// ALWAYS read and follow this skill before acting. Deploy to dltHub Platform
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. 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 | dlthub-platform-workflow |
| description | ALWAYS read and follow this skill before acting. Deploy to dltHub Platform |
ALWAYS start with Setup runtime (setup-runtime) — ensure workspace, dependencies, and runtime login are ready
prepare-deployment) — split dev/prod credentials, set up production destinationdeploy-workspace) — prepare scripts for production, deploy, launch, scheduledebug-deployment) — check job status, view logs, diagnose failuresdebug-pipeline / adjust-endpoint (modify existing) — when the user needs to build or modify a pipeline before deployingbuild-notebook — when the user wants to create marimo notebooks to deploy as interactive jobsdebug-pipeline or hardening steps) — pipeline name, destination, and dataset are already known; carry them into setup-runtime and deploy-workspace without re-discoverycreate-sql-database-pipeline, debug-pipeline, adjust-table, or add-table) — pipeline name, destination, and dataset are already known; carry them into setup-runtime and deploy-workspace without re-discoverycreate-filesystem-pipeline or add-incremental-loading) — pipeline name, destination, and dataset are already known; carry them into setup-runtime without re-discoverycreate-transformation) — transformation scripts and pipeline destination are already known; carry them into setup-runtimebuild-notebook) — notebook file already exists; deploy-workspace should use dlthub serve for the notebook jobrun-data-quality) — pipeline script with embedded @dq.with_checks decorators is the deployment target; carry the pipeline script path, pipeline name, and destination into setup-runtimerun-data-quality) — tools/dq_run.py already exists with confirmed checks; carry the script path, pipeline name, and destination into setup-runtime as the deployment targetReferences: