Bigquery AI ML

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Tool integration
Data and AI
Apache-2.0
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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/vector_search.md

VECTOR_SEARCH

BigQuery VECTOR_SEARCH is a table-valued function used to find semantically similar entities by searching embeddings in a base table against embeddings in a query table.

Syntax

VECTOR_SEARCH(
  { TABLE base_table | (base_table_query) },
  column_to_search,
  { TABLE query_table | (query_table_query) },
  [, query_column_to_search => query_column_to_search_value]
  [, top_k => top_k_value ]
  [, distance_type => distance_type_value ]
  [, options => options_value ]
)

Arguments

Argument	Requirement	Type	Description
`base_table`	One Of	TABLE	The table containing the embeddings to search. Mutually exclusive with base_table_query.
`base_table_query`	One Of	QUERY	A query that you can use to pre-filter the base table. Only SELECT, FROM, and WHERE clauses are allowed in this query. Don't apply any filters to the embedding column. Mutually exclusive with base_table.
`column_to_search`	Required	String	The name of the base table column containing embeddings (`ARRAY<FLOAT64>` or `STRING`).
`query_table`	One Of	TABLE	The table that provides the embeddings for which to find nearest neighbors. All columns are passed through as output columns. Mutually exclusive with query_table_query
`query_table_query`	One Of	QUERY	A query that provides the embeddings for which to find nearest neighbors. All columns are passed through as output columns. Mutually exclusive with query_table
`query_column_to_search`	Optional	String	The name of the column in the query table. Defaults to `column_to_search`. The value can be either `STRING` or `ARRAY<FLOAT64>`. If `STRING`, the base_table must have autonomous embedding generation enabled. The string values are embedded at runtime using the same connection and endpoint specified for the base table's embedding generation. If the value is an `ARRAY<FLOAT64>`, all elements in the array must be non-NULL and all values in the column must have the same array dimensions as the values in the column_to_search column.
`top_k`	Optional	Int64	The number of nearest neighbors to return. Defaults to 10.
`distance_type`	Optional	String	The metric to use: `'EUCLIDEAN'`, `'COSINE'`, or `'DOT_PRODUCT'`. Defaults to `'EUCLIDEAN'`.
`options`	Optional	String	JSON string for search options (e.g., `use_brute_force`, `fraction_lists_to_search`).

Output Columns

Column	Type	Description
`base`	STRUCT	A STRUCT value that contains all columns from base_table or a subset of the columns from base_table that you selected in the base_table_query query.
`query`	STRUCT	A STRUCT value that contains all selected columns from the query data. This column is only included in the output if you use the batch search syntax. For single vector searches, this column is omitted.
`distance`	FLOAT64	A FLOAT64 value that represents the distance between the base data and the query data.

Example: Combined Vector Search with AI.GENERATE_EMBEDDING

SELECT
  query.content AS search_query,
  base.product_name,
  base.description,
  distance
FROM
  VECTOR_SEARCH(
    TABLE `my_project.my_dataset.products`,
    'product_embedding',
    (
      SELECT
        content,
        embedding
      FROM
        AI.GENERATE_EMBEDDING(
          MODEL `my_project.my_dataset.embedding_model`,
          (SELECT "high-performance running shoes" AS content)
        )
    ),
    top_k => 5,
    distance_type => 'COSINE'
  );

Example: Optimized Single Search

This version is optimized for finding neighbors for a single value.

SELECT
  base.product_name,
  base.description,
  distance
FROM
  VECTOR_SEARCH(
    TABLE `my_project.my_dataset.products`,
    'product_embedding',
    query_value => (
      SELECT embedding
      FROM AI.GENERATE_EMBEDDING(
        MODEL `my_project.my_dataset.embedding_model`,
        (SELECT "high-performance running shoes" AS content)
      )
    ),
    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

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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.

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