/digital-marketing-pro:executive-dashboard

SkillMonitoring & ops

Design an executive marketing dashboard as a build-ready specification — 5-7 north-star metrics with rationale, metric hierarchy, chart choices, alert thresholds, data-source mapping, wireframe layout, drill-down structure, and a mobile variant. It designs the dashboard; it does not build or connect a live one. Triggers on \"/digital-marketing-pro:executive-dashboard\", \"design a CMO dashboard\", \"what metrics should the board see\", \"our exec report is too noisy\", \"create a leadership reporting view\". Reads the brand profile and guidelines; pairs with /digital-marketing-pro:exec-summary for the written companion narrative.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the /digital-marketing-pro:executive-dashboard skill

What this skill tells your AI

The instructions your AI receives, as published by indranilbanerjee/digital-marketing-pro in skills/executive-dashboard/SKILL.md and read by ahel’s review.

Purpose

Design a C-suite marketing dashboard that translates marketing metrics into business outcomes for executive decision-making. Bridges the gap between marketing activity data and business impact, giving senior leaders the clarity to make faster, better-informed strategic decisions without drowning in operational detail.

Input Required

The user must provide (or will be prompted for):

  • Executive role: Primary audience — CEO, CMO, CFO, VP Marketing, or board — each requires different metric emphasis and abstraction level
  • Business model and revenue drivers: How the company makes money — SaaS, e-commerce, lead gen, marketplace, subscription — and the key revenue levers marketing influences
  • Strategic priorities this quarter: The 2-4 business priorities the executive team is focused on that marketing should ladder up to
  • Reporting frequency: How often the dashboard will be reviewed — weekly executive standup, monthly leadership meeting, quarterly board review
  • Current data sources and tools: Analytics platforms, CRM, ad platforms, attribution tools, and BI systems currently in use with data freshness and reliability notes
  • Existing reports being replaced: Current reporting artifacts the dashboard will consolidate or replace — helps identify gaps and redundancies
  • Key decisions the dashboard should inform: Specific decisions executives make that this dashboard should support — budget allocation, channel mix, hiring, campaign scaling, market expansion
  • Stakeholder data literacy level: How comfortable the audience is with marketing metrics — determines labeling, context, and narrative density needed

Process

  1. Load brand context: Read ~/.claude-marketing/brands/_active-brand.json for the active slug, then load ~/.claude-marketing/brands/{slug}/profile.json. Apply brand voice, compliance rules for target markets (skills/context-engine/compliance-rules.md), and industry context. Also check for guidelines at ~/.claude-marketing/brands/{slug}/guidelines/_manifest.json — if present, load restrictions and relevant category files. Check for custom templates at ~/.claude-marketing/brands/{slug}/templates/. Check for agency SOPs at ~/.claude-marketing/sops/. If no brand exists, ask: "Set up a brand first (/digital-marketing-pro:brand-setup)?" — or proceed with defaults.
  2. Identify north-star metrics: Select 5-7 metrics that directly tie marketing activity to business outcomes — revenue influenced, pipeline generated, customer acquisition cost, lifetime value, market share, brand equity indicators
  3. Design metric hierarchy: Organize metrics into three tiers — leading indicators (predict future performance), lagging indicators (confirm past results), and health metrics (signal system stability and sustainability)
  4. Select visualization type per metric: Choose the optimal chart type for each metric based on data shape and decision context — trend lines for trajectory, gauges for targets, bar charts for comparisons, sparklines for density
  5. Define alert thresholds and anomaly triggers: Set green/yellow/red thresholds for each metric with specific trigger values, and configure anomaly detection rules for unexpected spikes or drops
  6. Map data sources to each metric: Document which system provides each metric, how it is calculated, data freshness (real-time, daily, weekly), and known limitations or lag
  7. Design layout for scanning speed: Structure the dashboard for F-pattern or Z-pattern scanning — most critical metrics top-left, summary before detail, consistent visual hierarchy, minimal cognitive load
  8. Add narrative guidance: Write "how to read this" instructions for each section — what good looks like, what bad looks like, and what action to take in each scenario
  9. Build drill-down structure: Design three levels of depth — summary view (the dashboard itself), detail view (campaign or channel breakdowns), and root cause view (diagnostic data for investigating anomalies)
  10. Create mobile-friendly variant: Adapt the dashboard layout for mobile or tablet viewing — prioritize top 3-5 metrics, stack vertically, enlarge touch targets, and simplify visualizations
  11. Add comparison baselines: Define what each metric is compared against — plan/target, prior period (MoM, QoQ, YoY), industry benchmark, and competitive estimate — with comparison display format

Output

A structured executive dashboard design containing:

  • North-star metrics (5-7): Selected metrics with business rationale explaining why each matters to the executive audience and how it connects to strategic priorities
  • Metric hierarchy diagram: Visual framework showing leading, lagging, and health metrics with causal relationships and directional influence between them
  • Visualization recommendations: Chart type, scale, color coding, and annotation style for each metric with rationale for the design choice
  • Alert threshold definitions: Green/yellow/red boundaries for each metric with specific trigger values, anomaly detection rules, and notification routing
  • Data source mapping: Metric-by-metric documentation of source system, calculation method, refresh frequency, data latency, and known quality issues
  • Dashboard wireframe layout: Spatial layout showing metric placement, section grouping, visual hierarchy, and scanning flow optimized for the target audience
  • Narrative guide: Section-by-section presentation guide explaining how to read each area, what questions it answers, and what actions to consider based on the data shown
  • Drill-down structure: Three-level depth design — summary (dashboard), detail (channel/campaign breakdown), and root cause (diagnostic investigation) with navigation flow
  • Mobile layout variant: Adapted design for mobile viewing with prioritized metrics, vertical stacking, simplified charts, and touch-optimized interactions
  • Comparison baseline definitions: For each metric, the comparison standard (target, prior period, benchmark, competitive) with display format and context notes
  • Refresh cadence and data latency notes: Documentation of how often each metric updates, expected data lag, and implications for decision timing
  • Executive summary template: A 3-sentence written narrative template that synthesizes dashboard findings into a verbal briefing — what happened, why it matters, what to do next
  • Glossary of terms: Plain-language definitions of all metrics and marketing terminology for non-marketing stakeholders with examples and context

Agents Used

  • analytics-analyst — Metric selection, hierarchy design, visualization recommendations, data source mapping, alert thresholds, drill-down architecture, refresh cadence, and dashboard layout optimization

Signals

GitHub stars
814
Forks
134
Last commit
Sep 2026
Advanced
Catalog kind
skill
Gateway key
executive-dashboard
Source
github.com/indranilbanerjee/digital-marketing-pro