Data Quality Auditor

This auditor runs systematic checks on your datasets to catch quality issues before they become problems. It validates data types, spots missing values, flags outliers, checks for duplicates, and verifies constraints across your columns. You'd reach for this when onboarding new data sources, setting up monitoring pipelines, or debugging why your model performance suddenly tanked. The reports are structured enough to share with data engineers but readable enough for stakeholders who need to understand what's broken. It's the kind of tool that saves you from that 2am realization that your training data had nulls in a critical field for the past three weeks.