dbt-analytics-qa

Answer business data questions by querying dbt models and semantic metrics.

8|Updated Feb 23, 2026

Search Tags:#metrics #data modeling #dbt #sql #business analytics #semantic layer

Ready to run

npx skills add https://github.com/rittmananalytics/wire-plugin --skill dbt-analytics-qa

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: dbt-analytics-qa
Source: https://github.com/rittmananalytics/wire-plugin/tree/main/skills/dbt-analytics-qa
Command: npx skills add https://github.com/rittmananalytics/wire-plugin --skill dbt-analytics-qa

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill enables users to quickly obtain answers to business analytics questions by leveraging dbt's Semantic Layer, Discovery API, or direct SQL queries, reducing manual data investigation time.

Core Features & Use Cases

Business Data Querying: Responds to questions about sales, KPIs, and customer metrics within dbt projects.
Multi-level Data Access: Utilizes Semantic Layer metrics, compiled SQL, model discovery, or manifest files to find the needed data.
Use Case: A data analyst asks, "What was the total revenue last quarter?" The Skill retrieves this metric without needing to manually write SQL or query raw tables.

Quick Start

Ask the AI: "Show me the total sales for the last month using the dbt project."

Frequently Asked Questions about dbt-analytics-qa

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

FAQPage Schema

How do I query business metrics from dbt models without writing SQL manually?▼

You can query business metrics from dbt models without writing SQL manually by leveraging the dbt Semantic Layer and Discovery API. This approach dynamically generates SQL queries from your manifest files to retrieve requested sales figures and KPIs.

How does the dbt Semantic Layer work for answering analytics questions?▼

The dbt Semantic Layer works for analytics questions by applying defined metric definitions and project schemas to dynamically generate SQL queries. This mechanism translates natural language business questions into executable SQL across your various connected databases.

Can I use this approach to retrieve customer metrics across different databases?▼

Yes, you can retrieve customer metrics across different databases using dbt model discovery and manifest files. This multi-level data access approach dynamically generates SQL queries to fetch your required business analytics data.

Do I need a dbt manifest file to generate SQL queries for KPIs?▼

Yes, you need integration with dbt manifest files, the Discovery API, or model discovery to generate SQL queries for KPIs. These project schemas provide the structural metadata required to dynamically build and execute the correct queries.

What is the best way to find total revenue figures in a dbt project?▼

The best way to find total revenue figures in a dbt project is by querying the Semantic Layer metrics. This method uses compiled SQL and model discovery to retrieve the exact sales figures without manual raw table investigation.

What are the limitations of using dbt semantic metrics for data investigation?▼

A limitation of using dbt semantic metrics for data investigation is the strict dependency on pre-defined project schemas and external Semantic Layer integration. Complex, ad-hoc queries outside these defined metrics may still require manually writing direct SQL.