revos-data-engineering

Create a RevOS Connection YAML that syncs data from a Source into the org's data warehouse. Use whenever the user asks to: add a connection, set up a sync, ingest a table, pipe data from a source, configure streams, pick which tables to ingest, or wire up a Source. The skill picks a sensible stream subset (especially important for databases — most projects don't want every table) and confirms the choice before writing YAML.

Create first-class Cube.dev cube definitions from existing RevOS dbt gold models. Use when asked to: build a semantic layer, create cubes, generate Cube definitions from dbt, define cube files, or create a semantic model from gold models.

Create new dbt transformations (silver/gold models) in the RevOS dbt project. Use when asked to create a dbt model, build a transformation, add a new layer model, declare a raw source, or register a new raw table. Bronze is source-declarations only — no SQL files. Covers dbt project conventions, sources, materialization, schema.yml, and validation commands.

Inspect the RevOS BigQuery lakehouse: list datasets and tables, introspect table schemas and column types, preview sample rows, assess data layers (bronze/silver/gold), and check data completeness and null rates. Required companion skill for create-dbt-transformations and create-cubes — load before generating dbt models or cube definitions to introspect warehouse columns and types. Use when asked to: explore the lakehouse, list BigQuery tables, inspect a table schema, preview data, check raw source tables, assess data quality, check null rates, understand available data, or perform BigQuery schema introspection.

Populate a BigQuery dataset with sample data from public datasets using bq cp. Use when asked to: load sample data, populate the lakehouse, add demo data, seed the dataset, get started with sample tables, or when the user needs data to explore.