Troubleshooting Dbt Job Errors
When a dbt Cloud job fails and the error message isn't clear, this walks you through systematic diagnosis using the Admin API, run logs, and git history. The workflow is opinionated about one thing: never modify a test to make it pass without understanding why it failed. It covers three failure types (infrastructure, code, data) and includes specifics like how to construct artifact URLs when you don't have MCP access and when to use the discovering-data skill for test failures. The "Rationalizations That Mean STOP" table is pointed and useful, calling out the "board meeting in 2 hours" excuse and the "probably just flaky" handwave.
- skills.sh installs
- 1K
- GitHub stars
- 759
- last updated
- Aug 21, 2026
Install
$ npx -y skills add dbt-labs/dbt-agent-skills --skill troubleshooting-dbt-job-errors --agent claude-codeInstalls into .claude/skills of the current project.
Files
Troubleshooting dbt Job Errors
Systematically diagnose and resolve dbt Cloud job failures using available MCP tools, CLI commands, and data investigation.
When to Use
- dbt Cloud / dbt platform job failed and you need to find the root cause
- Intermittent job failures that are hard to reproduce
- Error messages that don't clearly indicate the problem
- Post-merge failures where a recent change may have caused the issue
Not for: Local dbt development errors - use the skill using-dbt-for-analytics-engineering instead
The Iron Rule
Never modify a test to make it pass without understanding why it's failing.
A failing test is evidence of a problem. Changing the test to pass hides the problem. Investigate the root cause first.
Rationalizations That Mean STOP
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- Repository
- github.com/dbt-labs/dbt-agent-skills
- Categories
- Backend & APIs, Git & Pull Requests, AI & Agent Building, Debugging, Cloud & Infrastructure
- First seen
- Jun 3, 2026
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