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building-data-apps

Skill

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

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Install

git clone https://github.com/gemini-cli-extensions/data-agent-kit-starter-pack.git ~/.claude/skills/building-data-apps

What is 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.

What this can do

Capabilities declared in this component's own frontmatter — not inferred.

Inherit all session tools

Declares no tool restrictions — inherits every session tool

~247 tokens of context used while enabled, before you invoke anything

Documentation

README · ~3 min read

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).

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