data-agent-kit-plugin

Plugin data-agent-kit-plugin from marketplace data-cloud-plugins in GoogleCloudPlatform/data-cloud-plugins. Commands read from the repo's manifest — 3 days ago.

Skills (37)

• 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). Use by default for any Python data task involving BigQuery, including data processing, analysis, and machine learning. Don't use for SQL-first workflows or the google-cloud-bigquery client library — use bigquery-basics.

• bigquery-data-transfer-service

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

• bigquery-graph-author

  Provides an end-to-end journey for authoring a BigQuery property graph from your tables or from a model document/diagram (an ER diagram, an ontology, a semantic model): dataset discovery, candidate relationships verified against the data, a readable plan, and `CREATE PROPERTY GRAPH` DDL with schema

• bigquery-graph-query

  Provides guidelines and best practices for querying BigQuery property graphs and semantic graphs: native GQL (Graph Query Language) pattern matching, multi-hop and variable-length paths, aggregation, top-N, path extraction, output formatting (graph visualization with TO_JSON vs tabular), GRAPH_TABLE

• 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.

• bigtable-basics

  Assists in provisioning instances/tables, designing performant schemas, and querying data in Bigtable. Use when designing Bigtable row keys, configuring column families, writing SQL queries or client library code (Java, Go, Python) for Bigtable, or diagnosing performance/hotspotting issues. Also use

• 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

• dak-setup

  Configures (or reconfigures) the Google Cloud Data Agent Kit (DAK) plugin by checking gcloud and ADC credentials, collecting the GCP project ID, region, and services, and running its bundled `dak-setup` script. Use this proactively and without waiting to be asked whenever a SessionStart hook or any

• 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: v6 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

• 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-dag-authoring

  Guides the authoring and validation of Apache Airflow DAGs for Managed Service for Apache Airflow (MSAA; formerly Cloud Composer). Covers environment context discovery, Airflow 2 vs 3 compatibility, authoring best practices, and local/remote validation processes. Use when creating or extending an Ai

• 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-managed-airflow-recommendations

  Provides recommendations and best practices for creating, configuring, tuning and optimizing Managed Service for Apache Airflow (MSAA, Cloud Composer) environments. Use when the user asks for guidance, recommendations, or best practices on configuring Cloud Composer, scaling Airflow environments, pr

• gcp-managed-spark-upgrades

  Upgrades GCP Spark/Dataproc jobs to newer versions by analyzing, remediating, and testing job on target image version. Use when: - Upgrading Spark/Dataproc versions (major, minor, or sub-minor). Example prompt: "Upgrade Dataproc job [job_id] in project [project_id], region [region] to target image v

• 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-troubleshooting

  Provides expert guidance for troubleshooting Google Cloud Spark and Dataproc workloads (Dataproc Serverless batches and standard Dataproc clusters), and inspecting, streaming, searching, tailing, or summarizing Spark driver outputs and event logs in Cloud Storage. Use when the user asks to debug, tr

• gcp-spark

  Develops, optimizes and executes Spark code on Managed Spark on Google Cloud (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 Google Cloud Platform. - Optimizing PySpa

• gcs-security-assessment

  Assesses the security posture of Google Cloud Storage (GCS) buckets and projects. Grounds every finding in gathered telemetry, evaluates buckets against Google security best practices (public access, IAM over-granting, CMEK, VPC Service Controls, audit logging), and correlates signals to flag toxic

• google-cloud-auth-verification

  Mandatory Step 0 pre-flight execution order and authentication verification for Google Cloud Platform (GCP), Application Default Credentials (ADC), gcloud CLI, Spark, Dataproc, BigQuery, GCS, and notebook runtimes. Use whenever interacting with GCP resources, running Spark/PySpark pipelines, BigQuer

• 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

• google-cloud-storage-bucket-architect

  Creates Cloud Storage (Google Cloud Storage, or GCS) buckets. Analyzes the workload (sensitive data, media hosting, ingestion, web hosting, archiving, backup, logging, analytics, AI/ML, or general-purpose), validates project-level security settings, and designs a secure-by-default, cost-effective co

• google-cloud-storage-fuse

  Mounts Cloud Storage buckets as a POSIX file system with Cloud Storage FUSE (gcsfuse). Use when you need to interact with gcsfuse — decide whether FUSE, native gs:// reads, or Filestore/Managed Lustre fits a workload, deploy tuned mounts on GKE, Compute Engine, or Cloud Run, enable and size the file

• 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

• resolving-mcp-region-configs

  Mandatory Step 0 pre-flight check for regional Google Cloud MCP servers (e.g. Dataproc) before using MCP tools or falling back to gcloud/bq CLI. Use whenever interacting with Dataproc clusters, Spark batches, jobs, sessions, or regional GCP resources. Fixes regional MCP servers whose endpoint URL st

• schema-mapping

  Guides the process of analyzing, mapping, and documenting transformations between source and target schemas for any database, data warehouse, or data platform. Focuses exclusively on creating a high-fidelity mapping plan (Mapping Manifesto). Used when initiating an ETL, ELT, or data integration task

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