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answering-natural-language-questions-with-dbt

Answer business analytics questions by querying dbt's Semantic Layer or ad-hoc SQL.

11|3|Updated Mar 9, 2026
Search Tags:#analytics#dbt#sql#data-warehouse#natural-language#semantic-layer#business-questions
One-click install
npx skills add https://github.com/kyle-chalmers/dbt-agentic-development --skill answering-natural-language-questions-with-dbt-kyle-chalmers
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: answering-natural-language-questions-with-dbt
Source: https://github.com/kyle-chalmers/dbt-agentic-development/tree/main/.agents/skills/answering-natural-language-questions-with-dbt
Command: npx skills add https://github.com/kyle-chalmers/dbt-agentic-development --skill answering-natural-language-questions-with-dbt-kyle-chalmers

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill enables teams to answer analytics questions by querying the data warehouse through dbt's Semantic Layer or via ad-hoc SQL, reducing manual data wrangling and speeding insights.

Core Features & Use Cases

  • Semantic-layer-first question answering: fetch metrics and dimensions without writing SQL.
  • Fallback to compiled SQL or model discovery when the semantic layer cannot directly answer.
  • Use cases include revenue by region, top customers by revenue, and KPI trend analysis across time.

Quick Start

Ask a business question in natural language and receive an SQL-backed answer from the warehouse.

Frequently Asked Questions about answering-natural-language-questions-with-dbt

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I answer business questions about metrics using dbt's Semantic Layer?▼

To answer business questions using dbt's Semantic Layer, you query the data warehouse to fetch defined metrics and dimensions, retrieving KPIs across regions and products without manually writing SQL.

What is the best way to translate natural language questions into SQL for a data warehouse?▼

Translating natural language questions into SQL involves parsing the query intent and querying the warehouse through dbt's Semantic Layer, falling back to compiled ad-hoc SQL to calculate metrics like revenue by region or top customers.

Do I need a configured dbt project to answer analytics questions with the Semantic Layer?▼

Yes, answering analytics questions with the Semantic Layer requires access to a dbt project with a configured Semantic Layer and the ability to execute SQL directly against your data warehouse.

Can I use ad-hoc SQL when the dbt Semantic Layer cannot answer my question?▼

Yes, you can use ad-hoc SQL as a fallback mechanism for model discovery and question answering when the dbt Semantic Layer cannot directly resolve the requested business metrics or performance dimensions.

What kind of KPI trend analysis can I perform by querying the data warehouse with dbt?▼

Querying the data warehouse with dbt supports KPI trend analysis across time, allowing you to calculate performance metrics, revenue by region, and identify top customers by revenue directly from your analytics models.

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