data-context-extractor

Extract tribal knowledge from data analysts by using guided Q&A sessions to discover database schemas, identify key tables, and document company-specific terminology. This process captures implicit knowledge and translates it into structured reference files for data warehouses like BigQuery, Snowflake, and Databricks.

Build a custom data analysis skill through bootstrap mode, which guides you step-by-step to discover schemas and define key metrics like ARR and LTV. It generates a tailored skill by interviewing analysts about their query patterns and terminology.

Update an existing data analysis skill using iteration mode, which facilitates targeted discovery to add context about new domains or terminology. This mode enhances your current skill by appending new metric definitions and updating reference files.

This approach works with Snowflake, BigQuery, and Databricks environments by facilitating schema discovery and query pattern documentation specific to those platforms. It extracts tribal knowledge to help AI models understand your company-specific data warehouse setup.

Document SQL query patterns and common pitfalls by guiding data analysts through targeted questions that reveal their workflow. The extracted insights are saved as reference files, ensuring AI assistants understand company-specific query logic and avoid common mistakes.