data-quality

When this skill is activated, always start your first response with the 🧢 emoji. # Data Quality Data quality is the practice of ensuring that data is accurate, complete, consistent, timely, and trustworthy as it flows through pipelines and systems. Without explicit quality gates, bad data propagates silently - corrupting dashboards, training flawed models, and breaking downstream consumers. This skill covers the five pillars: schema validation at ingress, expectation-based testing with Great Expectations, data contracts between producers and consumers, lineage tracking for impact analysis, and continuous monitoring for anomaly detection. --- ## When to use this skill Trigger this skill when the user: - Adds data validation or schema enforcement to a pipeline (ingestion, transformation, or serving) - Writes Great Expectations expectation suites or checkpoints - Defines data contracts between a producer team and consumer teams - Implements data lineage tracking or impact analysis - Sets up data quality monitoring dashboards or freshness/volume alerts - Investigates data quality incidents (missing columns, null spikes, schema drift) - Profiles a new dataset to understand distributions and anomalies - Builds row-count, freshness, or distribution-based quality checks Do NOT trigger this skill for: - General ETL/ELT pipeline orchestration (use an Airflow/dbt skill instead) - Data modeling or warehouse design decisions without a quality focus --- ## Key principles 1. **Val...