name: data-quality-audit description: Systematically audit a dataset before modeling: nulls, leakage, duplicates, dtype lies, target contamination. Use when the user loads a new dataset, mentions cleaning data, or before any ML/analysis task on unfamiliar data.

Data Quality Audit

Run this audit BEFORE any modeling conversation gets serious. Work through the phases; report findings as a table at the end.

Phase 1: Shape and identity

• Rows x columns; do they match the user's expectation?
• Primary key exists? Duplicated keys?
• Date ranges: gaps, duplicates, future dates
• Grain check: is each row really one entity-time unit? Aggregate and count to verify

Phase 2: Dtype lies

Print dtypes and challenge them:

• Numeric columns loaded as object (commas, currency symbols, whitespace)
• IDs and zip codes as integers (leading zeros destroyed)
• Dates as strings with mixed formats in one column
• Booleans as 0/1/2

Fix dtype issues before computing anything else; null-rate statistics lie otherwise.

Phase 3: Missingness forensics

For every column with any nulls:

• Null rate
• Is missingness correlated with the target? (compute it; if yes, the missingness IS signal)
• Structured missing vs random: sort by another column and look for runs

Never silently drop or impute without reporting which rows/columns were affected.

Phase 4: Leakage scan

The highest-value phase:

• Any column that could not be known at prediction time? Flag it
• Aggregates computed over periods overlapping the label window?
• Target-derived features disguised (ratios containing the outcome)?
• Post-outcome statuses (charge-offs, cancellations) present as features?

Phase 5: Distribution shocks

• Train/test drift on numeric features (report top 5 by KS-statistic)
• Categorical cardinality explosions between splits
• Constant or near-constant columns
• Extreme outliers: count beyond 5 sigma per column; ask if they are errors before winsorizing

Report format

AUDIT: <dataset name>
Rows: N | Cols: M | Memory: X
BLOCKERS: [issues that invalidate modeling until fixed]
WARNINGS: [issues that bias results]
OK: [what was checked and passed]