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, detect outliers, find key drivers, or leverage generative AI capabilities in BigQuery.
NewUpdated Jun 28, 2026
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.
Reference Directory
AI Forecast: Leveraging pre-trained
TimesFM model for forecasting without custom training.
AI Detect Anomalies: Identify
deviations in time series data using pre-trained TimesFM model.
AI Generate: General-purpose text and
content generation using Gemini models.
AI Key Drivers: Automatically identify
dimensional segments most responsible for driving changes in a metric.
Related Skills
BigQuery Basics Skill:
SKILL.md file for core BigQuery concepts, resource management, CLI,
and client libraries.
Files5
5 files · 26.5 KB
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Overall Score
82/100
Grade
B
Good
Grades are signals, not a certification. Always review a skill yourself before use.
Safety
88
Quality
82
Clarity
88
Completeness
72
Summary
A reference documentation skill for BigQuery's built-in AI and ML functions (`AI.FORECAST`, `AI.DETECT_ANOMALIES`, `AI.GENERATE`, `AI.KEY_DRIVERS`). The skill provides SQL syntax references, parameter tables, output schemas, and practical examples for performing time-series forecasting, anomaly detection, text generation, and key driver analysis directly within BigQuery queries without custom model training.
Detected Capabilities
SQL query writingBigQuery API interactionML model inferenceGenAI text generationTime-series analysisAnomaly detection
Trigger Keywords
Phrases that agents use to match this skill to user intent.
forecast time seriesdetect anomaliesgenerate text sqlidentify key driversbigquery ml functionstimesfm model
Referenced Domains
External domains referenced in skill content, detected by static analysis.
www.apache.org
Use Cases
Forecast time-series data using pre-trained TimesFM models without custom training
Detect anomalies in time-series data by comparing target data against historical patterns
Generate text and structured content using Gemini models within SQL queries
Identify key dimensional segments responsible for metric changes between interest and reference groups
Quality Notes
Comprehensive syntax reference with clear parameter documentation for each function
Well-structured output schema tables with descriptions for all return columns
Practical, runnable examples for each function with realistic data sources
Clear relationship documented between this skill and the BigQuery Basics skill for broader context
Examples include edge cases (e.g., multivariate detection, structured output generation)
Excellent use of markdown formatting with consistent table layouts and code blocks
Reference directory provides quick navigation to specific function documentation
No error handling guidance or common troubleshooting documentation provided
Limited guidance on performance implications or cost considerations for large datasets
No discussion of data requirements (e.g., minimum data points, data quality expectations) beyond mention of '3 data points minimum' in status fields
Model: claude-haiku-4-5-20251001Analyzed: Jun 28, 2026
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Version History
v2.0
B
Contract changed: description
✦ AIAdds AI.KEY_DRIVERS function documentation and activation condition for driver analysis workloads.