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Dbt Transformation Patterns

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sickn33·vlatest·data·MIT

Identifies potential dbt transformation code improvements and common pitfalls based on established best practices.

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  1. 1

    Run this in your project

    terminal
    $ npx skills add sickn33/agentic-awesome-skills --skill dbt-transformation-patterns -a claude-code

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sickn33

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c808e6e480
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skills/dbt-transformation-patterns/SKILL.md
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About this skill

The dbt-transformation-patterns skill provides pre-defined data transformation logic for dbt (data build tool) projects, enabling AI agents to standardize complex ETL workflows. It allows agents to apply consistent patterns across datasets without writing repetitive SQL code from scratch.

When to use it

  • Standardizing column naming conventions and data types across multiple related tables in a warehouse.
  • Implementing common business logic like currency conversion or date normalization within transformation models.
  • Accelerating the setup of new dbt projects by leveraging established community patterns for joins and aggregations.
  • Ensuring consistency when multiple agents collaborate on different parts of the same data pipeline.

Key capabilities

  • Access to a library of reusable SQL transformation templates designed for dbt.
  • Support for defining custom macros that encapsulate complex transformation logic.
  • Integration with existing dbt project structures to maintain compatibility with Jinja templating.
  • Facilitates the creation of modular, maintainable data models by separating logic from queries.

Example prompts

  • "Generate a dbt model using the 'currency_normalization' pattern to convert transaction amounts into USD."
  • "Apply the standard 'date_partitioning' transformation pattern to my sales fact table for weekly reporting."
  • "Create a new schema that uses the provided join patterns to link customer and order tables efficiently."

Tips & gotchas

Ensure your dbt project is properly configured with the necessary dependencies before attempting to install or use these patterns. While the skill offers standard templates, always validate generated SQL against your specific data schema to avoid runtime errors due to missing columns or unexpected data types.