decide: real signal or data error?scripts/distribution_summary.py for descriptive stats and univariate histograms on each numeric column.scripts/correlation_explorer.py; flag pairs with |r| > 0.8 as potential multicollinearity or redundancy.references/eda_checklist.md and confirm each item before declaring the dataset profiled.assets/eda_report_template.md with full profiling output; distil top issues into assets/findings_summary.md.For pattern recipes (e.g. polars vs pandas equivalents, chunked reads for large files), see references/pandas_polars_recipes.md.
references/quality_thresholds.md)assets/eda_report_template.md (filled) — full profiling report with per-column statsassets/findings_summary.md (filled) — top 3–5 quality issues and recommended next steps