data-context-extractor

To extract tribal knowledge from data warehouse analysts, this Skill interviews domain experts to discover schemas and key tables, then generates tailored reference files and SQL guidance for a new data context skill.

The best way to create a data analysis skill from scratch is using Bootstrap mode, which discovers warehouse schemas, asks targeted questions, and generates an initial skill with domain-specific reference files.

Yes, you can update an existing data context skill using Iteration mode, which loads the current skill, asks targeted questions, and appends or updates domain-specific references to align with warehouse terminology and metrics.

Schema discovery works by identifying warehouse schemas and key tables through targeted questions directed at analysts, then coordinating that discovered data knowledge to generate tailored reference files and SQL guidance.

You need a data warehouse with identifiable schemas and key tables, plus analysts who can provide tribal knowledge, to generate domain-specific data analysis skills and maintain alignment with company metrics.

A data context extractor automates schema discovery and reference-file generation while updating knowledge-base navigation, preventing the manual documentation drift that breaks alignment with evolving warehouse terminology and metrics.