data-agent-kit-starter-pack

Plugin data-agent-kit-starter-pack from marketplace google-plugins in artanphn-dotcom/google-skills. Commands read from the repo's manifest — 1 month ago.

Skills (26)

• accidental-data-loss-prevention

  **STOP AND VERIFY**: Before running any command or tool that results in irreversible data loss, you MUST obtain explicit user consent. When in doubt, ask. It is better to wait for confirmation than to accidentally delete production data or critical project assets. Use this for: - SQL: DROP TABLE/VIE

• 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, detect outliers, find key drivers, or leverage generative AI capabilities in BigQuery.

• bigquery-bigframes

  Generates Python code using BigQuery DataFrames (BigFrames), the pandas/scikit-learn-style API over BigQuery. Use when writing BigFrames code or doing pandas-style dataframe/ML work against BigQuery (e.g. in a notebook). Don't use for SQL-first workflows or the google-cloud-bigquery client library —

• bigquery-data-transfer-service

  Discovers and inspects BigQuery Data Transfer Service (DTS) configurations.

• bigquery-graph

  Provides guidelines and best practices for querying and defining property graphs and semantic graphs in BigQuery using GQL (Graph Query Language). Use when creating property graphs or querying graph topologies in BigQuery.

• bigquery-sql

  Provides BigQuery SQL query optimization techniques, execution best practices, and performance tuning rules for high-efficiency querying. Use when optimizing BigQuery SQL queries, reducing query costs, or designing performant SQL transformations.

• building-data-apps

  Build modern data apps, dashboards, and interactive reports using either React + Vite or Streamlit. Includes optional Gemini Data Analytics chat integration for an AI powered "chat with your data" experience. Relevant when any of the following conditions are true: 1. User explicitly requests to buil

• data-autocleaning

  Automated data quality and transformation capabilities for Dataform/dbt/BigQuery

• dataform-bigquery

  Expertise in generating clean, correct, and efficient Dataform pipeline

• dbt-bigquery

  Expert guidance for creating, modifying, and optimizing dbt pipelines

• discovering-gcp-data-assets

  Finds and inspects data assets within Google Cloud. Relevant when any of the following conditions are true: 1. The user request involves finding, exploring, or inspecting data assets in Google Cloud, such as: - BigQuery datasets, tables, or views - BigLake catalog or tables - Spanner instances, data

• enforcing-resource-attribution

  Enforces resource attribution for CLI commands. Use this skill whenever you are running `bq` or `gcloud` commands via `run_command`. It ensures mandatory labeling for supported `bq` operations while avoiding invalid flags on read-only commands. version: v4 publisher: google

• federate-lakehouse-catalog

  Sets up Google Cloud Lakehouse federated catalogs to remote Iceberg REST Catalogs. Currently supported catalogs: Databricks Unity, AWS Glue. Supported clouds hosting those catalogs: GCP, AWS. The primary use case is connecting to remote data to query it from GCP engines (BigQuery, Spark). Examples o

• gcloud-auth-verification

  Guidelines for identifying and resolving missing Google Cloud authentication

• gcp-composer-troubleshooting

  Provides expert guidance for troubleshooting Cloud Composer (Apache

• gcp-data-pipelines

  Primary entry point for building, managing, and orchestrating data pipelines

• gcp-dataflow

  Guides writing, packaging, executing, and troubleshooting Apache Beam pipelines on Dataflow. Use when creating new pipelines, configuring Flex Templates, or analyzing performance of Dataflow jobs. Capabilities include Java/Python/Go setup, Cloud Build integration, and deep diagnostic analysis of job

• gcp-managed-airflow-migrations

  Provides guidance for migrating Apache Airflow DAGs in Managed Service for Apache Airflow (MSAA; formerly Cloud Composer). Covers migration to Airflow 2.11.1 (MSAA Gen 2 and 3) and Airflow 3 (MSAA Gen 3), including environment inspection, GCS download/upload and scanning patterns for breaking change

