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bigquery-ai-ml — Claude Code skill: what it does, how to install

This skill helps write BigQuery SQL for built-in ML and GenAI tasks. Use it for forecasting, anomaly detection, semantic search, and text analysis.

Install into Claude Code · Skills CLI (Vercel) All methods and platforms ↓
npx skills add google/skills --skill bigquery-ai-ml -a claude-code
Open on GitHub

What bigquery-ai-ml does

bigquery-ai-ml brings BigQuery's built-in machine learning and GenAI functions into SQL work. It covers time-series forecasts, value prediction, anomaly detection, key-driver analysis, correlations, and structural shifts in time series.

The skill also documents text classification, summarization, translation, model evaluation, semantic conditions, similarity, embeddings, and vector search. Its reference material includes functions such as AI.FORECAST, AI.GENERATE, AI.SEARCH, ML.TREND, and ML.CORRELATION.

Install it when your analytics work happens in BigQuery and the agent needs to write SQL for ML tasks. It is the wrong fit for general dataset, table, or job management.

What it helps with

  • forecast values in a time series
  • detect anomalies and structural changes
  • classify or summarize text in BigQuery
  • run semantic or vector search
  • measure the effect of an intervention

Example prompts for the agent

  • Write a BigQuery SQL query with AI.FORECAST for this time series.
  • Use AI.DETECT_ANOMALIES to find unusual values and explain the result.
  • Build a semantic search query with AI.SEARCH for these text records.

How to install bigquery-ai-ml

1Skills CLI (Vercel)

npx skills add google/skills --skill bigquery-ai-ml -a claude-code

2Manually: put the files in place

~/.claude/skills/bigquery-ai-ml/SKILL.md

Copy the whole skill folder; for one project use .claude/skills/bigquery-ai-ml/

From the same set172

The whole set →
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Questions about bigquery-ai-ml

How do I install bigquery-ai-ml in Claude Code?

Run npx skills add google/skills --skill bigquery-ai-ml -a claude-code. The skill file is placed at ~/.claude/skills/bigquery-ai-ml/SKILL.md.

What does bigquery-ai-ml do?

It guides the agent in writing BigQuery SQL that uses built-in ML and GenAI functions for forecasting, anomaly detection, text classification, semantic search, and related tasks.

Which platforms support bigquery-ai-ml?

The skill targets Claude Code, Gemini CLI, and Codex. The source also includes installation commands for environments such as GitHub Copilot.

Source text

SKILL.md · Apache-2.0
---
name: bigquery-ai-ml
metadata:
  version: "1.1.1"
  category: AiAndMachineLearning
description: >-
  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,
  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.
---

# 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](references/attribution.md). 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.

## Reference Directory

-   **Functions Reference**:

    -   **AI.AGG**: [ai_agg.md](references/ai_agg.md) - Multi-row semantic
        aggregation and summarization.
    -   **AI.CAUSAL_EFFECT**:
        [ai_causal_effect.md](references/ai_causal_effect.md) - Quantifies the
        impact of an intervention on a time series.
    -   **AI.CLASSIFY**: [ai_classify.md](references/ai_classify.md) - Classify
        text.
    -   **AI.DETECT_ANOMALIES**:
        [ai_detect_anomalies.md](references/ai_detect_anomalies.md) - Detect
        anomalies.
    -   **AI.EVALUATE**: [ai_evaluate.md](references/ai_evaluate.md) - Evaluate
        models.
    -   **AI.FORECAST**: [ai_forecast.md](references/ai_forecast.md) -
        Time-series forecasting.
    -   **AI.GENERATE**: [ai_generate.md](references/ai_generate.md) - Generate
        text using LLMs.
    -   **AI.GENERATE_EMBEDDING**:
        [ai_generate_embedding.md](references/ai_generate_embedding.md) -
        Generate embeddings.
    -   **AI.GENERATE_TABLE**:
        [ai_generate_table.md](references/ai_generate_table.md) - Table-valued
        AI generation.
    -   **AI.IF**: [ai_if.md](references/ai_if.md) - Evaluate semantic
        conditions.
    -   **AI.KEY_DRIVERS**: [ai_key_drivers.md](references/ai_key_drivers.md) -
        Identifies key drivers, this is a TVF.
    -   **AI.SCORE**: [ai_score.md](references/ai_score.md) - Score data.
    -   **AI.SEARCH**: [ai_search.md](references/ai_search.md) - Semantic
        search.
    -   **AI.SIMILARITY**: [ai_similarity.md](references/ai_similarity.md) -
        Semantic similarity.
    -   **Remote Models**: [remote_models.md](references/remote_models.md) -
        Working with remote models (Vertex AI).
    -   **CONTRIBUTION_ANALYSIS**:
        [ml_contribution_analysis.md](references/ml_contribution_analysis.md)
        -   Finds contributing factors, key drivers of change. Requires creating
            a MODEL entity.
    -   **ML.CORRELATION**: [ml_correlation.md](references/ml_correlation.md) -
        Calculates correlation between columns, optionally sliced by dimensions.
    -   **ML.DETECT_CHANGE_POINTS**:
        [ml_detect_change_points.md](references/ml_detect_change_points.md) -
        Detects structural breaks or sustained shifts in a time series.
    -   **ML.SEASONALITY**: [ml_seasonality.md](references/ml_seasonality.md) -
        Extracts seasonal components from a time series.
    -   **ML.TREND**: [ml_trend.md](references/ml_trend.md) - Extracts the
        long-term trend component from a time series.
    -   **VECTOR_SEARCH**: [vector_search.md](references/vector_search.md) -
        Vector search best practices.

## Related Skills

-   [BigQuery Basics Skill](../bigquery-basics): SKILL.md file for core BigQuery
    concepts, resource management, CLI, and client libraries.

© google · Apache-2.0 · google/skills

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