bigquery-ai-ml

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.

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Repository: google/adk-python
Letzte Quellaktivität: 8. Oktober 2026 um 16:56
Erkannte Sprache von SKILL.md: Englisch
Sterne: 21.778
Forks: 4.129

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Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.

Mit einem KI-Assistenten installieren

Diesen Prompt kopieren und in den verwendeten KI-Assistenten einfügen.

Follow https://skillsmp.com/skill-install/prompt.md to install only the Agent Skill at https://github.com/google/adk-python/tree/main/src/google/adk/integrations/bigquery/skills/bigquery-ai-ml.

Ein direkter Befehl überspringt den Prüf-Prompt. Prüfen Sie die Quelle, bevor Sie ihn ausführen.

npx skills add https://github.com/google/adk-python --skill bigquery-ai-ml

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SKILL.md

Quellanweisungen · Schreibgeschützte Vorschau

name: bigquery-ai-ml
license: Apache-2.0
metadata: {"author":"google-adk","version":"1.0"}
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`. ## 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.

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adk-architecture

Explains how the ADK runtime fits together: the node and graph execution model, Context and Event flow, checkpoint and resume, tracing, and the rules governing the public API surface. Use when answering "how does X work" about ADK internals, tracing where an event or a piece of state comes from, deciding where a new capability belongs, reviewing a change to BaseNode, Workflow, Runner, Agent, Event or Context, working out why a node re-ran or stayed waiting after a resume, or judging whether a change breaks the public API. Don't use for assembling an agent from existing pieces (use adk-agent-builder), diagnosing one failing run or test (use adk-debug), or formatting and naming conventions (use adk-style).

2026-10-09

adk-review

Reviews the uncommitted changes in an adk-python working tree and reports correctness, design, public-API stability, test, sample and documentation gaps as a prioritized findings report, fixing them only when asked. Use when the user asks to review local changes, wants a self-review before opening a pull request, asks whether a change breaks the public API or needs tests, samples or docs, or asks what is wrong with the current diff. Required for changes to public APIs, core architecture (Runner, Workflow, BaseNode), new features and major refactors. Don't use for a single style nit (use adk-style), for diagnosing a failing test or a misbehaving agent at runtime (use adk-debug), or for wording a commit message or PR description (use adk-git).

2026-10-09

adk-style

Python style and codebase conventions for ADK (Agent Development Kit): private-by-default file visibility, imports, type hints, Pydantic v2 models, formatting, docstrings, logging, async I/O, file and test layout, and unit test structure. Use when writing or editing ADK source or tests, deciding whether a new file or symbol should be public or private, naming or placing a test file, fixing a formatter, linter, or type-check failure (pyink, isort, ruff, mypy, addlicense, compliance-checks), or asking whether code matches house style. Don't use for reviewing a whole changeset (use adk-review), writing a developer guide or design doc for a code unit (use adk-unit-guide or adk-unit-design), building or configuring agents (use adk-agent-builder), or installing the toolchain (use adk-setup).

2026-10-08

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bigquery-ai-ml

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