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analytics-dashboard-design

Use when the task is to design a BI dashboard, 数据大屏, analytics workspace, reporting console, KPI board, or monitoring page. Focus on question-first information hierarchy, chart choice, filter clarity, metric context, and decision-ready layout.

Quellinformationen

Repository
Mr-Q526/TeamCC-Platform
Letzte Quellaktivität
15. April 2026 um 03:16
Erkannte Sprache von SKILL.md
Englisch
Sterne
7
Forks
1

Installationsoptionen

Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.

Quelldateien prüfen

Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.

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SKILL.md wird angezeigt

SKILL.md
Quellanweisungen · Schreibgeschützte Vorschau
schemaVersion
2026-04-11T00:00:00.000Z
skillId
frontend/analytics-dashboard-design
name
analytics-dashboard-design
displayName
Analytics Dashboard Design
description
Use when the task is to design a BI dashboard, 数据大屏, analytics workspace, reporting console, KPI board, or monitoring page. Focus on question-first information hierarchy, chart choice, filter clarity, metric context, and decision-ready layout.
aliases
["analytics-dashboard-design","Analytics Dashboard Design","analyticsdashboarddesign","数据分析","分析看板","BI","指标看板","仪表盘","看板","控制台","dashboard","analytics","数据看板设计","BI 看板","analytics dashboard","仪表盘设计","dashboard design"]
version
0.1.0
sourceHash
sha256:af5666a9ece8cdd0ec73bb133a58809c142f73a0461bb08782be6a51a21e54be
domain
frontend
departmentTags
["frontend-platform"]
sceneTags
["design"]
# Analytics Dashboard Design Use this skill when the page exists to help someone monitor, compare, diagnose, or decide from data. Goal: answer the top questions in one screen before showing supporting detail. Optimize for trends, context, and confidence, not decoration. ## Working model Before building, define three things: - primary questions: the top 3 questions the dashboard must answer - decision owner: who acts on the data and how often - freshness model: real-time, hourly, daily, weekly, or static ## Default structure 1. Global controls: date range, environment, segment, saved view 2. Key metrics: 3-5 headline KPIs with clear labels and deltas 3. Trend zone: one or two charts showing movement over time 4. Breakdown zone: dimensions, cohorts, channels, regions, or segments 5. Diagnostic detail: table, funnel, or event list 6. Notes and exceptions: anomalies, alerts, definitions, freshness ## Chart rules - Pick charts by question, not by novelty. - Line charts for trend, bars for comparison, area only when cumulative context matters. - Use consistent units, decimal precision, and time buckets. - Label axes and legends clearly; never rely on hover alone to explain the chart. - Reserve accent color for the main series and use muted support colors for context. - If a chart needs a paragraph to explain it, simplify it or split it. ## Layout rules - Put the most decision-driving metric in the first visual band. - Keep filters visible and predictable. - Align related charts to the same time range and vocabulary. - Use cards only when they group meaningfully; avoid a wall of equal-weight boxes. - Show metric definitions and last-updated times near the data. ## Interaction rules - Drill-down should preserve global filters. - Hover states add detail; they should not reveal the only useful information. - Empty states must explain whether data is missing, filtered out, or delayed. - Export actions must reflect the current filter state. ## Reject these failures - Decorative gradients behind production charts - Too many chart types on one page - KPI rows with no time context or comparison baseline - Legends that require color memory without labels - Full-screen "executive summary" banners that push real data below the fold ## Final checks - Can a user answer the top three questions in under 10 seconds? - Are metric definitions and freshness visible without hunting? - Does every chart earn its space by changing a decision?
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