Profile BigQuery tables and generate dbt source and model YAML documentation.
npx skills add https://github.com/vnatarajan4061/phoenix --skill document-dbt
Please help me install this Agent Skill. Skill: document-dbt Source: https://github.com/vnatarajan4061/phoenix/tree/main/.claude/skills/document-dbt Command: npx skills add https://github.com/vnatarajan4061/phoenix --skill document-dbt
This Skill automates the documentation of dbt source and model YAML descriptions, ensuring AI-driven query generation accuracy and ground-truth data usage.
Use the document-dbt skill with the path to your dbt source or model YAML file, or specify a BigQuery dataset name to auto-discover all tables and document them in parallel.
High-intent search queries and answers about installing and using this skill.
To automate dbt documentation for BigQuery, this Skill profiles your tables to extract schema metadata, sparse ratios, and enum values, then writes them directly into your source and model YAML files. It supports documenting specific tables or entire datasets.
Yes, you can auto-discover and document all tables within a BigQuery dataset. By specifying the dataset name, the tool profiles all tables and generates the corresponding dbt YAML documentation in parallel.
AI-driven query generation fails without ground-truth schema metadata because it lacks accurate column descriptions and data profiles. Automating dbt YAML documentation ensures AI queries use verified table structures, preventing hallucinated schemas.
The best way to generate dbt source YAML files from BigQuery is by profiling the tables for schema metadata and enum values. This tool automates that process, populating your YAML files with accurate data types and descriptions.
Yes, profiling sparse ratios and enum values is supported. When documenting your dbt models, the tool queries BigQuery to capture these metrics and injects them into your YAML descriptions for better query accuracy.
You need to provide the path to your dbt source or model YAML file to document specific tables. Alternatively, you can specify a BigQuery dataset name to auto-discover tables and generate the documentation automatically.
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