From dlthub-runtime
Debugs failed or misbehaving dltHub Runtime deployments by checking job status and logs, canceling runs, accessing production data, opening dashboard, and diagnosing common issues like missing dependencies or secrets.
npx claudepluginhub dlt-hub/dlthub-ai-workbench --plugin dlthub-runtimeThis skill uses the workspace's default tool permissions.
**Reference**: https://dlthub.com/docs/hub/runtime/overview.md
Deploys dlt pipelines to dltHub Runtime. Prepares Python scripts by removing dev_mode/limits, verifies dispositions, pins versions; runs dlt CLI deploy/launch/logs. For runtime deployment requests.
Debugs failed Render deployments by analyzing logs, metrics, and database state. Identifies errors like missing env vars, port binding, OOM and suggests fixes for service crashes, health check failures, or performance issues.
Debugs and inspects dlt pipelines after runs, checking traces, load packages, schemas, data, and errors like missing credentials or failed jobs. Use post-execution.
Share bugs, ideas, or general feedback.
Reference: https://dlthub.com/docs/hub/runtime/overview.md
dlt runtime job list # all jobs
dlt runtime job info <script_or_name> # details for one job
dlt runtime job-run list <script_or_name> # list all runs
dlt runtime job-run info <script_or_name> [run#] # specific run details
dlt runtime logs <script_or_name> # latest run
dlt runtime logs <script_or_name> <run#> # specific run
Note: logs always follow. You will get stuck if job is still running. This is a temporary limitation.
dlt runtime cancel <script_or_name> [run#]
configured (list profiles). if not: prod profile (if configured)to run a single command on given profile use:
WORKSPACE__PROFILE=prod dlt pipeline my_pipeline info
Note: you must pin profile for mcp server to see the change
dlt runtime dashboard
Opens the dltHub Runtime UI at dlthub.app — shows jobs, runs, logs, schedules, and deployment history.
If a job failed:
dlt runtime job-run info <script> <run#> — check exit status and timingdlt runtime logs <script> <run#> — read the error outputpyproject.toml — all packages must be declared, not just locally installedprod profile — runtime uses prod profile, check .dlt/prod.secrets.tomlif __name__ == "__main__": — the job does nothing without itdev_mode=True left in — drops and recreates dataset on every rundlt runtime deploy and relaunch with dlt runtime launch <script>