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Refactoring Dbt Models

Organization
AltimateAI
refactoring-dbt-models

Safely refactors dbt models with downstream impact analysis. Use when restructuring dbt models for: (1) Task mentions "refactor", "restructure", "extract", "split", "break into", or "reorganize" (2) Extracting CTEs to intermediate models or creating macros (3) Modifying model logic that has downstream consumers (4) Renaming columns, changing types, or reorganizing model dependencies Analyzes all downstream dependencies BEFORE making changes.

Overview

PublisherAltimateAI
Repositorydata-engineering-skills
Skill namerefactoring-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 Refactoring 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/refactoring-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/refactoring-dbt-models .claude/skills/refactoring-dbt-models
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Refactoring 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 Refactoring 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 Refactoring 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 Refactoring

Find ALL downstream dependencies before changing. Refactor in small steps. Verify output after each change.

Workflow

1. Analyze Current Model

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

Identify refactoring opportunities:

  • CTEs longer than 50 lines → extract to intermediate model
  • Logic repeated across models → extract to macro
  • Multiple joins in sequence → split into steps
  • Complex WHERE clauses → extract to staging filter

2. Find All Downstream Dependencies

CRITICAL: Never refactor without knowing impact.

bash
# Get full dependency tree (model and all its children)
dbt ls --select model_name+ --output list

# Find all models referencing this one
grep -r "ref('model_name')" models/ --include="*.sql"

Report to user: "Found X downstream models: [list]. These will be affected by changes."

3. Check What Columns Downstream Models Use

BEFORE changing any columns, check what downstream models reference:

bash
# For each downstream model, check what columns it uses
cat models/<path>/<downstream_model>.sql | grep -E "model_name\.\w+|alias\.\w+"

If downstream models reference specific columns, you MUST ensure those columns remain available after refactoring.

4. Plan Refactoring Strategy

OpportunityStrategy
Long CTEExtract to intermediate model
Repeated logicCreate macro in macros/
Complex joinSplit into intermediate models
Multiple concernsSeparate into focused models

5. Execute Refactoring

Pattern: Extract CTE to Model

Before:

sql
-- orders.sql (200 lines)
with customer_metrics as (
    -- 50 lines of complex logic
),
order_enriched as (
    select ...
    from orders
    join customer_metrics on ...
)
select * from order_enriched

After:

sql
-- customer_metrics.sql (new file)
select
    customer_id,
    -- complex logic here
from {{ ref('customers') }}

-- orders.sql (simplified)
with order_enriched as (
    select ...
    from {{ ref('raw_orders') }} orders
    join {{ ref('customer_metrics') }} cm on ...
)
select * from order_enriched
Pattern: Extract to Macro

Before (repeated in multiple models):

sql
case
    when amount < 0 then 'refund'
    when amount = 0 then 'zero'
    else 'positive'
end as amount_category

After:

sql
-- macros/categorize_amount.sql
{% macro categorize_amount(column_name) %}
case
    when {{ column_name }} < 0 then 'refund'
    when {{ column_name }} = 0 then 'zero'
    else 'positive'
end
{% endmacro %}

-- In models:
{{ categorize_amount('amount') }} as amount_category

6. Validate Changes

bash
# Compile to check syntax
dbt compile --select +model_name+

# Build entire lineage
dbt build --select +model_name+

# Check row counts (manual)
# Before: Record expected counts
# After: Verify counts match

7. Verify Output Matches Original

CRITICAL: Refactoring should not change output.

bash
# Compare row counts before and after
dbt show --inline "select count(*) from {{ ref('model_name') }}"

# Spot check key values
dbt show --select <model_name> --limit 10

8. Update Downstream Models

If changing output columns:

  1. Update all downstream refs
  2. Update schema.yml documentation
  3. Re-run downstream tests

Refactoring Checklist

  • All downstream dependencies identified
  • User informed of impact scope
  • One change at a time
  • Compile passes after each change
  • Build passes after each change
  • Output validated (row counts match)
  • Documentation updated
  • Tests still pass

Common Refactoring Triggers

SymptomRefactoring
Model > 200 linesExtract CTEs to models
Same logic in 3+ modelsExtract to macro
5+ joins in one modelCreate intermediate models
Hard to understandAdd CTEs with clear names
Slow performanceSplit to allow parallelization

Anti-Patterns

  • Refactoring without checking downstream impact
  • Making multiple changes at once
  • Not validating output matches after refactoring
  • Extracting prematurely (wait for 3+ uses)
  • Breaking existing tests without updating them

Frequently asked questions

What does the Refactoring Dbt Models AI skill do?

Safely refactors dbt models with downstream impact analysis. Use when restructuring dbt models for: (1) Task mentions "refactor", "restructure", "extract", "split", "break into", or "reorganize" (2) Extracting CTEs to intermediate models or creating macros (3) Modifying model logic that has downstream consumers (4) Renaming columns, changing types, or reorganizing model dependencies Analyzes all downstream dependencies BEFORE making changes.

Why use Refactoring Dbt Models on TypingMind?

Because you install it once and use it with any model. Refactoring 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 Refactoring 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/refactoring-dbt-models. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Refactoring 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 Refactoring Dbt Models?

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

Is the Refactoring 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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