agency-dashboard

Use when the user wants a portfolio-level view across all client brands — active campaigns, budget pacing, KPI health, content pipeline, team utilization, and upcoming deadlines.

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Install skill "agency-dashboard" with this command: npx skills add indranilbanerjee/digital-marketing-pro/indranilbanerjee-digital-marketing-pro-agency-dashboard

/dm:agency-dashboard

Purpose

Generate a portfolio-level dashboard aggregating health metrics across ALL client brands. Shows campaign activity, budget pacing, KPI attainment, content pipeline, and team utilization at a glance — giving agency leadership a single view of operational health without opening each account individually. Designed for daily standups, weekly agency reviews, or on-demand health checks.

Input Required

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

  • Dashboard scope: All brands or a specific list of brand slugs to include in the portfolio view
  • Time period: Current week, month, or quarter — determines the pacing calculations and comparison windows
  • Detail level: Summary (top-line health scores per client) or detailed (campaign-level breakdowns per client with individual campaign metrics)
  • Sort/filter preferences: Sort clients by health score, spend, revenue, or alphabetical — and optionally filter to only at-risk (amber/red) accounts
  • Team filter (optional): Filter by account lead or team pod if the agency has multiple pods managing different client sets
  • Comparison baseline (optional): Compare current period against prior period, same period last year, or plan/target — defaults to prior period
  • Alert threshold overrides (optional): Custom thresholds for performance drop alerts or budget pacing tolerance — defaults to 20% performance drop and 10% pacing variance
  • Export format (optional): Whether to output as markdown, Google Sheets, or Slack message — defaults to markdown

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. Check for agency SOPs at ~/.claude-marketing/sops/. If no brand exists, ask: "Set up a brand first (/dm:brand-setup)?" — or proceed with defaults.
  2. Enumerate all brands: Scan ~/.claude-marketing/brands/ for all configured brand directories (excluding _active-brand.json). For each brand, load profile.json to get client name, industry, engagement type, contract dates, assigned team members, and KPI targets
  3. Pull campaign data per brand: For each brand in scope, run campaign-tracker.py --brand {slug} --action list to retrieve active campaigns, statuses, budgets, objectives, and recent performance snapshots
  4. Pull execution status per brand: For each brand, run execution-tracker.py --brand {slug} --action list to get pending deliverables, in-progress tasks, completed items this period, and overdue items with age in days
  5. Check budget pacing per brand: For each brand, compare actual spend-to-date against planned spend for the current period — calculate pacing percentage and project end-of-period spend at current run rate
  6. Calculate per-client health score: Apply the RAG scoring formula from skills/context-engine/agency-operations-guide.md:
    • Green: On track across KPIs, budget on pace (within 10%), no overdue items, content pipeline flowing
    • Amber: 1-2 KPIs at risk, minor pacing drift (10-20%), items approaching deadline, or pending approvals aging
    • Red: Significant KPI misses, budget overspend (>20%), missed deadlines, stalled campaigns, or MCP disconnections
  7. Aggregate portfolio KPIs: Sum total active campaigns, total monthly spend, average ROAS across clients, total leads/conversions, total pending deliverables, and overall portfolio health distribution (count and percentage of green/amber/red)
  8. Check team utilization: Run team-manager.py --action check-capacity to assess current team workload — available capacity per team member, overloaded staff flagged, accounts at risk of under-service, and billable hours tracking
  9. Identify pending approvals: Scan execution logs across all brands for items awaiting client or internal approval — flag anything older than 48 hours as overdue, group by brand and urgency tier (routine, time-sensitive, blocking)
  10. Surface upcoming deadlines: Compile deadlines from all brands for the next 7 and 14 days — campaign launches, content due dates, reporting deadlines, contract milestones, renewal dates, and QBR schedules
  11. Detect alerts and anomalies: Flag any brand with sudden performance drops (>20% week-over-week on primary KPI), budget pacing issues (>10% ahead or behind plan), stalled campaigns (no activity in 5+ days), MCP connection failures, or expiring credentials
  12. Check content pipeline: Aggregate content status across all brands — items in draft, in review, approved, scheduled, and published — identify bottlenecks where content is stalling at a particular stage
  13. Generate trend comparison: Compare current portfolio health against the selected baseline period — show improving, stable, or declining trajectory for each client and the portfolio overall with directional arrows
  14. Compile portfolio dashboard: Assemble all data into a structured dashboard sorted by the user's preference, with drill-down detail available for any individual client

Output

A structured portfolio dashboard containing:

  • Portfolio health summary: Total clients in scope, health distribution (green/amber/red count and percentage), overall portfolio health score (weighted by client spend), and period-over-period trend direction
  • Per-client health cards: For each brand — client name, health score (RAG), active campaigns count, monthly spend with pacing status, primary KPI vs target with delta, next deadline, top alert if any, and assigned account lead
  • Aggregate KPI table: Total spend across portfolio, average ROAS, total active campaigns, total leads/conversions generated, cost efficiency trends, and period-over-period comparison with directional arrows
  • Budget pacing summary: Per-brand pacing status (on pace, underspending, overspending) with projected end-of-period spend, variance from plan in dollars and percentage, and portfolio-level pacing aggregate
  • Team utilization matrix: Per-team-member workload (accounts managed, hours allocated, capacity percentage, billable ratio), overloaded alerts, available bandwidth for new work, and staffing recommendations
  • Pending approvals queue: All items awaiting approval across brands with item description, age in hours, responsible owner, brand, urgency level, and estimated impact of delay
  • Upcoming deadlines (7/14 day): Chronological list of upcoming deadlines with brand, deliverable type, responsible owner, days remaining, dependency status, and risk assessment if missed
  • Content pipeline status: Aggregate view of content in draft, review, approved, and scheduled stages across all brands with stage-by-stage counts and bottleneck identification
  • Alerts and anomalies panel: Performance drops, pacing issues, stalled campaigns, MCP connection failures, expiring credentials, or overdue items requiring immediate attention — sorted by severity
  • Contract and renewal tracker: Upcoming contract renewals, engagement milestones, and retention risk indicators for clients approaching renewal windows
  • Drill-down guidance: Instructions for investigating any individual client in detail using /dm:performance-report, /dm:client-report, or /dm:credential-switch to activate that brand's context

Agents Used

  • agency-operations — Portfolio aggregation, per-client health scoring, team utilization analysis, approval tracking, deadline compilation, budget pacing calculations, content pipeline aggregation, and alert detection
  • analytics-analyst — Metrics analysis, KPI aggregation, trend calculations, anomaly detection, performance benchmarking across the portfolio, and comparison baseline computations

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