data-validator

Automate data quality validation by inferring schemas and assessing completeness, uniqueness, consistency, and referential integrity across CSV, JSON, SQLite, and API datasets. The skill returns a structured report of quality metrics and drift findings suitable for downstream pipeline automation.

Data drift detection identifies changes between versions of datasets by comparing them to surface structural and content shifts. It applies cross-dataset comparison and time-series-aware checks to ensure data reliability and prevent bad data from propagating through pipelines.

Yes, schema inference works for tabular and API data sources. It automatically infers schemas for CSV, JSON, SQLite, and API inputs, validating datasets against the inferred schema to assess accuracy, completeness, uniqueness, consistency, and referential integrity.

Yes, the data validator applies referential integrity checks alongside completeness, uniqueness, consistency, and accuracy validations. It infers schemas across multiple sources and returns a structured report of quality metrics and drift findings to ensure data reliability.

Prevent bad data propagation by running autonomous data quality validation to infer schemas and surface issues across CSV, JSON, SQLite, and API sources. It scores data quality, detects drift, and returns a structured report to feed downstream automation or dashboards.

A known constraint is the dependency on better-sqlite3, which requires a compatible Node.js environment to validate SQLite databases. The skill processes SQLite, CSV, JSON, and API sources to generate structured reports of schema, quality metrics, and drift findings.