dbt Development includes templates for model creation, conversion, optimization, and documentation. Notable examples are prompts for “Create a dbt model called {model_name} that {description}, as well as its yaml documentation” and converting existing SQL into dbt models.SQL Operations covers query writing, performance review, error resolution, and explanation of complex queries with focus on business context and transformations.Data Exploration enables analysis of table structures, value distributions, and identification of relevant tables for specific analyses, including JSON key extraction and analytics execution.Data Quality provides templates for detecting null values, identifying outliers, and comparing development versus production environments through profiling and schema analysis.Git Workflow assists with pull request descriptions documenting business changes and code modifications.Data Service Desk offers comprehensive templates for addressing business requests through either full PR creation or complete analytical investigations.Pandas supports data import and transformation workflows with filtering and column creation capabilities.Machine Learning provides frameworks for feature engineering, forecasting models, and scoring models, emphasizing proper dataset preparation and performance measurement.
