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docs: rename repository to data-agent-kit-starter-pack and update codex installation commands
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‎.agents/plugins/marketplace.json‎

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{
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"name": "data-cloud-ai-dev-kit-marketplace",
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"name": "data-agent-kit-starter-pack-marketplace",
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"interface": {
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"displayName": "data-cloud-ai-dev-kit Marketplace"
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"displayName": "Data Agent Kit Starter Pack Marketplace"
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},
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"plugins": [
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{
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"name": "data-cloud-ai-dev-kit",
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"name": "data-agent-kit-starter-pack",
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"source": {
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"source": "local",
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"path": "./"

‎.claude-plugin/marketplace.json‎

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{
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"name": "data-cloud-ai-dev-kit-marketplace",
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"name": "data-agent-kit-starter-pack-marketplace",
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"owner": {
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"name": "Google LLC"
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},
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"plugins": [
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{
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"name": "data-cloud-ai-dev-kit",
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"name": "data-agent-kit-starter-pack",
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"source": "./",
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"description": "This plugin provides a specialized suite of skills for data engineers and database practitioners working on Google Cloud. It acts as an expert assistant, allowing you to use natural language prompts in your preferred coding agent to architect complex data pipelines, transform data with dbt, write Spark and BigQuery SQL notebooks, and orchestrate end-to-end workflows across GCP's data ecosystem."
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}

‎.claude-plugin/plugin.json‎

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{
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"name": "data-cloud-ai-dev-kit",
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"name": "data-agent-kit-starter-pack",
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"version": "0.1.8",
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"description": "This plugin provides a specialized suite of skills for data engineers and database practitioners working on Google Cloud. It acts as an expert assistant, allowing you to use natural language prompts in your preferred coding agent to architect complex data pipelines, transform data with dbt, write Spark and BigQuery SQL notebooks, and orchestrate end-to-end workflows across GCP's data ecosystem.",
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"mcp": "./.mcp.json"

‎.codex-plugin/plugin.json‎

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{
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"name": "data-cloud-ai-dev-kit",
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"name": "data-agent-kit-starter-pack",
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"version": "0.1.8",
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"description": "This plugin provides a specialized suite of skills for data engineers and database practitioners working on Google Cloud. It acts as an expert assistant, allowing you to use natural language prompts in your preferred coding agent to architect complex data pipelines, transform data with dbt, write Spark and BigQuery SQL notebooks, and orchestrate end-to-end workflows across GCP's data ecosystem.",
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"skills": "./skills/",
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"interface": {
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"displayName": "Data Cloud AI Dev Kit",
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"shortDescription": "Data Cloud AI Dev Kit",
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"displayName": "Data Agent Kit Starter Pack",
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"shortDescription": "Data Agent Kit Starter Pack",
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"category": "Productivity"
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},
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"mcp": "./.mcp.json"

‎.lycheeignore‎

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https://github.com/gemini-cli-extensions/data-cloud-ai-dev-kit/compare/
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https://github.com/gemini-cli-extensions/data-cloud-ai-dev-kit
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https://raw.githubusercontent.com/gemini-cli-extensions/data-cloud-ai-dev-kit/main/codex-install.sh
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https://docs.cloud.google.com/data-cloud-extension/vs-code/install
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data-cloud-ai-dev-kit@data-cloud-ai-dev-kit-marketplace

