Deploy generated DAGs to a target directory for Airflow or Dagster.
npx skills add https://github.com/starlake-ai/starlake --skill dag-deploy
Please help me install this Agent Skill. Skill: dag-deploy Source: https://github.com/starlake-ai/starlake/tree/main/.agent/skills/dag-deploy Command: npx skills add https://github.com/starlake-ai/starlake --skill dag-deploy
Deploys generated DAGs to a target directory to enable orchestration by your scheduler.
Run starlake dag-deploy with a specified outputDir to deploy the generated DAGs.
High-intent search queries and answers about installing and using this skill.
To deploy generated DAGs, you run a deployment command that moves DAG files from a specified input directory to an output path for orchestration systems like Airflow or Dagster. This prepares the DAGs for scheduling.
Yes, you can configure per-project DAG sub-directories within the output path and use a cleanup option to clear the target directory before deployment, ensuring a safe and repeatable DAG deployment process.
The best way to promote debug DAGs to a test environment is to deploy them from an input directory to a specified output directory. This action aligns the DAGs with your orchestration system's expected path for testing.
Yes, the DAG deployment process validates the usage of options including inputDir, outputDir, dagDir, and clean. This validation aligns with instructions and ensures a safe, repeatable deployment to your orchestration target.
You need to specify an output directory because orchestration tools like Airflow or Dagster require DAGs to be placed in a specific target path. Deploying to this directory enables the scheduler to detect and run the DAGs.
HyperDbgGenerate Go unit test skeletons from interface definitions.
rory-dataTrigger Airflow DAG runs, diagnose failures, inspect logs, and apply fixes in iterative cycles.
AI-Foundry-CoreOrchestrate end-to-end MLOps pipelines from data ingestion through deployment.
leonardoteodorooAutomate end-to-end ML pipeline orchestration from data preparation to deployment.
