Prompt file imported from jlaws/dotfiles (.codex/prompts/j-data.md). Fill in {{arguments}} before use. Copyright stays with the author.

Before starting, gather diagnostic context:

Detect data stack from config files (dbt_project.yml, airflow.cfg, dagster workspace, prefect.yaml, spark configs).
Identify DB connections by searching for connection strings, database URLs, or ORM config (sqlalchemy, prisma, drizzle, knex).
Check pipeline definitions by searching for DAGs, workflows, ETL scripts, or migration files.
Get scope overview of the target area (if {{arguments}} specifies a pipeline or dataset, scope to that; otherwise scan for data/, pipelines/, dags/, migrations/, etl/ directories).

For deep data-engineering guidance, delegate to the data-engineer agent, passing the diagnostic findings above and the request. It loads its skills (test-driven-development, analysis-output-patterns) and the .agents/references/data/ library, then returns specific guidance. Verify its output before presenting.

Help with: {{arguments}}

Before starting, gather diagnostic context: 1. **Detect data stack** from config files (dbt_project.yml, airflow.cfg, dagster workspace, prefect.yaml, spark configs). 2. **Identify DB connections** by searching for connection strings, database URLs, or ORM config (sqlalchemy, prisma, drizzle, knex). 3. **Check pipeline definitions** by searching for DAGs, workflows, ETL scripts, or migration files. 4. **Get scope overview** of the target area (if {{arguments}} specifies a pipeline or dataset, scope to that; otherwise scan for data/, pipelines/, dags/, migrations/, etl/ directories). For deep data-engineering guidance, delegate to the `data-engineer` agent, passing the diagnostic findings above and the request. It loads its skills (test-driven-development, analysis-output-patterns) and the `.agents/references/data/` library, then returns specific guidance. Verify its output before presenting. Help with: {{arguments}}

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