When to use

• You receive a new dataset and need to understand its shape and quality before analysis
• An analysis produces surprising numbers and you want to verify the underlying data first
• A stakeholder asks "is this data reliable?" or "what's in this table?"
• You're about to run a model or statistical test and need data-quality assurance

Process

1. Load and overview, run scripts/data_overview.py to get row count, dtypes, memory usage, and a sample. Confirm grain (what one row represents).
2. Null profile, run scripts/null_profiler.py; compare output against thresholds in references/quality_thresholds.md and flag columns above limits.
3. Outlier detection, run scripts/outlier_detector.py (IQR + z-score) on numeric columns; document flagged values and decide: real signal or data error?
4. Distribution summary, run scripts/distribution_summary.py for descriptive stats and univariate histograms on each numeric column.
5. Correlation exploration, run scripts/correlation_explorer.py; flag pairs with |r| > 0.8 as potential multicollinearity or redundancy.
6. EDA checklist sign-off, work through references/eda_checklist.md and confirm each item before declaring the dataset profiled.
7. Write findings, fill 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.

Inputs the skill needs

• Required: dataset path (CSV / Parquet / Excel) or a DataFrame already in scope
• Required: business context, what does one row represent?
• Optional: quality threshold overrides (defaults in references/quality_thresholds.md)
• Optional: columns to skip (PII, binary blobs, high-cardinality IDs)