aibi-dashboards

Name: aibi-dashboards
Availability: InStock
Author: databricks-solutions

Create and deploy AI/BI dashboards in Databricks with SQL validation.

1.8k|404|Updated Dec 17, 2025

Search Tags:#dashboard #widgets #MCP tools #dashboard deployment #Databricks #SQL validation

Ready to run

npx skills add https://github.com/databricks-solutions/ai-dev-kit --skill aibi-dashboards

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: aibi-dashboards
Source: https://github.com/databricks-solutions/ai-dev-kit/tree/main/databricks-skills/aibi-dashboards
Command: npx skills add https://github.com/databricks-solutions/ai-dev-kit --skill aibi-dashboards

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Creating AI/BI dashboards in Databricks can be error-prone without validated SQL queries and a repeatable deployment process.

Core Features & Use Cases

Mandatory SQL validation: test all queries via execute_sql before deploying dashboards.
Guided deployment: assemble datasets and widgets following the prescribed architecture and MCP workflow.
Real-world scenario: design a regional revenue dashboard that updates after all queries pass verification.

Quick Start

Use this Skill to connect to your Databricks workspace, validate each SQL query with execute_sql, build the dashboard JSON using the MCP workflow, and deploy via create_or_update_dashboard after successful validation.

Frequently Asked Questions about aibi-dashboards

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I automate Databricks dashboard deployment with validated SQL?▼

Automate Databricks dashboard deployment by validating SQL queries via execute_sql, assembling widgets and datasets into dashboard JSON using MCP tools, and publishing with create_or_update_dashboard after verification.

Why should I validate SQL queries before deploying Databricks dashboards?▼

Validating SQL queries before deploying Databricks dashboards prevents runtime errors by testing all statements via execute_sql, ensuring datasets return expected results before dashboard widgets are assembled and published.

What is the MCP workflow for building AI dashboards in Databricks?▼

The MCP workflow for building AI dashboards in Databricks guides end-to-end assembly of datasets, widgets, and layouts into dashboard JSON, enforcing prescribed architecture before final deployment via create_or_update_dashboard.

Can I design and test Databricks dashboard layouts across multiple datasets?▼

Yes, you can design and test Databricks dashboards across multiple datasets by validating each SQL query independently, then assembling widgets and layouts into a unified dashboard JSON structure for deployment.

Does dashboard deployment in Databricks require SQL validation for every widget?▼

Yes, dashboard deployment in Databricks requires SQL validation for every widget dataset via execute_sql to ensure all queries execute successfully before the dashboard is published using MCP deployment tools.

What's the best way to prevent SQL errors when creating Databricks dashboards?▼

The best way to prevent SQL errors when creating Databricks dashboards is to enforce mandatory SQL validation through execute_sql on all queries before assembling widgets and deploying the final dashboard architecture.