Prompt Library

Create a dbt model called {model_name} that {description}, as well as its yaml documentation.
{Start from existing dbt models {ref_names}}
{Final output should include these columns: {column list}}
{Create this model in the {layer_name} layer / {folder_name} folder}

Create a staging model from this table, as well as its yaml documentation.

Create a dbt model called {model_name} from this SQL code, as well as its yaml documentation.
{Create this model in the {layer_name} layer / {folder_name} folder}

Explain what this dbt model does, including:
- Business purpose and use case
- Data sources and transformations
- Key metrics and calculations
- Downstream usage

Review this dbt model and suggest improvements for:
- Performance optimization
- Code readability and maintainability
- dbt best practices compliance
- repository best practices compliance

Add yaml documentation for this dbt model, following these rules:
- Add documentation in the relevant yaml file if it already exists, otherwise create it in the relevant folder. 
- Compare dbt model schema and yaml documentation, so that documentation is up to date with dbt model
- Follow existing yaml best practices of repository. 
- Reuse existing documentation already written for some columns.

Add dbt tests for this dbt model, following these rules:
- Add basic tests in the relevant yaml file if it already exists, otherwise create it in the relevant folder. 
- Add unique and not null tests for key/relevant columns only
- Follow existing yaml tests best practices of repository.

Create a custom dbt test for this dbt model that validates {business_rule}.
The test should render all the lines where {conditions when the test fails}

Generate unit tests for this dbt model using dbt's unit testing framework.
Include test scenarios for {test_scenarios} with mock input data and expected outputs.

Show me all the upstream and downstream dependencies for this dbt model.
Include both direct and indirect dependencies with their relationship types.

Find all models that use the column {column_name}.
Show me the complete lineage and how this column flows through the data pipeline.

Rename the column {column_name} to {new_column_name} in {this model / all models}
Identify all downstream models that will be impacted and provide the necessary changes.
Update all related documentation and tests accordingly.

Remove the column {column_name} from {this model / all models}
Identify all downstream models that will be impacted and provide the necessary changes.
Update all related documentation and tests accordingly.

Add a new column {column_name} in this model.
If column is not directly available in this model, add the necessary ref to the model to make it available. Make sure you follow the layers rules in doing so.
Update all related documentation and tests accordingly.

Convert this model to use incremental materialization.
Set up appropriate unique keys, incremental strategy, and handle late-arriving data.

Audit dbt project for better consistency and scalability:
- Detect code logics that can be factorized
- Detect dbt models that can be broken into smaller logics
- Detect dbt models that are not used 
- Detect inconsistencies in fields / metrics definitions
- Detect inconsistencies in the different data layers rules

Rename this model to {new_model_name}.
Update all references, dependencies, and documentation throughout the project.

Write a SQL query that {description}.
{Start from these tables: {table_names}}
{Final output should include these columns: {column list}}
{Create this SQL query in {folder_name} folder / an SQL worksheet}

Review this query and suggest improvements for:
- Performance optimization
- Code readability and maintainability
- repository best practices compliance

Explain what this SQL query does, including:
- Business purpose and use case
- Data sources and transformations
- Key metrics and calculations

Fix this SQL error:
Error: {error_message}

What are the distinct values in the column {column_name}?

Analyze the distribution of values in {column_name} from this table.

Determine the granularity of this table.
Identify the primary key and explain what each row represents in business terms.

Extract the JSON keys from the column {json_column} in this table.
Output the list of all the keys.

What are the most relevant tables and columns in the data warehouse to analyze: {analysis to run / information to find}

Run this analysis: {analysis description}
Write SQL query, and execute it to answer the question.

What's the percentage of null values in this column: {column_name}?

Detect any outlier values in this column: {column_name}

Profile these 2 tables of dev and production and compare them.
Provide a summary of differences in number of rows, schema, value distribution.

Generate a pull request description for the changes made.
Include:
- Business description of changes
- Summary of code changes
- Summary of data output changes

Create all necessary code changes for this business request:
{business_request}
Including models, tests, documentation, and PR description.
Make sure you check the code changes don't break any dependencies in the repository. Run any data quality checks needed to validate the code changes.

Run full analysis to answer this business request:
{business_request}
Find the relevant tables/columns to answer the request, and write the corresponding queries.
Answer with answer in plain english + raw query results to illustrate answer.

Write python code to import this data as a pandas dataframe.
Prepare the dataset with the following transformations:
- Filter data on these rules: {filtering rules}
- Create these new columns: {columns to create}

We want to create a ML model for {describe ML model}
Write feature engineering Python code to prepare the dataset for this ML model:
- Import this data as a pandas dataframe
- Filter data on these rules: {filtering rules}
- Create these new columns: {columns to create}
- Generate features relevant for the model

We want to create a ML model for forecasting {describe forecasting target}
Write Python code to:
- Import this data as a pandas dataframe
- Prepare the dataset for this ML model
- Write the training and test framework
- Implement ML model
- Measure performance of ML model

We want to create a ML model for scoring {describe scoring target}
Write Python code to:
- Import this data as a pandas dataframe
- Prepare the dataset for this ML model
- Write the training and test framework
- Implement ML model
- Measure performance of ML model