‎.mcp.json‎

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"command": "npx",
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"args": [
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"-y",
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"git+https://github.com/gemini-cli-extensions/data-cloud-ai-dev-kit.git"
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"git+https://github.com/gemini-cli-extensions/data-agent-kit-starter-pack.git"
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],
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"env": {},
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"env_vars": [
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"bigquery",
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"--stdio",
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"--user-agent-metadata",
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"google-cloud-data-agent-kit-claude-and-codex"
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"datacloud.claude-and-codex"
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],
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"env": {
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"BIGQUERY_LOCATION": "",
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"spanner",
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"--stdio",
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"--user-agent-metadata",
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"google-cloud-data-agent-kit-claude-and-codex"
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"datacloud.claude-and-codex"
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],
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"env": {
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"SPANNER_DATABASE": "",
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"alloydb-postgres-admin",
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"--stdio",
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"--user-agent-metadata",
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"google-cloud-data-agent-kit-claude-and-codex"
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"datacloud.claude-and-codex"
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],
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"env": {}
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},
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"alloydb-postgres",
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"--stdio",
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"--user-agent-metadata",
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"google-cloud-data-agent-kit-claude-and-codex"
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"datacloud.claude-and-codex"
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],
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"env": {
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"ALLOYDB_POSTGRES_CLUSTER": "",
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"cloud-sql-postgres-admin",
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"--stdio",
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"--user-agent-metadata",
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"google-cloud-data-agent-kit-claude-and-codex"
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"datacloud.claude-and-codex"
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],
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"env": {}
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},
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"cloud-sql-postgres",
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"--stdio",
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"--user-agent-metadata",
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"google-cloud-data-agent-kit-claude-and-codex"
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"datacloud.claude-and-codex"
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],
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"env": {
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"CLOUD_SQL_POSTGRES_DATABASE": "",
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"dataplex",
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"--stdio",
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"--user-agent-metadata",
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"google-cloud-data-agent-kit-claude-and-codex"
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"datacloud.claude-and-codex"
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],
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"env": {
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"DATAPLEX_PROJECT": ""
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"dataproc",
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"--stdio",
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"--user-agent-metadata",
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"google-cloud-data-agent-kit-claude-and-codex"
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"datacloud.claude-and-codex"
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],
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"env": {
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"DATAPROC_PROJECT": "",
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"serverless-spark",
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"--stdio",
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"--user-agent-metadata",
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"google-cloud-data-agent-kit-claude-and-codex"
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"datacloud.claude-and-codex"
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],
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"env": {
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"SERVERLESS_SPARK_PROJECT": "",

‎DEVELOPER.md‎

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# DEVELOPER.md
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This document provides instructions for setting up your development environment
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and contributing to the Cloud SQL for PostgreSQL Gemini CLI Extension project.
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and contributing to the Data Agent Kit Starter Pack project.
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## Prerequisites
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1. **Clone the Repository:**
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```bash
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git clone https://github.com/gemini-cli-extensions/data-cloud-ai-dev-kit.git
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cd data-cloud-ai-dev-kit
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git clone https://github.com/gemini-cli-extensions/data-agent-kit-starter-pack.git
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cd data-agent-kit-starter-pack
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```
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2. **Download the Toolbox Binary:** The required version of the `toolbox` binary
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The CLI will prompt you to confirm the installation. Accept it to proceed.
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4. **Testing Changes:** After installation, start the Gemini CLI (`gemini`).
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You can now interact with the `data-cloud-ai-dev-kit` tools to manually test your changes
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You can now interact with the `data-agent-kit-starter-pack` tools to manually test your changes
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against your connected database.
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### Adding or Updating Skills

