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name: dashboard-brief
description: "Convert a business question into a complete dashboard specification. Use when asked to design a dashboard, create a dashboard spec or brief, plan a BI report, or define what charts and metrics a dashboard should include. Produces a structured spec with metrics, dimensions, chart types, filters, and layout guidance."

Dashboard Brief Skill

This skill converts a business question or monitoring need into a complete, implementation-ready dashboard specification. The output gives a data engineer or BI developer everything they need to build without a follow-up meeting.

Required Inputs

Ask the user for these if not provided:

• The business question this dashboard should answer (e.g. "How is our activation funnel performing this week?")
• Primary audience (exec / product team / operations / customer success / engineering)
• Refresh cadence (real-time / hourly / daily / weekly)
• Data sources available (e.g. Postgres, BigQuery, Mixpanel, Salesforce, Jira)
• BI tool being used (Looker / Metabase / Tableau / Power BI / Grafana / Custom / Unknown)

Output Structure

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Dashboard Brief: [Dashboard Name]

Business Question: [The question this dashboard answers — verbatim from inputs or refined]
Audience: [Who uses this]
Refresh Rate: [Real-time / Hourly / Daily / Weekly]
Data Sources: [List]
BI Tool: [Tool or Unknown]

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Section 1: Key Metrics (KPI Cards)

List the headline numbers that should appear at the top of the dashboard as KPI cards.

Metric	Definition	Data Source	Comparison
[Metric name]	[How it's calculated]	[Table/source]	[vs. last week / vs. target / MoM]

Aim for 3–6 KPI cards. More than 6 is noise.

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Section 2: Charts & Visualisations

For each chart, specify:

Chart [N]: [Chart Title]

• Chart type: [Line / Bar / Stacked bar / Pie / Funnel / Heatmap / Table / Scatter]
• Why this chart type: [One sentence — why this type suits this data]
• X-axis / Rows: [Dimension — e.g. Date, User segment, Product]
• Y-axis / Values: [Metric — e.g. Count of active users, Revenue]
• Breakdown/colour: [Optional secondary dimension — e.g. by Plan tier, by Channel]
• Data source: [Table or source]
• Filters: [Any default filters applied — e.g. "Exclude internal test accounts"]
• Key insight to surface: [What pattern or signal this chart should help the viewer spot]

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Section 3: Filters & Controls

Global filters available to dashboard viewers:

Filter	Type	Default	Options
Date range	Date picker	Last 30 days	Custom
[Segment filter]	Dropdown	All	[List relevant values]
[Other filter]	Multi-select	All	[List relevant values]

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Section 4: Layout Recommendation

Describe the dashboard layout in plain terms:

[ROW 1 — KPI Cards]: [Metric 1] | [Metric 2] | [Metric 3] | [Metric 4]
[ROW 2 — Primary chart, full width]: [Chart name]
[ROW 3 — Two charts side by side]: [Chart A] | [Chart B]
[ROW 4 — Supporting table, full width]: [Table name]

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Section 5: Data Requirements

List any data transformations, joins, or derived fields needed:

Derived Field	Logic	Source Tables
[Field name]	[How it's calculated]	[Tables involved]

Flag any fields that may not exist in current data infrastructure.

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Section 6: Access & Ownership

• Dashboard owner: [Leave for user to fill]
• Who can edit: [Leave for user to fill]
• Who can view: [Leave for user to fill]
• Review cadence: [When should this dashboard be reviewed for relevance?]

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Quality Checks

• Every chart has a stated "key insight to surface" — not just "show the data"
• KPI cards are 3–6 (not more)
• Chart types are justified
• Layout follows visual hierarchy (summary → detail)
• Data requirements section flags any missing fields
• Filters are practical and don't require IT to configure

Anti-Patterns

• Do not specify metrics that the available data sources cannot actually support — always validate data availability
• Do not include more than 8–10 primary metrics on a single dashboard — more creates noise, not insight
• Do not skip the primary business question — a dashboard without a north-star question becomes a vanity metrics display
• Do not choose chart types for aesthetic reasons — every chart type must match the data relationship it represents
• Do not leave filter configurations vague — specify exact filter values, not just filter categories

Example Trigger Phrases

• "Design a dashboard to track [business process]"
• "Give me a spec for a [team] performance dashboard"
• "What should go on a [topic] dashboard?"
• "Write a dashboard brief for our [metric] monitoring"