Documenting Dbt Models logo

Documenting Dbt Models

Organization
AltimateAI
documenting-dbt-models

Documents dbt models and columns in schema.yml. Use when working with dbt documentation for: (1) Adding model descriptions or column definitions to schema.yml (2) Task mentions "document", "describe", "description", "dbt docs", or "schema.yml" (3) Explaining business context, grain, meaning of data, or business rules (4) Preparing dbt docs generate or improving model discoverability Matches existing project documentation style and conventions before writing.

Overview

PublisherAltimateAI
Repositorydata-engineering-skills
Skill namedocumenting-dbt-models
Stars
133
Forks
12
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by AltimateAI on GitHub. Read the source before you install it.

Installation

Install the Documenting Dbt Models AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/AltimateAI/data-engineering-skills/tree/main/skills/dbt/documenting-dbt-models
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/AltimateAI/data-engineering-skills.git /tmp/data-engineering-skills
mkdir -p .claude/skills
cp -r /tmp/data-engineering-skills/skills/dbt/documenting-dbt-models .claude/skills/documenting-dbt-models
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Documenting Dbt Models in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Documenting Dbt Models on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Documenting Dbt Models is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

dbt Documentation

Document the WHY, not just the WHAT. Include grain, business rules, and caveats.

Workflow

1. Study Existing Documentation Patterns

CRITICAL: Match the project's documentation style before adding new docs.

bash
# Find all schema.yml files with documentation
find . -name "schema.yml" | head -5

# Read well-documented models to learn patterns
cat models/marts/schema.yml | head -150
cat models/staging/schema.yml | head -150

Extract from existing documentation:

  • Description length (brief vs detailed)
  • Formatting style (plain text vs markdown with headers)
  • Information included (grain? business rules? caveats?)
  • Column description depth (all columns vs key columns)
  • Use of meta tags or custom properties

2. Read Model SQL

bash
cat models/<path>/<model_name>.sql

Understand: transformations, business logic, joins, filters.

3. Check Existing Documentation for This Model

bash
# Find existing schema.yml
find . -name "schema.yml" -exec grep -l "<model_name>" {} \;

# Read existing docs
cat models/<path>/schema.yml | grep -A 100 "<model_name>"

4. Identify Documentation Needs

For each model, document:

  • Model description: Purpose, grain, key business rules
  • Column descriptions: Business meaning, not just data type

For each column, consider:

  • What business concept does this represent?
  • Are there any caveats or special values?
  • What is the source of this data?

5. Write Documentation

Match the style discovered in step 1. Example format (adapt to project):

yaml
version: 2

models:
  - name: orders
    description: |
      Order transactions at the order line item grain.
      Each row represents one product in one order.

      **Business Rules:**
      - Revenue recognized on ship_date, not order_date
      - Cancelled orders excluded (status != 'cancelled')
      - Returns processed as negative line items

      **Grain:** One row per order_id + product_id combination

    columns:
      - name: order_id
        description: |
          Unique identifier for the order.
          Source: orders.id from Stripe webhook

      - name: customer_id
        description: |
          Foreign key to customers table.
          NULL for guest checkouts (pre-2023 only)

      - name: revenue
        description: |
          Net revenue for this line item in USD.
          Calculation: unit_price * quantity - discount_amount
          Excludes tax and shipping

      - name: order_status
        description: |
          Current status of the order.
          Values: pending, processing, shipped, delivered, cancelled, returned

6. Generate Docs

bash
dbt docs generate
dbt docs serve  # Optional: preview locally

Documentation Patterns

Note: These are default templates. Always adapt to match project's existing style.

Model Description Template

yaml
description: |
  [One sentence: what this model contains]

  **Grain:** [What does one row represent?]

  **Business Rules:**
  - [Key rule 1]
  - [Key rule 2]

  **Caveats:**
  - [Important limitation or edge case]

Column Description Patterns

Column TypeDocumentation Focus
Primary keySource system, uniqueness guarantee
Foreign keyWhat it joins to, NULL handling
MetricCalculation formula, units, exclusions
DateTimezone, what event it represents
Status/CategoryAll possible values, business meaning
Boolean/FlagWhat true/false means in business terms

Documenting Calculated Fields

yaml
- name: gross_margin
  description: |
    Gross margin percentage.
    Calculation: (revenue - cogs) / revenue * 100
    NULL when revenue = 0 to avoid division by zero

Anti-Patterns

  • Adding documentation without checking existing project patterns
  • Using different formatting style than existing documentation
  • Describing WHAT (e.g., "The order ID") instead of WHY/context
  • Missing grain documentation
  • Not documenting NULL handling
  • Leaving columns undocumented
  • Copy-pasting column names as descriptions

Frequently asked questions

What does the Documenting Dbt Models AI skill do?

Documents dbt models and columns in schema.yml. Use when working with dbt documentation for: (1) Adding model descriptions or column definitions to schema.yml (2) Task mentions "document", "describe", "description", "dbt docs", or "schema.yml" (3) Explaining business context, grain, meaning of data, or business rules (4) Preparing dbt docs generate or improving model discoverability Matches existing project documentation style and conventions before writing.

Why use Documenting Dbt Models on TypingMind?

Because you install it once and use it with any model. Documenting Dbt Models is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Documenting Dbt Models in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/AltimateAI/data-engineering-skills/tree/main/skills/dbt/documenting-dbt-models. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Documenting Dbt Models?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Documenting Dbt Models?

As many as you like. As long as a model supports skills, you can use Documenting Dbt Models with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Documenting Dbt Models AI skill free?

Yes. It is published on GitHub by AltimateAI under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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