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ThomazRossito
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databricks-aibi-dashboards

Build, validate, and deploy Databricks AI/BI dashboards with SQL checks.

31|18|Updated Apr 4, 2026
Search Tags:#validation#mcp#dashboard#sql#databricks#lakeview
One-click install
npx skills add https://github.com/ThomazRossito/data-agents --skill databricks-aibi-dashboards-thomazrossito
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: databricks-aibi-dashboards
Source: https://github.com/ThomazRossito/data-agents/tree/main/skills/databricks/databricks-aibi-dashboards
Command: npx skills add https://github.com/ThomazRossito/data-agents --skill databricks-aibi-dashboards-thomazrossito

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Create and deploy reliable Databricks AI/BI dashboards (Lakeview) by enforcing a strict, test-driven validation workflow that prevents broken dashboards.

Core Features & Use Cases

  • Enforces a mandatory validation workflow: discover table schemas, craft SQL queries for each dataset, run every query through execute_sql, and build dashboard JSON for deployment.
  • Supports creation, updates, and deployment of Lakeview dashboards across Databricks and Fabric contexts, with guardrails to ensure widget-field alignment and dataset integrity.
  • Use Case: a data team rapidly builds a new dashboard, validates all data queries, and deploys with confidence using manage_dashboard.

Quick Start

Follow the mandatory validation workflow: fetch schemas, write and test queries with execute_sql, then deploy the dashboard via manage_dashboard.

Frequently Asked Questions about databricks-aibi-dashboards

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

FAQPage Schema
How do I build Databricks Lakeview dashboards without broken widgets?▼

To build Databricks Lakeview dashboards safely, you must execute a strict validation workflow: discover table schemas, test per-dataset SQL queries via execute_sql, and match query fields to widget encodings before deploying with manage_dashboard.

What is the mandatory validation workflow for Databricks AI/BI dashboards?▼

The mandatory validation workflow for Databricks AI/BI dashboards requires discovering table schemas, crafting per-dataset SQL queries, running every query through execute_sql to test execution, and then generating dashboard JSON for deployment via manage_dashboard.

How do I validate SQL queries before deploying a Databricks dashboard?▼

You validate SQL queries for Databricks dashboard deployment by running each per-dataset query through the execute_sql function to ensure execution success and dataset integrity before generating the final dashboard JSON.

Do I need to use fully-qualified table references in Databricks AI/BI dashboards?▼

Yes, you must use fully-qualified table references when building Databricks AI/BI dashboards. The dataset-architecture guidelines require fully-qualified references and strict matching between query fields and widget encodings to prevent broken dashboards.

Can I update and deploy existing Lakeview dashboards using manage_dashboard?▼

Yes, you can update and deploy existing Lakeview dashboards across Databricks and Fabric contexts using manage_dashboard, provided you enforce the mandatory validation workflow and maintain widget-field alignment for dataset integrity.

Why does my Databricks dashboard deployment fail with mismatched widget fields?▼

Databricks dashboard deployments fail when widget fields are mismatched because the strict validation workflow requires aligning query fields with widget encodings and testing dataset execution via execute_sql before deploying with manage_dashboard.

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