• gcp-pipeline-orchestration

  This skill helps the agent generate or update orchestration pipeline

• gcp-pipeline-resource-provisioning

  Automates declarative resource creation and provisioning for data pipelines, supporting BigQuery, Dataform, Dataproc, BigQuery Data Transfer Service (DTS), and other resources. It manages environment-specific configurations (dev, staging, prod) through a deployment.yaml file. Use when: - Modifying o

• gcp-spark

  Develops and executes Spark code on Dataproc Clusters and Serverless. Reads and writes data using BigLake Iceberg catalogs, BigQuery and Spanner. Debugs execution failures. Use when: - Writing Spark ETL pipelines on GCP. - Training or running inference with ML models with spark on GCP. - Managing Sp

• gcs-security-assessment

  Assesses security posture, evaluates risks, and checks SAIF compliance for Google Cloud Storage buckets or projects. Use when the user requests security scans, vulnerability checks, or SAIF assessments. Don't use when: The user is asking about non-GCS resources (Compute Engine, GKE, etc.), investiga

• google-cloud-storage-basics

  Stores, retrieves, and manages data as objects in Cloud Storage (Google Cloud Storage, or GCS) buckets. Use when you need to interact with Cloud Storage — create or configure buckets, upload, download, stream, or transfer data, organize objects with folders, generate signed URLs, control access (IAM

• managing-python-dependencies

  Ensures proper Python dependency management, avoiding global `pip install` and adhering to project-specific tooling. Use this skill if any of the following are true: 1. Attempting to run `pip install {package_name}`. 2. Python packages or dependencies need to be added or modified. 3. Initiating a ne

• ml-best-practices

  CRITICAL RULE: You MUST use this skill whenever the task involves any machine learning tasks or data analysis. Use this skill if the user's prompt or requirements mention any of the following: * Clustering * Classification * Regression * Time series forecasting * Statistical testing * Model comparis

• notebook-guidance

  This skill guides the use of Jupyter notebooks for data analysis, exploration, and visualization, particularly with BigQuery. It outlines best practices for notebook execution and validation (supporting both cell-by-cell execution and full notebook generation depending on tool availability), library

MCP servers (12)

• notebook

  runs: npx -y git+https://github.com/gemini-cli-extensions/data-agent-kit-starter-pack.git --mode=notebook

• visualization

  runs: npx -y git+https://github.com/gemini-cli-extensions/data-agent-kit-starter-pack.git --mode=visualization

• bigquery

  runs: npx -y @toolbox-sdk/server@>=1.1.0 --prebuilt bigquery --stdio --user-agent-metadata datacloud.codex

• spanner

  runs: npx -y @toolbox-sdk/server@>=1.1.0 --prebuilt spanner --stdio --user-agent-metadata datacloud.codex

• alloydb-postgres-admin

  runs: npx -y @toolbox-sdk/server@>=1.1.0 --prebuilt alloydb-postgres-admin --stdio --user-agent-metadata datacloud.codex

• alloydb-postgres

  runs: npx -y @toolbox-sdk/server@>=1.1.0 --prebuilt alloydb-postgres --stdio --user-agent-metadata datacloud.codex

• cloud-sql-postgresql-admin

  runs: npx -y @toolbox-sdk/server@>=1.1.0 --prebuilt cloud-sql-postgres-admin --stdio --user-agent-metadata datacloud.codex

• cloud-sql-postgresql

  runs: npx -y @toolbox-sdk/server@>=1.1.0 --prebuilt cloud-sql-postgres --stdio --user-agent-metadata datacloud.codex

• knowledge_catalog

  runs: npx -y @toolbox-sdk/server@>=1.1.0 --prebuilt dataplex --stdio --user-agent-metadata datacloud.codex

• dataproc

  runs: npx -y @toolbox-sdk/server@>=1.1.0 --prebuilt dataproc --stdio --user-agent-metadata datacloud.codex

• serverless-spark

  runs: npx -y @toolbox-sdk/server@>=1.1.0 --prebuilt serverless-spark --stdio --user-agent-metadata datacloud.codex

• bigtable

  runs: npx -y @toolbox-sdk/server@>=1.1.0 --prebuilt bigtable --stdio --user-agent-metadata datacloud.codex

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