dak

Plugin dak from marketplace dak-marketplace in GoogleCloudPlatform/data-agent-kit-plugin. Commands read from the repo's manifest — today.

Skills (41)

• 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, predict values, detect outliers or anomalies, find key drivers, perform semantic search or vector search, classify text, cal

• 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-observability

  Provides data-retrieval best practices, tool selection guidance, and performant SQL query syntax for BigQuery telemetry across INFORMATION_SCHEMA, Cloud Monitoring, and the REST API. Use when the telemetry to fetch is already known, selecting telemetry tools, writing performant INFORMATION_SCHEMA qu

• bigquery-optimization

  Provides workflows to optimize BigQuery environments (capacity planning, editions), storage assets (partitioning, clustering, storage lifecycles, billing models), and SQL queries. Use when optimizing cost, modeling Edition migrations, rightsizing reservations, evaluating logical vs. physical storage

• bigquery-troubleshooting

  Provides diagnostic workflows and step-by-step root-cause analysis procedures for actively broken, failing, or slow BigQuery jobs, execution graph and query plan stage bottlenecks, system performance issues, or unexpectedly expensive workloads. Use when interpreting symptoms, isolating bottlenecks,

• bigtable-basics

  Provisions instances and tables, designs performant schemas, and queries 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 when provis

• 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

• cloud-databases-onboarding

  Guides users through discovering their database requirements, recommends a Google Cloud database product or migration destination based on a recommendation matrix, and assists in database creation. Use when a user asks 'What database service should I use?', 'Help me pick a database', asks which Goog

• curate-context

  Creates, imports, pulls, edits, repairs, or publishes a Knowledge Catalog (KC) context set as Open Knowledge Format (OKF v0.2) markdown under `bundle/` with `kcmd`, for BigQuery, Spanner, BigLake, Cloud SQL, AlloyDB, or Hive Metastore. Load FIRST whenever creating, importing, pulling, editing, repai

• 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

• discover-assets

  Finds and inspects data assets within Google Cloud. Use for any question that needs to discover data sources or find/inspect schemas, metadata, or domain rules for tables. Relevant when: (1) finding, exploring, or inspecting GCP data assets; (2) retrieving schemas, metadata, or governance policies f

• 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: v7 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 Spark workloads on Google Cloud Managed Service for Apache Spark (formerly known as Dataproc) clusters and Serverless to a newer image or runtime version. Supports static compatibility analysis, code upgrade, build checks against the target version, and optional verification runs. Use when:

• 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-observability

  Monitors, inventories, and assesses Google Cloud Managed Service for Apache Spark (Dataproc Serverless and Dataproc on Compute Engine) environments, clusters, batches, jobs, sessions, metrics, logs, costs, and configurations across single and multiple regions. Use when listing active or historical S

• gcp-spark-troubleshooting

  Provides expert guidance for troubleshooting Google Cloud Spark and Dataproc workloads (Dataproc Serverless batches, interactive sessions, and standard Dataproc clusters), and inspecting, streaming, searching, tailing, or summarizing Spark driver outputs and event logs in Cloud Storage. Use when deb

• gcp-spark

  Authors, optimizes, and runs PySpark and Spark code on Google Cloud Managed Spark (Dataproc Serverless, clusters, and Spark Connect). Use when: - Authoring or running Spark/PySpark notebooks in any kernel (Local Kernel with spark-remote templates, Remote Spark / Remote Kernel, ManagedSparkSession, S

• 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-storage-basics

  Stores, retrieves, and manages data as objects in Cloud Storage on Google Cloud (also known colloquially as 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,

• 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 (Composer dak-composer, Dataform dak-dataform, and Dataflow dak-dataflow) before using regional MCP tools or falling back to gcloud/bq CLI. Use whenever a task involves Cloud Composer / Managed Airflow environments, DAGs, or tas

• 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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