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miptah21
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documenting-dbt-models

Document dbt models and columns in schema.yml with business context.

2|Updated Apr 8, 2026
Search Tags:#dbt#business rules#schema.yml#data documentation#model grain#column descriptions#dbt docs
One-click install
npx skills add https://github.com/miptah21/skills --skill documenting-dbt-models
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: documenting-dbt-models
Source: https://github.com/miptah21/skills/tree/main/.agents/skills/documenting-dbt-models
Command: npx skills add https://github.com/miptah21/skills --skill documenting-dbt-models

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill helps you document dbt models and columns in schema.yml so your team understands the dataset’s grain, business rules, and caveats—not just the technical structure.

Core Features & Use Cases

  • Matches existing project documentation patterns by first inspecting existing schema.yml files and copying their style and depth.
  • Turns model SQL into clear documentation by reading the relevant model SQL to identify grain and logic worth documenting.
  • Improves discoverability for dbt docs by producing consistent model and column descriptions that support dbt docs generation and easier navigation.
  • Use case: When you add a new model or update a business-critical metric, document the model and its key columns in schema.yml so analytics consumers trust how the data is defined and calculated.

Quick Start

Use the documenting-dbt-models skill to update models/marts/schema.yml by adding a model description and business-focused column definitions for a dbt model you are working on.

Frequently Asked Questions about documenting-dbt-models

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

FAQPage Schema
How do I document dbt models in schema.yml with business context?▼

To document dbt models in schema.yml, add or update model and column descriptions that explain the dataset's grain, business rules, and caveats. This ensures analytics consumers understand how data is defined and calculated beyond the technical structure.

What should dbt column descriptions include to clarify meaning?▼

dbt column descriptions should include business context, grain, and specific caveats. By reading the model SQL to identify logic worth documenting, you can write descriptions that clarify the meaning and calculation rules of business-critical metrics.

How do I match new schema.yml documentation to existing project patterns?▼

Match existing schema.yml documentation patterns by inspecting existing files in the project and copying their style and depth. This ensures consistent model and column descriptions across your dbt project.

Can I automatically generate dbt docs after updating schema.yml descriptions?▼

Yes, after updating schema.yml descriptions, you can run dbt docs generate and serve. Producing consistent, business-focused model descriptions supports dbt docs generation and easier navigation for analytics consumers.

When do I need to document the model grain in dbt?▼

You need to document the model grain in dbt when adding a new model or updating a business-critical metric. Documenting the grain and logic in schema.yml helps your team understand the dataset's meaning and trust the calculated data.

Does writing dbt schema.yml docs require manual alignment with model SQL?▼

Writing dbt schema.yml docs requires alignment with model SQL to accurately identify grain and logic. The documentation process reads the relevant model SQL to ensure the written descriptions match the actual transformation logic and business rules.

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