‎README.md‎

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# Data Cloud AI Dev Kit
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# Data Agent Kit Starter Pack
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> [!NOTE]
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> This extension is currently in beta (pre-v1.0), and may see breaking changes until the first stable release (v1.0).
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This plugin provides a specialized suite of skills and MCP tools for data engineers and database practitioners working on Google Cloud. It acts as an expert assistant, allowing you to use natural language prompts in your preferred coding agent to architect complex data pipelines, transform data with dbt, write Spark and BigQuery SQL notebooks, and orchestrate end-to-end workflows across GCP's data ecosystem (BigQuery, Spanner, BigLake, Dataproc, etc.).
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This plugin provides a specialized suite of skills and MCP tools for data engineers and database practitioners working on Google Cloud. It acts as an expert assistant, allowing you to use natural language prompts in your preferred coding agent to architect complex data pipelines, transform data with dbt, write Spark and BigQuery SQL notebooks, and orchestrate end-to-end workflows across the Google Cloud data ecosystem (BigQuery, Spanner, BigLake, Dataproc, etc.).
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> [!IMPORTANT]
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> **We Want Your Feedback!**
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> Please share your thoughts with us by opening an issue on GitHub. Your input is invaluable and helps us improve the project for everyone.
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## Why Use the Data Cloud AI Dev Kit?
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## Why Use the Data Agent Kit Starter Pack?
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* **Seamless Workflow:** Brings Google Cloud data engineering expertise directly into your terminal or IDE via Gemini CLI, Claude Code, or Codex.
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* **End-to-End Data Pipelines:** Effortlessly generate code that reads raw data from GCS, processes it with Spark or BigQuery, transforms it through medallion architectures (bronze, silver, gold) using dbt, and exports it to serving layers like Cloud Spanner.
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* **Ecosystem Integration:** Work across boundaries—generate BigLake Iceberg catalog tables, train BigQuery ML models (XGBoost, KMEANS), and create interactive Streamlit dashboards or LookML models all from natural language.
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* **Seamless Workflow:** Bring Google Cloud data engineering expertise directly into your terminal or IDE via Gemini CLI, Claude Code, or Codex.
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* **End-to-End Data Pipelines:** Effortlessly generate code that reads raw data from Cloud Storage, processes it with Spark or BigQuery, transform it through medallion architectures (bronze, silver, gold) using dbt, and export it to serving layers like Spanner.
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* **Ecosystem Integration:** Work across boundaries—generate BigLake Iceberg catalog tables, train BigQuery ML models (XGBoost, KMEANS), and create interactive Streamlit dashboards or LookML models, all from natural language.
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* **Workflow Orchestration:** Automatically create and schedule orchestration pipelines that tie your notebooks and dbt models together into robust, scheduled jobs.
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## Prerequisites
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* [Gemini CLI](https://github.com/google-gemini/gemini-cli) (v0.6.0+)
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* [Claude Code](https://docs.anthropic.com/en/docs/agents-and-tools/claude-code/overview)
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* Codex CLI
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* **(Optional) IDE Extension:** [Google Cloud Data Agent Kit](https://docs.cloud.google.com/data-cloud-extension/vs-code/install).
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## Getting Started
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### Installation
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Choose the installation method for your preferred coding agent.
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Choose the installation method for your preferred coding agent. Run the commands in terminal
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<details>
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<summary><b>Gemini CLI and Gemini Code Assist</b></summary>
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#### Gemini CLI
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```bash
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gemini extensions install https://github.com/gemini-cli-extensions/data-cloud-ai-dev-kit
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gemini extensions install https://github.com/gemini-cli-extensions/data-agent-kit-starter-pack
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```
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</details>
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<details>
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<summary><b>Claude Code</b></summary>
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Run the `claude` command to start the agent, then run:
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#### Claude Code
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```bash
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/plugin marketplace add https://github.com/gemini-cli-extensions/data-cloud-ai-dev-kit
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/plugin install data-cloud-ai-dev-kit@data-cloud-ai-dev-kit-marketplace
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/plugin marketplace add https://github.com/gemini-cli-extensions/data-agent-kit-starter-pack
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/plugin install data-agent-kit-starter-pack@data-agent-kit-starter-pack-marketplace
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```
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</details>
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<details>
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<summary><b>Codex</b></summary>
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Run the following commands in your terminal:
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#### Codex
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**macOS / Linux:**
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```bash
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gh api repos/gemini-cli-extensions/data-cloud-ai-dev-kit/contents/codex-install.sh -H "Accept: application/vnd.github.raw" | bash
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curl -sSL https://raw.githubusercontent.com/gemini-cli-extensions/data-agent-kit-starter-pack/main/codex-install.sh | bash
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```
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**Windows:**
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```powershell
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gh api repos/gemini-cli-extensions/data-cloud-extension/contents/codex-install.ps1 -H "Accept: application/vnd.github.raw" | powershell -ExecutionPolicy Bypass -
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irm https://raw.githubusercontent.com/gemini-cli-extensions/data-agent-kit-starter-pack/main/codex-install.ps1 | iex
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```
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After running the installation script, run the `codex` command to start the agent, then run:
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```bash
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/plugins
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```
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Use the interactive options to install the extension with the name `Data Agent Kit Starter Pack`.
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</details>
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### Configuration
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MCP toolboxes are added to the respective agent configuration files. You must configure the MCP toolboxes in your agent's configuration files for them to start successfully.
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In all cases, you must restart the agent after updating the configuration files.
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To verify your configuration:
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* Run the `/mcp` command to check the status of available MCP servers.
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* Ask your agent "What skills are installed?" to view the list of active skills.
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<details>
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<summary><b>Gemini CLI and Gemini Code Assist</b></summary>
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Edit the configuration file:
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`~/.gemini/extensions/data-agent-kit-starter-pack/gemini-extension.json`
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</details>
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<details>
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<summary><b>Claude Code</b></summary>
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1. Edit the configuration file:
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`~/.claude/plugins/cache/data-agent-kit-starter-pack-marketplace/data-agent-kit-starter-pack/<version>/.mcp.json`
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2. Reinstall the plugin:
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Run `/plugin` and use interactive options to uninstall `data-agent-kit-starter-pack`. Then run `/plugin install` to add it back.
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</details>
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<details>
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<summary><b>Codex</b></summary>
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1. Edit the configuration file:
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`~/.agents/plugins/data-agent-kit-starter-pack/.mcp.json`
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2. Use interactive options to uninstall and install the extension:
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```bash
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/plugins
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```
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Install with name: `Data Agent Kit Starter Pack`
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</details>
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## Usage Examples
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## Security Reminder: Agent Environment Hardening
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Your agent has the power to
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execute tools and commands on your behalf. Protect your GCP resources by
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enforcing **Strict Least Privilege** across all CLIs, MCP servers and other
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resources available to your agents. For example, use scoped Service Accounts
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(read more
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[here](https://docs.cloud.google.com/docs/authentication/use-service-account-impersonation))
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for tasks accessing your cloud resources and conducting regular permission and
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agent settings audits to minimize your attack surface.
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Your agent has the power to execute tools and commands on your behalf. Protect your GCP resources by enforcing **Strict Least Privilege** across all CLIs, MCP servers and other resources available to your agents.
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* Use [service accounts](https://docs.cloud.google.com/docs/authentication/use-service-account-impersonation) for accessing your cloud resources.
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* Assign the service account a role with [limited permissions](https://docs.cloud.google.com/iam/docs/roles-overview).
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* Prevent unwanted cross-org agent access by utilizing **Principal Access Boundaries** to scope your agent to [projects](https://docs.cloud.google.com/iam/docs/principal-access-boundary-policies#use-case-one-project) you intend the agent to access.
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> [!NOTE]
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> The Principal Access Boundary condition should bind the policy to the service accounts you intend to restrict.

