Answering Natural Language Questions With Dbt

This turns natural language questions like "what were sales last quarter?" into actual answers by working through a priority waterfall: semantic layer first, then modifying compiled SQL if you need custom logic, then model discovery, then manifest parsing as a last resort. The systematic approach is the point here. It resists the temptation to immediately write ad-hoc SQL and instead exhausts what dbt already knows about your metrics and dimensions. When you're in a dbt project, it'll suggest semantic layer improvements rather than telling you to bug your data team. Built by dbt Labs, so the decision flow reflects how they think the semantic layer should actually be used in practice.

skills.sh installs: 1.1K
GitHub stars: 759
last updated: Aug 21, 2026

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Install

$ npx -y skills add dbt-labs/dbt-agent-skills --skill answering-natural-language-questions-with-dbt --agent claude-code

Installs into .claude/skills of the current project.

Files

Answering Natural Language Questions with dbt

Overview

Answer data questions using the best available method: semantic layer first, then SQL modification, then model discovery, then manifest analysis. Always exhaust options before saying "cannot answer."

Use for: Business questions from users that need data answers