Use when building, debugging, scheduling, or testing data pipelines with Apache Airflow v3. Trigger on any code importing airflow, DAG definitions, operator usage, or user mentioning DAGs/tasks/operators/sensors/scheduling.
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Install skill "airflow" with this command: npx skills add diegoscarabelli/skillsmp-diegoscarabelli-diegoscarabelli-airflow
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Related by shared tags or category signals.
Run when the Conductor reports that project work is complete. Confirms with the user, tells the Conductor to close the project, waits for the close-project report, then terminates the Conductor and Reviewer and marks the project done.
Conventions for building Python ETL/ELT pipelines: file state machine (ingest/process/store/quarantine), standard task sequence with customization hooks, config-first parametrization, per-pipeline directory layout, SQLAlchemy ORM integration (make_base, fkey, upsert). Framework-agnostic; applies to Airflow, Prefect, and others.
Use when building, debugging, deploying, or testing data pipelines with Prefect v3. Trigger on any code importing prefect, prefect YAML config, or user mentioning Prefect flows/tasks/deployments/workers.
Run on first launch (when ~/.system2/knowledge/infrastructure.md is still the template) or whenever the user explicitly asks to re-onboard. Greets the user, learns about them, detects the system, installs and configures the data stack (database, orchestrator, Python environments), scaffolds the pipeline repository, and captures interaction preferences.