data-agent-kit-starter-pack

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

Skills (18)

• 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-data-transfer-service

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

• 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

• developing-with-bigquery

  A repository of BigQuery-specific logic, knowledge, and specialized standards. Use this skill whenever you are doing anything with BigQuery, including: 1. BigQuery query optimization 2. BigFrames Python code 3. BigQuery ML/AI functions. version: v1 publisher: google

• 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

• 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-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

• 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 (11)

• 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

• datacloud_bigquery_toolbox

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

• datacloud_spanner_toolbox

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

• datacloud_alloydb-postgres-admin_toolbox

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

• datacloud_alloydb-postgres_toolbox

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

• datacloud_cloud-sql-postgresql-admin_toolbox

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

• datacloud_cloud-sql-postgresql_toolbox

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

• datacloud_knowledge_catalog_toolbox

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

• datacloud_dataproc_toolbox

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

• datacloud_serverless-spark_toolbox

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

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