A curated collection of Agent Skills for working with dbt, to help AI agents understand and execute dbt workflows more effectively.
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This is the last resort method when all other approaches fail (no MCP tools available, manifest.json too large or missing). Directly parse the model's SQL/Python code to extract dependencies.
Locate the model file:
models/**/{model_name}.sql or models/**/{model_name}.pymodels/staging/, models/marts/, etc.Read the model file and extract dependencies:
For SQL models:
{{ ref('model_name') }} calls - these are model dependencies{{ source('source_name', 'table_name') }} calls - these are source dependenciesFor Python models:
dbt.ref('model_name') calls - these are model dependenciesdbt.source('source_name', 'table_name') calls - these are source dependenciesFind downstream dependencies (children):
{{ ref('current_model_name') }} across the projectmodels/staging/, models/intermediate/, models/marts/Determine node types (best effort):
models/ directory with .sql or .py extension{{ source() }} calls (you may need to check models/sources.yml or similar)seeds/ directory with .csv extensionmodels/**/*.yml for exposure definitionsBuild limited lineage graph:
Use the Grep tool (not bash grep) and Glob tool (not bash find) for all searches:
ref('customers') in models/source( in models/staging/models/**/{model_name}.sqlUse this method only when:
⚠️ This method provides best-effort lineage and may be incomplete. If possible, try to:
dbt parse and use the manifest.json method insteadevals
skills
dbt
skills
adding-dbt-unit-test
references
answering-natural-language-questions-with-dbt
building-dbt-semantic-layer
configuring-dbt-mcp-server
fetching-dbt-docs
scripts
running-dbt-commands
troubleshooting-dbt-job-errors
references
using-dbt-for-analytics-engineering
working-with-dbt-mesh
dbt-extras
skills
creating-mermaid-dbt-dag
dbt-migration
skills
migrating-dbt-core-to-fusion
migrating-dbt-project-across-platforms