‎codex-install.ps1‎

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$ErrorActionPreference = "Stop"
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$pluginName = "data-cloud-ai-dev-kit"
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$repoUrl = "https://github.com/gemini-cli-extensions/data-cloud-ai-dev-kit"
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$pluginName = "data-agent-kit-starter-pack"
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$repoUrl = "https://github.com/gemini-cli-extensions/data-agent-kit-starter-pack"
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$pluginsRoot = Join-Path $HOME ".agents\plugins"
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$installDir = Join-Path $pluginsRoot $pluginName
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$marketplaceFile = Join-Path $pluginsRoot "marketplace.json"
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{
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"name": "$pluginName",
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"interface": {
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"displayName": "Google Data Cloud AI Dev Kit"
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"displayName": "Data Agent Kit Starter Pack"
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},
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"source": {
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"source": "local",

‎codex-install.sh‎

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set -e
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PLUGIN_NAME="data-cloud-ai-dev-kit"
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REPO_URL="https://github.com/gemini-cli-extensions/data-cloud-ai-dev-kit"
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PLUGIN_NAME="data-agent-kit-starter-pack"
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REPO_URL="https://github.com/gemini-cli-extensions/data-agent-kit-starter-pack"
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INSTALL_DIR="$HOME/.agents/plugins/$PLUGIN_NAME"
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MARKETPLACE_FILE="$HOME/.agents/plugins/marketplace.json"
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data.plugins = data.plugins.filter(p => p.name !== '${PLUGIN_NAME}');
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data.plugins.push({
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name: '${PLUGIN_NAME}',
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interface: { displayName: 'Google Data Cloud AI Dev Kit' },
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interface: { displayName: 'Data Agent Kit Starter Pack' },
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source: { source: 'local', path: './.agents/plugins/${PLUGIN_NAME}' },
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policy: { installation: 'AVAILABLE', authentication: 'ON_INSTALL' },
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category: 'Productivity'

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