SKILL.md

name	bigquery-analytics
description	Use these skills when you need to handle advanced data intelligence and predictive tasks. Use when a user asks "why" data changed or needs future projections. Provides automated insight generation and time-series forecasting.

Usage

All scripts can be executed using Node.js. Replace <param_name> and <param_value> with actual values.

Bash: node <skill_dir>/scripts/<script_name>.js '{"<param_name>": "<param_value>"}'

PowerShell: node <skill_dir>/scripts/<script_name>.js '{\"<param_name>\": \"<param_value>\"}'

Note: The scripts automatically load the environment variables from various .env files. Do not ask the user to set vars unless skill executions fails due to env var absence.

Scripts

analyze_contribution

Use this skill to analyze the contribution about changes to key metrics in multi-dimensional data.

Parameters

Name	Type	Description	Required	Default
input_data	string	The data that contain the test and control data to analyze. Can be a fully qualified BigQuery table ID or a SQL query.	Yes
contribution_metric	string	The name of the column that contains the metric to analyze.

	Provides the expression to use to calculate the metric you are analyzing.
	To calculate a summable metric, the expression must be in the form SUM(metric_column_name),
	where metric_column_name is a numeric data type.

	To calculate a summable ratio metric, the expression must be in the form
	SUM(numerator_metric_column_name)/SUM(denominator_metric_column_name),
	where numerator_metric_column_name and denominator_metric_column_name are numeric data types.

	To calculate a summable by category metric, the expression must be in the form
	SUM(metric_sum_column_name)/COUNT(DISTINCT categorical_column_name). The summed column must be a numeric data type.
	The categorical column must have type BOOL, DATE, DATETIME, TIME, TIMESTAMP, STRING, or INT64. | Yes |  |

| is_test_col | string | The name of the column that identifies whether a row is in the test or control group. | Yes | | | dimension_id_cols | array | An array of column names that uniquely identify each dimension. | No | | | top_k_insights_by_apriori_support | integer | The number of top insights to return, ranked by apriori support. | No | 30 | | pruning_method | string | The method to use for pruning redundant insights. Can be 'NO_PRUNING' or 'PRUNE_REDUNDANT_INSIGHTS'. | No | PRUNE_REDUNDANT_INSIGHTS |

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ask_data_insights

Use this skill to perform data analysis, get insights, or answer complex questions about the contents of specific BigQuery tables.

Parameters

Name	Type	Description	Required	Default
user_query_with_context	string	The user's question, potentially including conversation history and system instructions for context.	Yes
table_references	string	A JSON string of a list of BigQuery tables to use as context. Each object in the list must contain 'projectId', 'datasetId', and 'tableId'. Example: '[{"projectId": "my-gcp-project", "datasetId": "my_dataset", "tableId": "my_table"}]'.	Yes

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forecast

Use this skill to forecast time series data.

Parameters

Name	Type	Description	Required	Default
history_data	string	The table id or the query of the history time series data.	Yes
timestamp_col	string	The name of the time series timestamp column.	Yes
data_col	string	The name of the time series data column.	Yes
id_cols	array	An array of the time series id column names.	No	[]
horizon	integer	The number of forecasting steps.	No	10

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search_catalog

Use this skill to find tables, views, models, routines or connections.

Parameters

Name	Type	Description	Required	Default
prompt	string	Prompt representing search intention. Do not rewrite the prompt.	Yes
datasetIds	array	Array of dataset IDs.	No	[]
projectIds	array	Array of project IDs.	No	[]
types	array	Array of data types to filter by.	No	[]
pageSize	integer	Number of results in the search page.	No	5

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