dashboard-designer

这段内容是审核时保存的快照。外部来源才是完整且最新的版本。

---
name: dashboard-designer
description: "Use this skill when designing a data dashboard—choosing KPIs, structuring layout, applying visual hierarchy, or deciding which BI tool to use. Trigger phrases: 'design a dashboard', 'build a KPI dashboard', 'what should my dashboard show', 'help me layout a dashboard', 'dashboard for monitoring'. Not for building chart code from scratch (use chart-builder), writing SQL queries (use sql-analyst), or designing marketing landing pages."
version: 1.0.0
author: community
tags: [data, dashboard, visualization, business-intelligence]
license: MIT
---

# Dashboard Designer

## Overview
This skill provides a structured approach to dashboard design—from defining the right KPIs and audience through information architecture, visual hierarchy, color coding, and tool selection. A well-designed dashboard surfaces the right information to the right person at the right time. This skill combines data visualization principles with UX thinking to create dashboards that drive decisions, not just display data.

## When to Use
- You're building a new dashboard and need to decide what to show and how to arrange it
- An existing dashboard is cluttered, ignored, or confusing to users
- You need to choose between BI tools (Tableau, Power BI, Looker, Grafana)
- You want to design a KPI framework before building the technical implementation
- You're presenting data to an executive audience and need to structure the narrative

## When NOT to Use
- Writing SQL queries to power the dashboard (use sql-analyst skill)
- Building chart code in Python/JavaScript (use chart-builder skill)
- Creating a static report or slide deck (use a presentation skill)
- Designing the underlying data model or warehouse (use a data engineering skill)

## Quick Reference
| Design Principle | Rule |
|-----------------|------|
| Visual hierarchy | Most important KPI top-left; details bottom-right |
| White space | 15–20% of dashboard area; prevents cognitive overload |
| Color coding | Max 4–5 colors; use red/green for status, grey for context |
| Chart density | Max 6–8 charts per screen; prefer fewer, larger charts |
| KPI count | Executive: 3–5 KPIs. Operational: 8–12. Analyst: unlimited |
| Text | Titles as insights ("Revenue Up 12%"), not labels ("Revenue") |
| Refresh rate | Real-time for ops, daily for management, weekly/monthly for strategy |
| Interactivity | Filters at top; drill-down by click; default to most common view |
| Mobile | Stack charts vertically for mobile; limit to top 3 KPIs |
| Accessibility | Colorblind-safe palette; min contrast ratio 4.5:1 |

## Instructions

### Step 1: Define the Audience and Purpose
Answer these questions before designing anything:
1. **Who uses this dashboard?** Executive, team manager, analyst, engineer, customer?
2. **What decision does it inform?** ("Should we increase marketing spend in the West region?")
3. **How often will they look at it?** Real-time (ops), daily, weekly, monthly?
4. **What action can they take?** If no action → reconsider whether a dashboard is the right format.

**Dashboard archetypes:**
| Type | Audience | Refresh | KPI Count | Example |
|------|----------|---------|-----------|---------|
| Strategic | C-suite, VPs | Weekly/Monthly | 3–5 | Company health scorecard |
| Operational | Team leads, managers | Daily | 8–15 | Sales pipeline dashboard |
| Analytical | Data analysts | On-demand | Unlimited | Cohort analysis explorer |
| Real-time monitoring | Engineers, support | Live | 10–20 | System uptime & error rates |

### Step 2: Select the Right KPIs
A KPI must be:
- **Actionable**: the viewer can change it based on what they see
- **Timely**: available at the frequency the dashboard refreshes
- **Comparable**: has a target, benchmark, or prior period to compare against
- **Unambiguous**: has one clear definition shared by all stakeholders

**KPI selection process:**
1. List 20 metrics the audience cares about
2. Filter to metrics the audience can act on (eliminate