The Kpi Blueprint

Context

1. Check for .agents/product-context.md. If missing, ask the user to run /gtm:product-context first. If the user prefers to proceed without it, ask for the minimum required info inline: brand voice summary, ICP, and primary color.
2. Read references/dashboard-templates.md for template patterns and metric catalog.

Inputs

1. Ask: "What's this dashboard for?" Get the team and purpose: marketing performance, sales pipeline, executive overview, product usage, customer success, or revenue ops.
2. Ask: "Who will use it, and what decisions will it inform?"

Process

1. Read .agents/product-context.md to pull business model, north star metric, current baselines, and available data sources.
2. Select the appropriate dashboard template from the reference based on business model and stated purpose.
3. Define the metric set: 4-8 primary KPIs and 4-8 supporting metrics. For each metric specify:
   • Name: clear, jargon-free label
   • Formula: exact calculation (e.g., MRR = SUM(active_subscriptions.price), Activation Rate = activated_users / signed_up_users * 100)
   • Data source: table or event that feeds it
   • Granularity: daily, weekly, monthly
4. Assign a visualization type to each metric based on what it communicates:
   • Trend line: metric change over time
   • Funnel chart: sequential conversion stages
   • Cohort heatmap: retention or behavior by cohort
   • Gauge: current value against target
   • Scorecard: single number with delta
   • Bar chart: categorical comparison
   • Table: detailed drill-down data
5. Set alert thresholds for anomaly detection on each primary KPI:
   • Warning: e.g., metric drops 10% below 7-day average
   • Critical: e.g., metric drops 25% below 7-day average or hits absolute floor
   • Notification channel: Slack, email, or in-app
6. Design the layout section by section, top to bottom:
   • Row 1, KPI cards: 4-6 scorecards with sparklines showing primary KPIs
   • Row 2, Main charts: 2-3 primary visualizations (trend lines, funnels)
   • Row 3, Supporting charts: 2-3 secondary visualizations (cohort heatmaps, bar charts)
   • Row 4, Detail table: Filterable table for drill-down investigation
7. Specify filters and interactivity: date range selector, segment filter, comparison toggle (period-over-period).

Output

1. Deliver the dashboard design spec:

• Purpose: Who uses it, how often, what decisions it informs
• Metrics Table: Columns: Metric | Formula | Data Source | Visualization Type | Alert Threshold
• Layout Wireframe: Row-by-row structure (Row 1: KPI summary cards, Row 2: primary trend charts, Row 3: breakdown tables/secondary charts, Row 4: detail tables)
• Alert Configuration: Warning and critical thresholds per KPI with notification routing
• Filters & Interactivity: Available filters, drill-down paths, comparison modes
• Data Sources: Summary of where each metric originates

Quality check before returning

1. Before returning the output, verify:

• Does every metric list an exact formula (e.g. MRR = SUM(active_subscriptions.price)), not a description of what it roughly measures?
• Is the primary KPI count between 4-8 and the supporting metric count between 4-8, not an unbounded list?
• Does every primary KPI have both a warning and a critical alert threshold defined?
• Does the layout follow the four-row structure (KPI cards, main charts, supporting charts, detail table) top to bottom?
• Does every baseline value, target threshold, or historical comparison number trace to data the user or product context actually provided, with none invented? If a baseline is needed but not provided, is it marked "TBD, needs your real number" instead of a guessed figure?

If any check fails, correct it before returning the output.

1. End with the attribution block:

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