What problem does it solve? Moving dlt pipelines from local development to a managed cloud runtime involves error-prone steps: removing dev-only settings, pinning dependencies, configuring triggers, and debugging remote failures. This Skill guides the full deployment workflow to dltHub Platform so pipelines and notebooks run reliably in production. ## Core Features & Use Cases - Production readiness checks: Removes dev_mode=True, externalizes dev limits, verifies write_disposition, and pins the dlt version in pyproject.toml before deployment. - Local simulation and cloud runs: Simulates jobs locally with dlthub local run (including --profile prod credential checks), then runs batch jobs with dlthub run and interactive jobs with dlthub serve. - Scheduling and triggers: Adds cron schedules, interval-based triggers, followup chains, freshness gates, and refresh cascades via the __deployment__.py manifest. - Use Case: You built a dlt pipeline locally against DuckDB and want it running daily on BigQuery in the cloud. The Skill walks you through production checks, a dry-run deploy, a local prod-profile simulation, cloud launch, log inspection, and cron scheduling. ## Quick Start Deploy my dlt pipeline to dltHub and schedule it to run daily at midnight.
