Bigquery AI ML

GoogleOfficialPublished by Google

Skill
Tool integration
Data and AI
Apache-2.0
No issues found

From the author

Leverages BigQuery's built-in machine learning and GenAI capabilities for advanced data analytics. Use when you need to write SQL queries that perform time-series forecasting, predict values, detect outliers or anomalies, find key drivers, perform semantic search or vector search, classify text, calculate similarity, summarize content, translate language, evaluate models, filter by semantic conditions, measure the causal effect of an intervention, compute correlations between columns, generate descriptive statistics and table distribution profiles, detect change points or structural breaks, extract trend or seasonality components, or leverage generative AI capabilities in BigQuery. Do not use for general BigQuery dataset, table, or job management requests.

— the description by Google

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The BigQuery AI & ML skill provides a comprehensive interface for executing advanced data analytics, machine learning, and generative AI tasks directly inside BigQuery using SQL. It enables users to perform complex operations like time-series forecasting, anomaly detection, semantic search, and LLM-based content generation without needing to export data to external environments. The skill covers a wide range of built-in functions, including zero-shot models for forecasting and classification, as well as tools for statistical analysis and causal inference.

What it does

Performs zero-shot time-series forecasting and anomaly detection.
Executes LLM-based text summarization, classification, and semantic scoring.
Conducts causal effect analysis on time-series data.
Generates descriptive statistics and correlation matrices for datasets.
Enables semantic and vector search capabilities within SQL.
Extracts trends and seasonality components from historical data.

When to use it

When you need to perform ML or GenAI tasks directly on data stored in BigQuery.
When you want to avoid moving data out of BigQuery for analysis or model inference.
When you need to analyze time-series data for shifts, trends, or causal impacts.
When you need to perform semantic operations like summarization or classification at scale.
When you need to identify key drivers behind metric changes in your data.

Advantages

Seamless integration with existing BigQuery SQL workflows.
Eliminates the need for complex data pipelines to external ML services.
Supports zero-shot learning for rapid insights without custom model training.
Scalable processing for large datasets directly within the database engine.
Unified syntax for both traditional statistical analysis and modern generative AI.

Usage examples

“How can I forecast future values for my sales data in BigQuery?”: The agent will guide you to use the AI.FORECAST function, explaining how to specify your data column, timestamp column, and the required forecast horizon.
“Summarize the sentiment of my customer reviews stored in a BigQuery table.”: The agent will demonstrate how to use the AI.AGG function with a natural language instruction to synthesize sentiment across grouped rows.
“Detect anomalies in my server log time-series data.”: The agent will provide a query using AI.DETECT_ANOMALIES to identify isolated point anomalies in your time-series data.
“Find the key drivers for a drop in website traffic.”: The agent will show you how to use the AI.KEY_DRIVERS function to attribute metric changes to specific dimensional segments.

Install

Download the skill as a ZIP:
https://autogineer.com/agent-skills/google/bigquery-ai-ml/download

Unzip it into the cross-client skills folder of your project:

mkdir -p .agents/skills && unzip bigquery-ai-ml.zip -d .agents/skills

Documentation by Google

BigQuery AI & ML

BigQuery integrates with Vertex AI to provide powerful machine learning and generative AI capabilities directly within SQL queries using built-in functions like AI.FORECAST, AI.KEY_DRIVERS, AI.DETECT_ANOMALIES, and AI.GENERATE.

Attribution

MUST READ: Before executing any gcloud, REST API, or MCP command, you MUST read Usage Attribution. For commands executed directly in the terminal (never in generated scripts, Terraform, or user code), prefix gcloud with CLOUDSDK_METRICS_REQUEST_ATTRIBUTION="agent-skills/1.0 (skill:bigquery-ai-ml)" and append agent-skills/1.0 (skill:bigquery-ai-ml) to User-Agent headers.

