dbt-transformation-patterns
Standardized patterns for building and maintaining dbt data transformations.
Install
mkdir -p .claude/skills/dbt-transformation-patterns-fiscfed9 && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/14507" && unzip -o skill.zip -d .claude/skills/dbt-transformation-patterns-fiscfed9 && rm skill.zipInstalls to .claude/skills/dbt-transformation-patterns-fiscfed9
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Production-ready patterns for dbt (data build tool) including model organization, testing strategies, documentation, and incremental processing.Key capabilities
- →Build data transformation pipelines with dbt
- →Implement data quality tests and documentation
- →Create incremental models for large datasets
- →Set up dbt project structure and conventions
How it works
The skill provides patterns for structuring dbt projects, including defining model layers, implementing tests, and choosing materialization strategies. It guides the user in optimizing dbt runs with selectors and CI workflows.
Inputs & outputs
When to use dbt-transformation-patterns
- →Structuring dbt models
- →Implementing data quality tests
- →Setting up incremental materialization
About this skill
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.mdfor detailed dbt patterns and examples.
When not to use it
- →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
Limitations
- →Requires the use of dbt
- →Requires a warehouse-backed workflow
- →Requires access to source data or schemas
How it compares
This skill offers production-ready patterns for dbt projects, providing a structured approach compared to building dbt models without established conventions.
Compared to similar skills
dbt-transformation-patterns side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| dbt-transformation-patterns (this skill) | 0 | 2mo | No flags | Intermediate |
| sql-queries | 18 | 5mo | No flags | Intermediate |
| senior-data-engineer | 21 | 7mo | Review | Advanced |
| powerbi-modeling | 11 | 6mo | No flags | Intermediate |
Try saying
Example prompts that trigger this skill in your AI assistant.
More by FISCFED9
View all by FISCFED9 →You might also like
sql-queries
anthropics
Write correct, performant SQL across all major data warehouse dialects (Snowflake, BigQuery, Databricks, PostgreSQL, etc.). Use when writing queries, optimizing slow SQL, translating between dialects, or building complex analytical queries with CTEs, window functions, or aggregations.
senior-data-engineer
davila7
World-class data engineering skill for building scalable data pipelines, ETL/ELT systems, and data infrastructure. Expertise in Python, SQL, Spark, Airflow, dbt, Kafka, and modern data stack. Includes data modeling, pipeline orchestration, data quality, and DataOps. Use when designing data architectures, building data pipelines, optimizing data workflows, or implementing data governance.
powerbi-modeling
github
Power BI semantic modeling assistant for building optimized data models. Use when working with Power BI semantic models, creating measures, designing star schemas, configuring relationships, implementing RLS, or optimizing model performance. Triggers on queries about DAX calculations, table relationships, dimension/fact table design, naming conventions, model documentation, cardinality, cross-filter direction, calculation groups, and data model best practices. Always connects to the active model first using power-bi-modeling MCP tools to understand the data structure before providing guidance.
data-quality-frameworks
wshobson
Implement data quality validation with Great Expectations, dbt tests, and data contracts. Use when building data quality pipelines, implementing validation rules, or establishing data contracts.
fuzzy-matching
dadbodgeoff
Multi-stage fuzzy matching pipeline for entity reconciliation. PostgreSQL trigram pre-filter, salient overlap check, and multi-factor similarity scoring.
query-writing
langchain-ai
For writing and executing SQL queries - from simple single-table queries to complex multi-table JOINs and aggregations