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answering-natural-language-questions-with-dbt

Orchestrates semantic layer queries, compiled SQL, and dbt model discovery to answer data questions.

1|Updated Mar 25, 2025
Search Tags:#dbt#data-analytics#model-discovery#semantic-layer#answer#manifest-analysis#business-questions
One-click install
npx skills add https://github.com/ramtinJ95/dotfiles --skill answering-natural-language-questions-with-dbt
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/ramtinJ95/dotfiles/tree/main/arch-config/arch-dotfiles/agents/.agents/skills/answering-natural-language-questions-with-dbt
Command: npx skills add https://github.com/ramtinJ95/dotfiles --skill answering-natural-language-questions-with-dbt

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps data-driven teams answer business questions by orchestrating semantic layer queries, compiled SQL, model discovery, and manifest analysis to obtain reliable answers from dbt projects.

Core Features & Use Cases

  • Semantic Layer Query: When a semantic layer is available, query metrics directly and verify dimensions.
  • Modified Compiled SQL: If the semantic layer exists but needs minor adjustments, modify the compiled SQL and execute.
  • Model Discovery: When no semantic layer but marts/models are accessible, discover models and write SQL against them.
  • Manifest/Catalog Analysis: In a full dbt project, analyze manifest/catalog to derive SQL where needed.

Quick Start

Analyze a business question by applying a semantic layer first, then fallback methods as needed.

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 using dbt models and a semantic layer?▼

Answer business questions by orchestrating dbt semantic layer queries first, then falling back to compiled SQL modification, model discovery, and manifest analysis to derive reliable results.

What is the best way to query metrics when my dbt semantic layer needs SQL adjustments?▼

Modify the compiled SQL directly to adjust your dbt semantic layer queries, executing the updated statements to retrieve precise metric results when standard semantic layer querying falls short.

How do I find and query data models in a dbt project without a semantic layer?▼

Discover accessible dbt marts and models directly, then write and execute custom SQL queries against those discovered structures to extract the required business answers.

Can I use dbt manifest and catalog files to generate SQL for business analytics?▼

Analyze dbt manifest and catalog artifacts to understand model structures and relationships, deriving the necessary SQL statements to produce analytical answers from the project.

Do I need a configured semantic layer to get answers from my dbt project?▼

A configured semantic layer is not required; the workflow prioritizes semantic layer querying but falls back to dbt model discovery and manifest analysis to answer questions without it.

Why use dbt manifest analysis instead of writing SQL directly against data models?▼

Manifest analysis provides structural context of dbt models and relationships, ensuring derived SQL accurately targets available data and maintains project integrity for business answers.

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