Function Selection & Reference Directory

Function	Reference	SQL Shape	Requires `CREATE MODEL` or `CREATE VECTOR INDEX`?	When to Use & Disambiguation
`AI.AGG`	ai_agg.md	`Aggregate`	No	Multi-row semantic aggregation with `GROUP BY`; synthesizes one summary string across many rows (unlike per-row `AI.GENERATE`).
`AI.CAUSAL_EFFECT`	ai_causal_effect.md	`TVF`	No	Quantifies counterfactual post-intervention impact on a time series after a specific treatment timestamp.
`AI.CLASSIFY`	ai_classify.md	`Scalar`	No	Semantic classification of text or multimodal unstructured data into predefined category labels.
`AI.DETECT_ANOMALIES`	ai_detect_anomalies.md	`TVF`	No	Zero-shot TimesFM detector for isolated time-series point anomalies/outliers (use `ML.DETECT_CHANGE_POINTS` for sustained shifts).
`AI.EVALUATE`	ai_evaluate.md	`TVF`	No	Zero-shot TimesFM time-series forecast evaluation/backtesting against a historical holdout split.
`AI.FORECAST`	ai_forecast.md	`TVF`	No	Zero-shot TimesFM time-series forecasting without training a custom model.
`AI.GENERATE`	ai_generate.md	`Scalar`	No	Default for per-row LLM text or multimodal structured extraction (`output_schema`); returns `STRUCT`. Prefer over `AI.GENERATE_TABLE`.
`AI.GENERATE_EMBEDDING`	ai_generate_embedding.md	`TVF`	Yes	Batch generates `ARRAY<FLOAT64>` vector embeddings for text or visual content using a remote embedding model.
`AI.GENERATE_TABLE`	ai_generate_table.md	`TVF`	Yes	Table-valued structured extraction (`FROM` clause); only use instead of `AI.GENERATE` when a remote model + TVF is explicitly needed.
`AI.IF`	ai_if.md	`Scalar`	No	Evaluates boolean semantic conditions (`BOOL`) on text or multimodal data for `WHERE`/`CASE`/`JOIN` filtering (do not use `AI.GENERATE_TABLE`).
`AI.KEY_DRIVERS`	ai_key_drivers.md	`TVF`	No	Default one-step attribution of dimensional segments driving metric changes (`interest_label_col` `TRUE` vs. `FALSE`). Prefer over `CONTRIBUTION_ANALYSIS`.
`AI.PREDICT`	ai_predict.md	`TVF`	No	Zero-shot TabFM foundation model for tabular classification or regression without training and managing custom models.
`AI.SCORE`	ai_score.md	`Scalar`	No	Assigns a continuous `FLOAT64` semantic score to text or multimodal data for ranking or sorting (`ORDER BY`).
`AI.SEARCH`	ai_search.md	`TVF`	No	Simplified semantic search using a raw string query on tables with autonomous background embeddings (`AI.EMBED` generated column).
`AI.SIMILARITY`	ai_similarity.md	`Scalar`	No	Computes an on-the-fly semantic similarity score (`FLOAT64`) between two text or multimodal inputs without stored embeddings.
`Remote Models`	remote_models.md	`CREATE MODEL`	Yes	Creates a Vertex AI `REMOTE WITH CONNECTION DEFAULT` model entity for functions that require one (`AI.GENERATE_TABLE`, `AI.GENERATE_EMBEDDING`, `VECTOR_SEARCH`).
`CONTRIBUTION_ANALYSIS`	ml_contribution_analysis.md	`CREATE MODEL`	Yes	Model-based driver analysis (`CREATE MODEL ... OPTIONS(MODEL_TYPE='CONTRIBUTION_ANALYSIS')` + `ML.GET_INSIGHTS`). Use `AI.KEY_DRIVERS` unless a `MODEL` entity is required.
`ML.CORRELATION`	ml_correlation.md	`TVF`	No	Calculates statistical correlation coefficients between numeric columns, optionally sliced by dimensions.
`ML.DESCRIBE_DATA`	ml_describe_data.md	`TVF`	No	Generates descriptive statistics (e.g., count, mean, median, min, max, stddev, quantiles, nulls, top values) for columns in a table or subquery.
`ML.DETECT_CHANGE_POINTS`	ml_detect_change_points.md	`TVF`	No	Detects structural breaks or sustained shifts (`[begin_timestamp, end_timestamp]` intervals) in a time series.
`ML.SEASONALITY`	ml_seasonality.md	`TVF`	No	Extracts recurring periodic/seasonal components from a historical time series.
`ML.TREND`	ml_trend.md	`TVF`	No	Extracts the smoothed long-term trend component from a historical time series.
`VECTOR_SEARCH`	vector_search.md	`TVF`	Yes	Searches pre-computed `ARRAY<FLOAT64>` embedding columns between a base table and query table (paired with `AI.GENERATE_EMBEDDING`, unlike `AI.SEARCH`).

BigQuery Basics Skill: SKILL.md file for core BigQuery concepts, resource management, CLI, and client libraries.

Files

ATTRIBUTION.mdAdded by AutoGineer846 B
LICENSELicence file added by AutoGineer11.1 KiB
SKILL.md8.8 KiB
references/ai_agg.md3.5 KiB
references/ai_causal_effect.md11.2 KiB
references/ai_classify.md3.7 KiB
references/ai_detect_anomalies.md5.0 KiB
references/ai_evaluate.md2.2 KiB
references/ai_forecast.md11.1 KiB
references/ai_generate.md3.4 KiB
references/ai_generate_embedding.md2.0 KiB
references/ai_generate_table.md3.3 KiB
references/ai_if.md2.0 KiB
references/ai_key_drivers.md4.6 KiB
references/ai_predict.md8.5 KiB
references/ai_score.md1.9 KiB
references/ai_search.md3.7 KiB
references/ai_similarity.md2.7 KiB
references/attribution.md1.8 KiB
references/ml_contribution_analysis.md4.8 KiB
references/ml_correlation.md8.9 KiB
references/ml_describe_data.md5.1 KiB
references/ml_detect_change_points.md5.5 KiB
references/ml_seasonality.md6.6 KiB
references/ml_trend.md4.1 KiB
references/remote_models.md1.1 KiB
references/vector_search.md4.0 KiB

references/ai_key_drivers.md

BigQuery AI.Key_Drivers

AI.KEY_DRIVERS automatically identifies the key dimensional segments most responsible for driving changes in a specified metric between a defined interest group and a reference group.

