mcpbeat

Agency Dashboard

indranilbanerjee/agency-dashboard

Portfolio-level agency dashboard aggregating health metrics across all client brands — campaign status, budget pacing, KPI attainment, team utilization. Use when reviewing cross-brand portfolio health, preparing for agency leadership standups, or getting a single-view snapshot of all client accounts.

3k tokens
context cost
the whole folder, loaded on every use
1
files
instructions only
0
copies elsewhere
how many repositories repackaged it
694
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/indranilbanerjee/digital-marketing-pro --skill agency-dashboard

The instruction itself

6 sections, as written by the author

/digital-marketing-pro: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

  • 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 (/digital-marketing-pro:brand-setup)?" — or proceed with defaults.
  • 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
  • Pull campaign data per brand: For each brand in scope, run python "${CLAUDE_PLUGIN_ROOT}/scripts/campaign-tracker.py" --brand {slug} --action list-campaigns to retrieve active campaigns, statuses, budgets, and objectives (use --action get-campaign --id {id} for a single campaign's detail)
  • Pull execution status per brand: For each brand, run python "${CLAUDE_PLUGIN_ROOT}/scripts/execution-tracker.py" --brand {slug} --action get-history to get logged executions — completed deliverables, launches, and tasks — then derive pending / overdue status in analysis
  • 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
  • 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
  • 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)
  • Check team utilization: Run python "${CLAUDE_PLUGIN_ROOT}/scripts/team-manager.py" --action check-capacity --brand {slug} to assess current team workload — available capacity per team member, overloaded staff flagged, accounts at risk of under-service, and billable-hours tracking. Optionally add a Claude Code cost line — only if the user supplies the data. The plugin cannot read Claude Code usage itself. If the user pastes the output of their own /usage command (a Claude Code CLI slash command they run interactively), aggregate the per-model token/cost figures it reports into a "Claude Code consumption" line so leadership can see AI cost before the monthly invoice. Label models by whatever the user's /usage output names — do not assume specific model IDs. Brand-per-directory workspaces (~/work/clients/{slug}) make the figures brand-attributable.
  • 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
  • Claude Code consumption (per brand) — *only if the user supplied /usage data*: aggregated per working directory mapped to brand, using whatever model tiers the user's /usage output reports, with token totals and USD cost for the reporting window. Flag any brand whose Claude Code spend is >2× the portfolio median for the same retainer tier as a candidate for engagement-pattern review or rate renegotiation. Omit this panel entirely if no usage data was provided.
  • 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 /digital-marketing-pro:performance-report, /digital-marketing-pro:client-report, or /digital-marketing-pro: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

How to use it

Copy the folder

Take indranilbanerjee/agency-dashboard from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.