Migrating SQL To Dbt Agent Skill

| (1) Converting stored procedures, views, or raw SQL files to dbt models (2) Task mentions "migrate", "convert", "legacy SQL", "transform to dbt", or "modernize" (3) Breaking monolithic queries into modular layers (discovers project conventions first) (4) Porting existing data pipelines or ETL to dbt patterns Checks for existing models/sources, builds and validates layer by layer.

764 tokens

context cost

the whole folder, loaded on every use

files

instructions only

copies elsewhere

how many repositories repackaged it

115

stars on the repo

on the repository, not the skill itself

Install

one command, takes just this skill from the repository

npx skills add https://github.com/AltimateAI/data-engineering-skills --skill migrating-sql-to-dbt

The instruction itself

12 sections, as written by the author

dbt Migration

Don't convert everything at once. Build and validate layer by layer.

Workflow

1. Analyze Legacy SQL

cat <legacy_sql_file>

Identify all tables referenced in the query.

2. Check What Already Exists

# Search for existing models/sources that reference the table
grep -r "<table_name>" models/ --include="*.sql" --include="*.yml"
find models/ -name "*.sql" | xargs grep -l "<table_name>"

For each table referenced in the legacy SQL:

Check if an existing model already references this table
Check if a source definition exists
If neither exists, ask user: "Table X not found - should I create it as a source?"

Only proceed to intermediate/mart layers after all dependencies exist.

3. Create Missing Sources

# models/staging/sources.yml
version: 2

sources:
  - name: raw_database
    schema: raw_schema
    tables:
      - name: orders
        description: Raw orders from source system
      - name: customers
        description: Raw customer records

4. Build Staging Layer

One staging model per source table. Follow existing project naming conventions.

Build before proceeding:

dbt build --select <staging_model>

5. Build Intermediate Layer (if needed)

Extract complex joins/logic into intermediate models.

Build incrementally:

dbt build --select <intermediate_model>

6. Build Mart Layer

Final business-facing model with aggregations.

7. Validate Migration

# Build entire lineage
dbt build --select +<final_model>
dbt show --select <final_model>

Migration Checklist

[ ] All source tables identified and documented
[ ] Sources.yml created with descriptions
[ ] Staging models: 1:1 with sources, renamed columns
[ ] Intermediate models: business logic extracted
[ ] Mart models: final aggregations
[ ] Each layer compiles successfully
[ ] Each layer builds successfully
[ ] Row counts match original (manual validation)
[ ] Tests added for key constraints

Common Migration Patterns

Nested subqueries → Separate models (staging → intermediate → mart)
Temp tables → Ephemeral materialization {{ config(materialized='ephemeral') }}
Hardcoded values → Variables {{ var("name") }}

Anti-Patterns

Converting entire legacy query to single dbt model
Skipping the staging layer
Not validating each layer before proceeding
Keeping hardcoded values instead of using variables
Not documenting business logic during migration

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How to use it

Copy the folder

Take altimateai/migrating-sql-to-dbt from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.