Does it support DBT ecosystem packages?

Absolutely. It includes built-in support for dbt-codegen for boilerplate generation, dbt-utils for macros, and dbt-expectations for advanced data quality testing.

Can I target specific parts of my DBT project?

Yes, you can run specific models, paths, or tags using flexible selection syntax, allowing for targeted incremental updates rather than full builds.

How does the skill ensure project health?

It includes a diagnosis step that validates configuration files, checks model layers, and verifies database connections before attempting execution.

Can this skill handle failed DBT models?

Yes, the skill is designed to identify failed models and provides specific commands to retry them using state-aware selection filters.

DBT Job Runner

Name: DBT Job Runner
Author: nerver111

bynerver111

0•

Data Science & ML

Executes, monitors, and optimizes DBT data transformation pipelines with intelligent error handling and performance reporting.

The DBT Job Runner skill empowers Claude to manage complex data transformation workflows by executing DBT commands, monitoring real-time progress, and automatically diagnosing failures. It supports full builds, incremental runs, and specific model selections while integrating powerful ecosystem packages like dbt-codegen and dbt-expectations for enhanced data quality. This skill is ideal for data engineers looking to automate pipeline execution, generate documentation, and maintain high-quality data models directly within their development environment through intelligent command generation and execution summaries.

Key Features

01Comprehensive execution summaries featuring success/failure counts and performance metrics

02Flexible execution scope including full builds, incremental runs, and specific model selection

030 GitHub stars

04Automated retry logic for failed models using state-aware execution tracking

05Deep integration with DBT ecosystem packages like dbt-codegen, dbt-utils, and dbt-expectations

06Real-time monitoring and performance analysis of DBT jobs with detailed output

Use Cases

01Selectively retrying failed models in a complex DAG to ensure data integrity after pipeline interruptions

02Automating the generation of source YAMLs and staging models using codegen operations

03Running and testing a complete data warehouse refresh while monitoring for performance bottlenecks

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add nerver111/git-skill-test dbt-job-runner

For use in Claude.ai and ChatGPT

Key Features

01Comprehensive execution summaries featuring success/failure counts and performance metrics

02Flexible execution scope including full builds, incremental runs, and specific model selection

030 GitHub stars

04Automated retry logic for failed models using state-aware execution tracking

05Deep integration with DBT ecosystem packages like dbt-codegen, dbt-utils, and dbt-expectations

06Real-time monitoring and performance analysis of DBT jobs with detailed output

Use Cases

01Selectively retrying failed models in a complex DAG to ensure data integrity after pipeline interruptions

02Automating the generation of source YAMLs and staging models using codegen operations

03Running and testing a complete data warehouse refresh while monitoring for performance bottlenecks