weekly-working-hours

To automate weekly dbt model build verification, this Skill syncs your main branch, creates an isolated git worktree, resets the BigQuery sandbox, and runs targeted dbt builds to validate the error-detection view returns zero problematic rows.

BigQuery sandbox reset for scheduled dbt data quality monitoring clears the target environment before recreating an isolated worktree, ensuring the weekly dbt_job build and run steps execute against a clean state to accurately enforce row_count equals 0.

Yes, you can use git worktree orchestration to isolate scheduled dbt builds by creating a dedicated worktree for the tenant-specific working_hours_report models branch, preventing conflicts during the dbt build and BigQuery COUNT validation.

The best way to enforce zero-row error detection is to run a dedicated dbt job to materialize the view, then execute a BigQuery COUNT(*) check that immediately stops the process if the row_count is not equal to 0.

If a dbt model build fails during weekly data quality checks, the Skill stops immediately and safely retains the isolated git worktree for debugging, rather than automatically tearing it down.

Yes, a git main fast-forward sync is required before running dbt builds, ensuring the isolated worktree for the scheduled branch is created from the latest repository state before resetting the BigQuery sandbox.