Syntax Reference

SELECT
  *
FROM
  AI.KEY_DRIVERS(
    { TABLE TABLE | (QUERY_STATEMENT) },
    metric_col => 'METRIC_COL',
    dimension_cols => DIMENSION_COLS,
    interest_label_col => 'INTEREST_LABEL_COL'
    [, min_apriori_support => MIN_APRIORI_SUPPORT]
    [, top_k => TOP_K]
    [, enable_pruning => ENABLE_PRUNING]
  )

Input Arguments

Argument	Requirement	Type	Description
`input_data`	Required		The source table or subquery containing the data to analyze.
`metric_col`	Required	`STRING`	Metric column name. Must be of type: INT64, NUMERIC, BIGNUMERIC, or FLOAT64. The values in the metric column that you use in the `metric_col` option must be non-negative, unless you specify 0 for the `min_apriori_support` value.
`interest_label_col`	Required	`STRING`	Boolean column name: `TRUE` for interest group, `FALSE` for reference group.
`dimension_cols`	Required	`ARRAY<STRING>`	1-12 dimension columns (INT64, BOOL, STRING); cannot be `metric_col` or `interest_label_col`.
`min_apriori_support`	Optional	`FLOAT64`	Minimum apriori support threshold [0, 1] for output segments. Default: 0.1. Cannot be used with `top_k`.
`top_k`	Optional	`INT64`	Return top k insights [1, 1M] by apriori support. If unset, uses `min_apriori_support=0.1`. Cannot be used with `min_apriori_support`.
`enable_pruning`	Optional	`BOOL`	If `TRUE` (default), redundant insights are pruned. If `FALSE`, all insights meeting thresholds are returned. Two segments are redundant if two conditions are met: 1) their metric values are equal 2) The dimensions and corresponding values of one row are a subset of the dimensions and corresponding values of the other. In this case, the row with more dimensions (the more descriptive row) is kept.

Output Schema

Returns a STRUCT with the following fields:

Column Name	Type	Description
`drivers`	`ARRAY<STRING>`	Provides a list of drivers, or dimension values of interest, which describes each of the segments.
`metric_interest`	`NUMERIC`	The sum of the metric_column for the data in the interest segment.
`metric_reference`	`NUMERIC`	The sum of the metric_column for data in the reference segment.
`difference`	`NUMERIC`	The difference between the interest and reference metric values for a segment.
`relative_difference`	`NUMERIC`	The relative change of a segment, calculated as the difference divided by the reference metric value.
`unexpected_difference`	`NUMERIC`	Measures deviation of segment from the rest of the population's growth. Calculated as: (segment relative_difference - complement relative_difference) * segment reference metric.
`relative_unexpected_difference`	`NUMERIC`	The unexpected_difference divided by the expected interest metric value for a segment.
`contribution`	`NUMERIC`	Contains the absolute value of the difference value: `ABS(difference)`.
`apriori_support`	`NUMERIC`	Segment size relative to the total population (filters small segments).

Examples

Identifying Key Drivers in 2024 H2 Liquor Sales

WITH InputData AS (
  SELECT
    sale_dollars,
    city,
    category_name,
    vendor_name,
    (date > '2024-07-01') AS IS_H2
  FROM `bigquery-public-data.iowa_liquor_sales.sales`
  WHERE EXTRACT(YEAR FROM DATE) = 2024
)
SELECT *
FROM AI.KEY_DRIVERS(
  TABLE InputData,
  metric_col => 'sale_dollars',
  dimension_cols => ['city', 'vendor_name', 'category_name'],
  interest_label_col => 'IS_H2',
  min_apriori_support => 0
);

Finding Top 5 Key Drivers in Bottle Costs

SELECT *
FROM AI.KEY_DRIVERS(
  (
    SELECT
      state_bottle_cost,
      city,
      category_name,
      (EXTRACT(MONTH FROM date) >= 7) AS is_h2
    FROM `bigquery-public-data.iowa_liquor_sales.sales`
    WHERE EXTRACT(YEAR FROM date) = 2024
  ),
  metric_col => 'state_bottle_cost',
  dimension_cols => ['city', 'category_name'],
  interest_label_col => 'is_h2',
  top_k => 5
);

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Security

No issues found by our static scan· No issues found

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Published by

Google

Publisher

Google (Official)

Source

google/skills · skills/cloud/bigquery-ai-ml · commit 2194d3b

Licence

Apache License 2.0 (redistribution allowed)

LICENSE (added by AutoGineer from LICENSE)
ATTRIBUTION.md (written by AutoGineer)

Synced

2026-11-09

AutoGineer is not affiliated with or endorsed by Google or Google.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download bigquery-ai-ml.zip