shine-dashboard-spec

Name: shine-dashboard-spec
Availability: InStock
Author: diShine

Produce complete dashboard specifications with KPIs, dimensions, and data sources.

1|Updated Apr 15, 2026

Search Tags:#specification #dashboard #audience #kpis #cadence #dimensions #data-sources

Ready to run

npx skills add https://github.com/diShine-digital-agency/SHINE-Code-System --skill shine-dashboard-spec

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: shine-dashboard-spec
Source: https://github.com/diShine-digital-agency/SHINE-Code-System/tree/main/skills/shine-dashboard-spec
Command: npx skills add https://github.com/diShine-digital-agency/SHINE-Code-System --skill shine-dashboard-spec

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

BI teams often struggle to translate vague prompts into concrete dashboard specifications. This skill helps translate business needs into a complete dashboard blueprint that guides design, data modeling, and tooling.

Core Features & Use Cases

Audience & use-case framing: Identify the intended audience and use case for the dashboard.
KPIs & metrics definitions: Specify leading/lagging indicators and precise metric definitions.
Dimensions, filters, and data sources: Define the data model, filtering options, and sources; show an example with hypothetical data lines.

Quick Start

Provide a complete dashboard specification for the target audience using a given business prompt.

Frequently Asked Questions about shine-dashboard-spec

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

FAQPage Schema

How do I write a complete BI dashboard specification from a business prompt?▼

To write a BI dashboard specification, translate a business prompt into a complete blueprint covering audience, decisions, KPIs, dimensions, filters, refresh cadence, data sources, and metric definitions. This ensures design and data modeling align with the intended objective.

What should be included in a dashboard specification for Tableau, Looker, or Metabase?▼

A dashboard specification for Tableau, Looker, or Metabase should include audience framing, leading and lagging KPIs, precise metric definitions, data sources, filtering options, refresh cadence, and known caveats. The output remains tool-agnostic to guide platform-specific implementation.

How do I define KPIs and metric definitions for a new dashboard?▼

Define KPIs and metric definitions by identifying leading and lagging indicators relevant to the use case, specifying exact calculation logic, and documenting known caveats. This ensures the dashboard specification provides consistent and accurate data modeling.

Does a dashboard specification need to be tool-specific or can it be platform agnostic?▼

A dashboard specification should be tool-agnostic to maintain consistency with the defined objective. While it applies to platforms like Looker, Metabase, or Tableau, the blueprint defines dimensions, filters, and data sources independently of specific BI tooling constraints.

Why do my dashboard designs lack clear dimensions and data sources?▼

Dashboard designs lack clear dimensions and data sources when the initial specification omits data modeling and filtering options. Defining these elements alongside metric definitions and refresh cadence translates vague prompts into a structured dashboard blueprint.

When should I document refresh cadence and known caveats in a dashboard blueprint?▼

Document refresh cadence and known caveats in a dashboard blueprint during the initial specification phase. Specifying data update frequencies and limitations alongside data sources ensures the final dashboard meets audience expectations and use-case requirements.