Analyzes BigQuery data for contributions to metric changes, generates insights from queries, and performs time-series forecasting. Useful for explaining data shifts or future projections.
npx claudepluginhub gemini-cli-extensions/bigquery-data-analytics --plugin bigquery-data-analyticsThis skill uses the workspace's default tool permissions.
All scripts can be executed using Node.js. Replace `<param_name>` and `<param_value>` with actual values.
Guides BigQuery engineering with bq CLI for queries, table ops, data load/export; GoogleSQL syntax, functions, window funcs; partitioning, clustering, optimization.
Generates SQL queries for BigQuery AI/ML using AI.* functions like FORECAST, CLASSIFY, DETECT_ANOMALIES, GENERATE, SCORE, and SEARCH. Preferred over dedicated tools for time-series forecasting, semantic search, and text generation.
Answers data questions via SQL on connected warehouses: quick metrics, trend investigations, segment comparisons, formal reports. Uses manual input if no connection.
Share bugs, ideas, or general feedback.
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.
Use this skill to analyze the contribution about changes to key metrics in multi-dimensional data.
| 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 |
Use this skill to perform data analysis, get insights, or answer complex questions about the contents of specific BigQuery tables.
| 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 |
Use this skill to forecast time series data.
| 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 |
Use this skill to find tables, views, models, routines or connections.
| 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 |