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aibi-dashboard-guardrails

Validate Databricks AI/BI dashboard changes against YAML, schemas, and user requirements.

Updated May 20, 2026
Search Tags:#semantic layer#databricks#data quality checks#kpi validation#pre-publish review#dashboard governance#prompt vs yaml
One-click install
npx skills add https://github.com/ice-droid-99/BI-Dashboard-Generation-Agent-in-Databricks- --skill aibi-dashboard-guardrails
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: aibi-dashboard-guardrails
Source: https://github.com/ice-droid-99/BI-Dashboard-Generation-Agent-in-Databricks-/tree/main/.gemini/skills/aibi-dashboard-guardrails
Command: npx skills add https://github.com/ice-droid-99/BI-Dashboard-Generation-Agent-in-Databricks- --skill aibi-dashboard-guardrails

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

It prevents repeated, high-impact mistakes when generating or updating Databricks AI/BI dashboards from semantic YAML, schemas, and user requirements.

Core Features & Use Cases

  • Prompt-vs-YAML conflict enforcement: Treat the user prompt as the source of truth and explicitly flag any contradictions before changes are made.
  • KPI and SQL validation: Require KPI formulas to be validated with explicit SQL prior to publishing updates.
  • Display vs raw dataset correctness: Ensure presentation-ready (formatted/rounded) tables are only used when the user expects display-shaped output, not raw calculation datasets.
  • Ambiguity handling: Clarify ambiguous measure names (gross/net, booked/realized, filtered/raw, period/lifetime) so the dashboard logic matches intended business meaning.
  • Cross-check and pre-publish discipline: When users provide expected values, cross-check rendered results before publishing; otherwise run a structured pre-publish checklist.

Quick Start

Use aibi-dashboard-guardrails to verify that your proposed dashboard changes (KPI formulas, formatting, and datasets) align with the prompt, validate with SQL, and pass any user-provided expected-value cross-checks.

Frequently Asked Questions about aibi-dashboard-guardrails

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

FAQPage Schema
How do I validate Databricks AI/BI dashboard KPI formulas against semantic YAML definitions?▼

Dashboard governance requires treating the user prompt as the source of truth and explicitly flagging any prompt-vs-YAML contradictions before making changes. This prevents repeated mistakes when generating or updating Databricks AI/BI dashboards.

What is the correct way to separate raw and display-shaped datasets in Databricks dashboards?▼

Yes, a pre-publish review workflow enforces KPI formula validation, prompt-vs-YAML conflict disclosure, and business-value cross-checks. Databricks AI/BI dashboards only publish after passing these structured pre-publish checklist steps.

How do I resolve ambiguous measure names like gross versus net in semantic layer YAML?▼

Resolving ambiguous measure names in semantic layer YAML requires clarifying definitions like gross/net, booked/realized, and filtered/raw before publishing. This ensures dashboard logic accurately matches the intended business meaning.

What happens when user prompt requirements contradict the semantic YAML schema for a dashboard?▼

When user prompt requirements contradict the semantic YAML schema, the conflict is explicitly flagged before any changes are made. The user prompt is treated as the source of truth to prevent high-impact mistakes during dashboard generation.

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