data-quality-profiler

# Data Quality Profiler Profiles data assets to assess quality dimensions and detect anomalies across the six core data quality dimensions. ## Overview This skill performs comprehensive data profiling to assess completeness, accuracy, consistency, validity, timeliness, and uniqueness. It generates statistical profiles, detects anomalies, identifies PII, and provides actionable recommendations for data quality improvement. ## Capabilities - **Statistical profiling** - Distributions, cardinality, null percentages, min/max values - **Data type inference and validation** - Detect actual vs declared types - **Pattern detection** - Regex patterns, formats, common structures - **Anomaly detection** - Outliers, drift, unexpected values - **Referential integrity checking** - Foreign key validation - **Freshness monitoring** - Data age and update frequency - **Volume trend analysis** - Record count patterns over time - **Schema change detection** - Structural changes between runs - **Cross-column correlation analysis** - Identify dependent columns - **PII detection and classification** - Sensitive data identification ## Input Schema ```json { "dataSource": { "type": "object", "required": true, "properties": { "type": { "type": "string", "enum": ["table", "file", "query"], "description": "Type of data source" }, "connection": { "type": "object", "description": "Connection details (platform, database, schema)"...