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dbt-transformation-patterns

@sickn33/antigravity-awesome-skills
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Production-ready patterns for dbt (data build tool) including model organization, testing strategies, documentation, and incremental processing.

Install Skill

Shared

Installs to .agents/skills, used by Codex, Amp, Warp, Cursor, OpenCode, and more.

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Available across projects.

$npx skills-installer add @sickn33/antigravity-awesome-skills/dbt-transformation-patterns --client shared
Project

Writes to .agents/skills.

$npx skills-installer add @sickn33/antigravity-awesome-skills/dbt-transformation-patterns -p --client shared
Note: Review the skill instructions before using it.

SKILL.md

name dbt-transformation-patterns
description Production-ready patterns for dbt (data build tool) including model organization, testing strategies, documentation, and incremental processing.
risk none
source community
date_added 2026-02-27

dbt Transformation Patterns

Production-ready patterns for dbt (data build tool) including model organization, testing strategies, documentation, and incremental processing.

Use this skill when

  • Building data transformation pipelines with dbt
  • Organizing models into staging, intermediate, and marts layers
  • Implementing data quality tests and documentation
  • Creating incremental models for large datasets
  • Setting up dbt project structure and conventions

Do not use this skill when

  • The project is not using dbt or a warehouse-backed workflow
  • You only need ad-hoc SQL queries
  • There is no access to source data or schemas

Instructions

  • Define model layers, naming, and ownership.
  • Implement tests, documentation, and freshness checks.
  • Choose materializations and incremental strategies.
  • Optimize runs with selectors and CI workflows.
  • If detailed patterns are required, open resources/implementation-playbook.md.

Resources

  • resources/implementation-playbook.md for detailed dbt patterns and examples.

Limitations

  • Use this skill only when the task clearly matches the scope described above.
  • Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
  • Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.