// ALWAYS read and follow this skill before acting. Data quality workflow
ALWAYS read and follow this skill before acting. Data quality conventions
Deploy dlt pipelines to dltHub Platform. Use when the user says "deploy to dltHub", "launch on dltHub", "run on dltHub", "schedule pipeline", or wants to deploy a pipeline or notebook to dltHub.
ALWAYS read and follow this skill before acting. Profiles
ALWAYS read and follow this skill before acting. Deploy to dltHub Platform
ALWAYS read and follow this skill before acting. Filesystem pipeline workflow
Prepare production credentials and destinations for dltHub Platform. Use when setting up prod profile secrets, splitting dev/prod credentials, or configuring a production destination like Motherduck.
| name | data-quality-workflow |
| description | ALWAYS read and follow this skill before acting. Data quality workflow |
ALWAYS start with Setup data quality (setup-data-quality) SKILL — connect to a dlt pipeline and prepare the data quality environment
setup-data-quality) — connect to pipeline, inspect schema, prepare data quality environmentdefine-data-quality-checks) — define checks per table/columnrun-data-quality) — execute checks against the pipeline datareview-data-quality) — review results, surface failures, suggest fixesdefine-data-quality-checks) after seeing initial results; adjust thresholds, add new checks, or remove over-strict onesreview-data-quality), when data quality failures reveal upstream modeling issues that need fixing; start at annotate-sourcesreview-data-quality), when the user wants to deploy the pipeline script (with embedded @dq.with_checks decorators) as a scheduled job; start at setup-runtimereview-data-quality), when the user wants to schedule tools/dq_run.py as a standalone recurring job; start at setup-runtimereview-data-quality), when metric anomalies need deeper interactive investigation; start at explore-datavalidate-data) — pipeline name and dataset already known; skip discovery in (setup-data-quality)validate-data) — pipeline name and dataset already known; skip discovery in (setup-data-quality)create-filesystem-pipeline) — pipeline name and dataset already known; skip discovery in (setup-data-quality)create-transformation) — transformed tables already known; go straight to (define-data-quality-checks)