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databricks-aibi-dashboards

Generate Databricks AI/BI dashboard JSON with validated SQL queries.

Updated May 20, 2026
Search Tags:#databricks#lakeview#sql validation#dashboard json#aibi dashboards#widget specification#global filters
One-click install
npx skills add https://github.com/ice-droid-99/BI-Dashboard-Generation-Agent-in-Databricks- --skill databricks-aibi-dashboards-ice-droid-99
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: databricks-aibi-dashboards
Source: https://github.com/ice-droid-99/BI-Dashboard-Generation-Agent-in-Databricks-/tree/main/.gemini/skills/databricks-aibi-dashboards
Command: npx skills add https://github.com/ice-droid-99/BI-Dashboard-Generation-Agent-in-Databricks- --skill databricks-aibi-dashboards-ice-droid-99

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill removes the trial-and-error of building Databricks AI/BI (Lakeview) dashboards by enforcing a validation-first workflow, strict dataset-to-widget contracts, and repeatable dashboard JSON structure.

Core Features & Use Cases

  • End-to-end dashboard creation: guides assembling datasets, pages, and widgets into a deployable Lakeview JSON payload.
  • Contract-driven widget correctness: enforces exact fieldName alignment between dataset query outputs and widget encodings to prevent empty/invalid widgets.
  • Mandatory query validation workflow: requires running every SQL query via execute_sql before deploying with manage_dashboard.
  • Production-ready dashboard patterns: provides conventions for widget types, versions, layout grid rules, and filter wiring (global vs page-level).

Quick Start

Ask the AI to generate a Databricks AI/BI dashboard for your use case, including the dataset SQL queries and the full dashboard JSON, then run every query through execute_sql before calling manage_dashboard with create_or_update.

Frequently Asked Questions about databricks-aibi-dashboards

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

FAQPage Schema
How do I generate a Databricks AI/BI dashboard with deployable JSON?▼

To generate a Databricks AI/BI dashboard, assemble dataset SQL queries, widget specifications, and page layouts into a deployable Lakeview JSON payload. This process enforces strict dataset-to-widget field name matching and GRID_V1 layout rules for valid visualization workflows.

Why do my Databricks Lakeview widgets show up empty or invalid?▼

Empty or invalid Databricks Lakeview widgets often result from mismatched field names between dataset SQL query outputs and widget encodings. Enforcing exact fieldName alignment ensures the dashboard correctly maps dataset columns to chart, counter, and table widget specifications.

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

Validate SQL queries for Databricks AI/BI dashboards by running every dataset query through execute_sql before deployment. This mandatory validation workflow tests query correctness before calling manage_dashboard with create_or_update to finalize the dashboard.

Can I configure global and page-level filters for Lakeview dashboards?▼

Yes, you can configure global and page-level filters for Lakeview dashboards. The dashboard JSON specification supports wiring filters across various widget types, including charts, counters, tables, and text blocks, applying constraints across single or multiple dashboard pages.

What is the GRID_V1 layout rule for Databricks AI/BI dashboards?▼

The GRID_V1 layout rule defines the structural grid placement for widgets within Databricks AI/BI dashboards. Adhering to these layout rules ensures that generated widget specifications, including charts and tables, render correctly in the deployed Lakeview dashboard JSON.

Do I need to manually test SQL before creating a Databricks AI/BI dashboard?▼

Yes, manual SQL testing is required before creating a Databricks AI/BI dashboard. The workflow mandates running every SQL query via execute_sql to verify dataset outputs, preventing deployment failures and ensuring strict contract-driven widget correctness.

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