bigquery-ai-ml
Skill52 starsChanged 13 months agoApache-2.0View on GitHub
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.
7 other projects use a version of this, 6 of them edited.
Adopted by
Most recently taken up by
- oivanfelipe/claude-skillsedited, first seen Nov 8, 2026
- olaitanojo/skillsedited, first seen Oct 17, 2026
- google/adk-pythonedited, first seen Oct 13, 2026
- GoogleCloudPlatform/bigquery-data-analytics-pluginword for word, first seen Oct 9, 2026
- google/skillsedited, first seen Sep 29, 2026
- Adhamxon/claude-code-skillsedited, first seen Jul 30, 2026
Other versions7 projects use a version of this.
Word for word · 1
- GoogleCloudPlatform/bigquery-data-analytics-plugin★ 52100% same
Edited · 6
- google/skills★ 24k88% same
- google/adk-python★ 22k57% same
- GoogleCloudPlatform/data-agent-kit-plugin★ 24073% same
- Adhamxon/claude-code-skills★ 237% same
- olaitanojo/skills★ 155% same
- oivanfelipe/claude-skills★ 055% same
Wrote their own version · 0
None found.
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repositoryOne other file it gives its agents.
The file
Files
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.
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 1 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 regime 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 regime 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). |
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.