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Reusable instructions for compatible AI agents.
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Analyze tech integration projects (e.g., middleware, payment platforms) to forecast engineering and operational resources, implement granular cost tracking (cloud tagging, Kubernetes), and define cross-functional workflows between finance and engineering teams using Bay Area professional communic...
Trace upstream data lineage. Use when the user asks where data comes from, what feeds a table, upstream dependencies, data sources, or needs to understand data origins.
Generate airflow dag generator operations. Auto-activating skill for Data Pipelines. Triggers on: airflow dag generator, airflow dag generator Part of the Data Pipelines skill category. Use when working with airflow dag generator functionality. Trigger with phrases like "airflow dag generator", "...
Build scalable data pipelines, modern data warehouses, and
Extracts and maps data lineage from various sources including SQL, dbt, Airflow, and Spark, generating comprehensive lineage graphs for impact analysis.
Complete methodology for designing, building, operating, and
Build a data pipeline — ETL/ELT with extraction, transformation, loading, error handling, and scheduling. Use when asked to "build ETL", "data pipeline", "move data from X to Y", or "sync data".
Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment. Use when creating ML pipelines, implementing MLOps practices, or automating model training and deployment workflows.
Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment. Use when creating ML pipelines, implementing MLOps practices, or automating mod...
Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment. Use when creating ML pipelines, implementing MLOps practices, or automating model training and deployment workflows.
Complete end-to-end MLOps pipeline orchestration from data preparation through model deployment.
Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment. Use when creating ML pipelines, implementing MLOps practices, or automating model training and deployment workflows.
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