Systematic exploratory data analysis. Activate when a dataset needs profiling — structure check, nulls, outliers, distributions, correlations — before deeper analysis begins.
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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 stepsNo reviews yet. Tell others what you thought.
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Rigorous A/B test statistical analysis. Use when analyzing experiment results, calculating statistical significance, checking for sample ratio mismatch, or validating test design before launch.
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Track and document analytical assumptions and decisions. Use when making analytical choices, documenting trade-offs, ensuring transparency, or creating audit trails for analytical work.
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Structured, reproducible analysis documentation. Use when documenting analysis findings, creating analysis notebooks, ensuring reproducibility, or building analysis archives for future reference.
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Structure analysis approach before starting work. Use when receiving new analysis requests, breaking down complex questions into steps, or planning iterative analysis workflows.
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Pre-delivery quality assurance for analysis work. Use when reviewing analysis before sharing with stakeholders, checking for completeness, validating assumptions, or ensuring clarity of recommendations.
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Post-analysis learning and process improvement. Use when completing major analysis projects, documenting lessons learned, or improving team analytical practices.
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