Your entire data stack in your favorite agentic tool.
安装
npx skills add GoogleCloudPlatform/data-agent-kit-plugingh skill install GoogleCloudPlatform/data-agent-kit-plugingit clone --depth 1 https://github.com/GoogleCloudPlatform/data-agent-kit-plugin合集内的技能
- Accidental Data Loss PreventionGoogle Cloud Platform**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/VIEW/SCHEMA/DATABASE, TRUNCATE, or broad DELETE (missing WHERE or using 1=1).
- Bigquery Ai MlGoogle Cloud PlatformLeverages 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 Data Transfer ServiceGoogle Cloud PlatformDiscovers and inspects BigQuery Data Transfer Service (DTS) configurations. Use this to identify existing ingestion pipelines and extract datasource or transfer config metadata for data pipelines. Use when a user asks for ingestion scenarios while building or managing data pipelines or when a user asks to "ingest" or "add" data that may already be managed by a DTS transfer.
- Bigquery Graph AuthorGoogle Cloud PlatformProvides 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 best practices and semantic measures/dimensions, plus an opt-in verification suite for the landed…
- Bigquery Graph QueryGoogle Cloud PlatformProvides 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 relational SQL integration, and semantic graph querying via the GRAPH_EXPAND TVF with…
- Bigquery SQLGoogle Cloud PlatformProvides 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 AppsGoogle Cloud PlatformBuild 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.
- Dak SetupGoogle Cloud PlatformConfigures (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.
- Data AutocleaningGoogle Cloud PlatformAutomated data quality and transformation capabilities for Dataform/dbt/BigQuery pipelines. Processes data sourced from BigQuery or Cloud Storage (GCS), applying best practices for data ingestion, movement, schema mapping, and comprehensive data cleaning.
- Dataform BigqueryGoogle Cloud PlatformExpertise in generating clean, correct, and efficient Dataform pipeline code for BigQuery ELT. Use this when creating or modifying Dataform pipelines, actions, or source declarations, when Dataform, SQLX, or BigQuery are mentioned in a transformation, when data needs to be ingested from GCS into BigQuery via Dataform, or when setting up a new Dataform project or configuring workflow_settings.yaml.
- Dbt BigqueryGoogle Cloud PlatformExpert guidance for creating, modifying, and optimizing dbt pipelines for BigQuery. Use this skill whenever user asks for generating or modifying a dbt model or project. Activate this skill when the user - Creates, modifies, or troubleshoots **dbt models or pipelines** - Needs to **optimize SQL** within a dbt project - Is **setting up a new dbt project** or configuring existing one
- Discovering Gcp Data AssetsGoogle Cloud PlatformFinds 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, databases or tables - etc. 2.
- Enforcing Resource AttributionGoogle Cloud PlatformEnforces 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.
- Federate Lakehouse CatalogGoogle Cloud PlatformSets 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).
- Gcp Composer TroubleshootingGoogle Cloud PlatformProvides expert guidance for troubleshooting Cloud Composer (Apache Airflow) and Orchestration pipelines. Use this skill when the user asks to generate Root Cause Analysis (RCA), troubleshoot or fix a failed pipeline, DAG in Composer environment and generate RCA report.
- Gcp Data PipelinesGoogle Cloud PlatformPrimary entry point for building, managing, and orchestrating data pipelines on Google Cloud. Guides users to the appropriate skill for dbt, Dataflow (Apache Beam), Dataform, Spark (Dataproc Serverless), BigQuery Data Transfer Service (DTS) or orchestration pipeline using Cloud Composer. Clarify requirements and resolve ambiguity for creating, updating and running data pipelines.
- Gcp DataflowGoogle Cloud PlatformGuides 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 health and autoscaling. Use when: - Creating an Apache Beam Dataflow pipeline.
- Gcp Managed Airflow Dag AuthoringGoogle Cloud PlatformGuides 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 Airflow DAG. Don't use when authoring Python code unrelated to Airflow DAGs.
- Gcp Managed Airflow MigrationsGoogle Cloud PlatformProvides 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 changes.
- Gcp Managed Airflow RecommendationsGoogle Cloud PlatformProvides 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, preventing workload restarts, or analyzing system health.
- Gcp Managed Spark UpgradesGoogle Cloud PlatformUpgrades 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 version [target_version].
- Gcp Pipeline OrchestrationGoogle Cloud PlatformThis skill helps the agent generate or update orchestration pipeline definitions for Google Cloud Composer to initialize orchestration pipeline or update the orchestration definition for orchestration of various data pipelines, like dbt pipelines, notebooks, Spark jobs, Dataform, Python scripts or inline BigQuery SQL queries. This skill also helps deploy and trigger orchestration pipelines.
- Gcp Pipeline Resource ProvisioningGoogle Cloud PlatformAutomates 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 or creating deployment.yaml for deployment settings.
- Gcp SparkGoogle Cloud PlatformDevelops, optimizes and runs PySpark/Spark code on Managed Spark (Dataproc clusters and Serverless) on Google Cloud. Use when: - Authoring or running Spark/PySpark notebooks (any kernel), incl. data analysis, reports and visualizations. - Writing Spark ETL pipelines or preparing workloads for production. - Training or running inference with ML models on Spark.
- Gcp Spark TroubleshootingGoogle Cloud PlatformProvides 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.
- Gcs Security AssessmentGoogle Cloud PlatformAssesses 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 combinations of individually low-risk settings, with actionable remediation.
- Google Cloud Auth VerificationGoogle Cloud PlatformMandatory 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, BigQuery queries, GCS paths (gs://), or creating/running notebooks.
- Google Cloud Storage BasicsGoogle Cloud PlatformStores, retrieves, and manages data as objects in Cloud Storage (Google Cloud Storage, or GCS) buckets. Use when you need to interact with Cloud Storage — set up a Storage MCP server (remote or local Toolbox), create or configure buckets, upload, download, stream, or transfer data, organize objects with folders, generate signed URLs, control access (IAM, ACLs, public access prevention), set…
- Google Cloud Storage Bucket ArchitectGoogle Cloud PlatformCreates 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 configuration (location, storage class, uniform bucket-level access, public access prevention, soft…
- Google Cloud Storage FuseGoogle Cloud PlatformMounts 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, stat, and list caches, tune mount flags or config-file settings, apply workload profiles, keep…
- Managing Python DependenciesGoogle Cloud PlatformEnsures 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 new Python project. 4. Installing missing packages inside a notebook or Python project. 5.
- Ml Best PracticesGoogle Cloud PlatformCRITICAL 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 comparison * ML * Data analysis SQL/BigQuery ML HANDOFF: If the user requires a SQL solution, use this…
- Notebook GuidanceGoogle Cloud PlatformThis 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 installation, and structuring notebooks for clarity.
- Resolving MCP Region ConfigsGoogle Cloud PlatformMandatory 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.
- Schema MappingGoogle Cloud PlatformGuides 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 with schema mapping specification for multiple tables (i.e.