dbt Analytics Engineering Skills

A comprehensive collection of skills for analytics engineering with dbt. Covers building models, writing tests, querying the semantic layer, troubleshooting jobs, and more.

Included Skills

using-dbt-for-analytics-engineering

Builds and modifies dbt models, writes SQL transformations using ref() and source(), creates tests, and validates results with dbt show. Use when doing any dbt work - building or modifying models, debugging errors, exploring unfamiliar data sources, writing tests, or evaluating impact of changes.

adding-dbt-unit-test

Creates unit test YAML definitions that mock upstream model inputs and validate expected outputs. Use when adding unit tests for a dbt model or practicing test-driven development (TDD) in dbt.

building-dbt-semantic-layer

Use when creating or modifying dbt Semantic Layer components - semantic models, metrics, dimensions, entities, measures, or time spines. Covers MetricFlow configuration, metric types (simple, derived, cumulative, ratio, conversion), and validation for both latest and legacy YAML specs.

answering-natural-language-questions-with-dbt

Writes and executes SQL queries against the data warehouse using dbt's Semantic Layer or ad-hoc SQL to answer business questions. Use when a user asks about analytics, metrics, KPIs, or data.