Package Validator Skill

Data package validation scans JSON files for structural integrity, required fields, and schema adherence using Pydantic models. Run the validator against your packages directory to catch malformed or inconsistent data before it corrupts your database, ensuring only high-quality data enters your pipeline.

Yes. The validator cross-checks package JSON against available Pydantic models to enforce strict schema compliance and validate data types. When models are unavailable, it gracefully skips model checks while continuing structural validation and reporting inconsistencies.

The validator generates explicit error and warning messages with a tabular summary showing which fields are missing, which values are invalid, and where data diverges from raw artifacts. All reporting is read-only and non-destructive, leaving packages untouched for manual review.

Yes. The validator applies across sports by scanning the artifacts/{sport}/packages/{date} directory structure. Specify the sport and date as command-line arguments to validate packages for any sport in your dataset consistently.

Early validation at the packaging stage prevents downstream corruption, reduces costly database cleanups, and catches consistency issues like negative stats or unrealistic odds before data enters your system, saving time and maintaining data reliability.

Yes. The validator handles missing models gracefully by focusing on JSON structure validation and required field checks. As you define Pydantic models, the validator automatically applies stricter schema validation without requiring any configuration changes.