// ALWAYS read and follow this skill before acting. Data quality conventions
ALWAYS read and follow this skill before acting. Data quality workflow
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-dq-rules |
| description | ALWAYS read and follow this skill before acting. Data quality conventions |
Continuous data quality, not ad-hoc. This toolkit sets up persistent checks and metrics that run on every pipeline load. For one-off data sanity checks during development, use the data-exploration toolkit or the validate-data step in rest-api-pipeline instead.
Use the dlt data quality API, not notebooks. When the user asks to "set up data quality" or "monitor data quality", invoke setup-data-quality. Never generate a custom Marimo notebook as a data quality solution.
License first. The dlthub.data_quality license scope must be present before checks can run. If it is absent, stop and run dlt license issue dlthub.data_quality before continuing.
Prefer built-in checks. Use is_unique(), is_not_null(), is_in(), case() before writing any custom logic. Custom code is a last resort. Do NOT use is_primary_key() — it is not yet fully implemented and raises LineageFailedException at runtime; use is_unique() instead.
Business intent first. Ask for the user's data quality requirements in plain language; map them to checks. Do not expose the API surface (dq.checks.*, dq.metrics.*) until the user's intent is clear.
Query results incrementally. In review-data-quality, scope all queries to one table at a time. Show aggregated summaries first; load row-level detail only on explicit user request.