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 build a data dashboard, data application, or visualization UI, and the UI pulls data from a GCP database (defaulting to BigQuery unless otherwise specified). 2. You need to generate a frontend web application to interact with, query, and visualize data from GCP data sources. 3. User wants to build a "chat with your data" experience or integrate the Gemini Data Analytics chat API into a web interface. Do NOT use when any of the following conditions are true: 1. The request is for building backend-only services. 2. The request is for simple CLI scripts or command-line applications. 3. The web application is not data-centric or does not involve visualizing/querying data from GCP sources.
Install
With your agent
Install OMGSkills skill "googlecloudplatform/data-agent-kit-plugin:skills/building-data-apps" into this agent's normal skills directory: call the OMGSkills MCP get_install_plan tool for your agent and run the script it returns (it installs only the pinned commit and verifies SKILL.md), and stop if MCP or the pin is unavailable. Do not overwrite or change existing skills without asking me.
Manual command
git clone https://github.com/GoogleCloudPlatform/data-agent-kit-plugin /tmp/data-agent-kit-plugin && ln -s /tmp/data-agent-kit-plugin/skills/building-data-apps ~/.claude/skills/building-data-appsFrom README
Building Data Applications Architect high-quality data dashboards and interactive reports. You MUST select the appropriate framework before implementation. Step 0: Framework Selection You MUST select the framework based on the user's maintenance requirements and data ecosystem. Choice: Streamlit User Profile: Data Scientists / Python users. Logic Complexity: High Python dependency (Pandas, NumPy, local data processing). Deployment: Single-file Python script. Customization: Standard layout (fast boilerplate). Choice: React + Vite User Profile: Web Developers / Full-stack teams. Logic Complexity: High UI and Interactivity requirements (e.g., drag-and-drop, interactive maps). Deployment: Standalone Frontend + Backend API. Customization: Infinite (Custom CSS, specialized JS libraries).
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