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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Repositório: google/adk-python
Última atividade na origem: 8 de outubro de 2026 às 16:56
Idioma detectado do SKILL.md: inglês
Estrelas: 21.870
Forks: 4.182

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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.

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SKILL.md

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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-setup

Sets up a local ADK Python development environment in a git clone of the open-source adk-python repository: a uv virtual environment, all dependency extras, pre-commit hooks, and a first unit-test run. Runs only when explicitly requested, never on its own. Use when asked to set up, bootstrap, or repair a development checkout, install project dependencies, fix a missing or broken .venv, or prepare a machine for contributing a pull request. Don't use for debugging a running agent (use adk-debug), for commit and pull-request mechanics (use adk-git), or for re-running formatters on a checkout that is already set up (pre-commit run --all-files).

2026-10-12

adk-agent-builder

Builds ADK (Agent Development Kit) Python agents: LLM agents with tools, graph workflows of function and agent nodes, conditional routing, fan-out and join, schema-validated delegation between agents, human-in-the-loop pauses, and pytest coverage for all of it. Use when asked to create an agent or a workflow, add a tool to one, branch or loop between nodes, run steps in parallel, pause for user approval, or test an agent. Don't use for explaining how ADK works internally or designing its core components (use `adk-architecture`), for an agent that already runs but misbehaves (use `adk-debug`), for authoring a sample under `contributing/` (use `adk-sample-creator`), or for naming, typing, and formatting conventions (use `adk-style`).

2026-10-12

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

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bigquery-ai-ml

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