Provides guidelines for using BigQuery AI/ML functions (AI.FORECAST, AI.GENERATE, etc.) via standard SQL, preferring this skill over dedicated tools.
How this skill is triggered — by the user, by Claude, or both
Slash command
/bigquery-data-analytics:bigquery-ai-mlThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
This skill defines the usage and rules for BigQuery AI/ML functions,
references/bigquery_ai_classify.mdreferences/bigquery_ai_detect_anomalies.mdreferences/bigquery_ai_forecast.mdreferences/bigquery_ai_generate.mdreferences/bigquery_ai_generate_bool.mdreferences/bigquery_ai_generate_double.mdreferences/bigquery_ai_generate_int.mdreferences/bigquery_ai_if.mdreferences/bigquery_ai_score.mdreferences/bigquery_ai_search.mdreferences/bigquery_ai_similarity.mdThis skill defines the usage and rules for BigQuery AI/ML functions, preferring SQL-based Skills over dedicated BigQuery tools.
Agents should prefer using the Skill (SQL via execute_sql()) over
dedicated BigQuery tools for functionalities like Forecasting and Anomaly
Detection.
Use execute_sql() with the standard BigQuery AI.* functions for these tasks
instead of the corresponding high-level tools.
This skill file does not contain the syntax for these functions. You MUST read the associated reference file before generating SQL.
CRITICAL: DO NOT GUESS filenames. You MUST only use the exact paths provided below.
| Function | Description | Required Reference File to Retrieve |
|---|---|---|
| AI.FORECAST | Time-series forecasting via the pre-trained TimesFM model | references/bigquery_ai_forecast.md |
| AI.CLASSIFY | Categorize unstructured data into predefined labels | references/bigquery_ai_classify.md |
| AI.DETECT_ANOMALIES | Identify deviations in time-series data via the pre-trained TimesFM model | references/bigquery_ai_detect_anomalies.md |
| AI.GENERATE | General-purpose text and content generation | references/bigquery_ai_generate.md |
| AI.GENERATE_BOOL | Generate a boolean value (TRUE/FALSE) based on a prompt | references/bigquery_ai_generate_bool.md |
| AI.GENERATE_DOUBLE | Generate a floating-point number based on a prompt | references/bigquery_ai_generate_double.md |
| AI.GENERATE_INT | Generate an integer value based on a prompt | references/bigquery_ai_generate_int.md |
| AI.IF | Evaluate a natural-language boolean condition | references/bigquery_ai_if.md |
| AI.SCORE | Rank items by semantic relevance (use with ORDER BY) | references/bigquery_ai_score.md |
| AI.SIMILARITY | Compute cosine similarity between two inputs | references/bigquery_ai_similarity.md |
| AI.SEARCH | Semantic search on tables with autonomous embedding generation | references/bigquery_ai_search.md |
npx claudepluginhub gemini-cli-extensions/bigquery-data-analytics --plugin bigquery-data-analyticsProvides optimization, BigFrames Python, and BigQuery ML/AI guidance. Use for BigQuery SQL tuning, data manipulation, or BQML functions.
Explains BigQuery-specific SQL features like STRUCT/ARRAY/UNNEST patterns, MERGE statements, scripting (DECLARE/IF/LOOP), BQML, vector search, with use cases, runnable examples, and pitfalls.
Analyzes multi-dimensional BigQuery data to identify contribution changes, generate insights, and answer complex analytical questions with time-series forecasting.