dbt-knowledgebase

To document dbt data models from project research, this Skill identifies findings and proposes structured, evidence-backed knowledge entries into your knowledge base. It categorizes research into understanding, conventions, decisions, domain-rules, debugging, and quirks to ensure consistent documentation across dbt initiatives.

The best way to automate dbt knowledge base entries for onboarding is using a checklist-guided workflow that proposes structured entries at org, project, and connection scopes. This reduces manual note-taking by building evidence-based proposals across six predefined knowledge categories.

Yes, you can propose dbt domain rules and conventions at the project scope. The Skill supports categorizing research findings at org-wide, project-level, and connection-level scopes, outputting structured knowledge proposals via the defined propose_knowledge function.

You structure dbt research findings into evidence-backed knowledge by categorizing them into six specific areas: understanding, conventions, decisions, domain-rules, debugging, and quirks. The Skill processes your project research to generate structured proposals with evidence-backed bodies for each entry.

Yes, dbt knowledge base generation works without external dependencies. The Skill operates independently to identify research findings and output knowledge proposals via the internal propose_knowledge function, requiring no additional components or external integrations to populate entries.