data-context-extractor

Extracting tribal knowledge from data warehouse schemas is done by discovering tables and asking targeted questions to generate structured reference files for entities, metrics, and tables. This bridges the gap between raw data and actionable insights.

Generating a custom data analysis skill from scratch uses bootstrap mode to discover your warehouse schemas and ask targeted questions, codifying your organization's unique data knowledge into accessible reference files.

Adding domain context to existing data analysis skills is best achieved through iteration mode, which enhances your current skills by incorporating new domains, metrics, and terminology specific to your company.

Yes, you can use this to help new analysts query Snowflake data warehouses by creating a tailored data analysis skill that understands specific customer definitions, key sales metrics, and common data pitfalls.

Structured reference files generated for data warehouse entities and metrics capture your specific domain context, table definitions, and key metrics, making your organization's unique data knowledge accessible to AI.