model-bank-metadata

You can automate model metadata management by running a skill that populates knowledge cutoff, family, and generation fields across the model-bank. It sources authoritative data and applies naming conventions to ensure accuracy.

The best way to backfill metadata for a new model is to use an automated skill that determines the correct knowledge cutoff, family, and generation based on the model name and available information, ensuring data integrity.

A repo-wide sweep updates model knowledge cutoff and generation data by scanning the entire model-bank repository, sourcing authoritative information, and applying standardized naming conventions to all model entries.

Yes, model family naming conventions are applied automatically. The skill sources metadata from authoritative references and enforces consistent naming conventions across the entire model-bank during sweeps or backfilling.

Model knowledge cutoff dates are determined by sourcing information from authoritative sources. The skill verifies available model data to populate accurate cutoff dates and maintain repository-wide data integrity.