/digital-marketing-pro:live-dashboard

/digital-marketing-pro:live-dashboard

Script location. If your host does not set ${CLAUDE_PLUGIN_ROOT}, the scripts are in this plugin's scripts/ folder, next to skills/. Run them with Python 3.10 or newer: python, or python3 where python is not found (macOS, many Linux systems).

Purpose

Create and configure a live Google Looker Studio dashboard connected to the brand's marketing data sources. Auto-selects appropriate metrics, dimensions, and chart types based on the business model (SaaS, eCommerce, B2B, agency). Provides always-current visibility into marketing performance without manual data pulls. Eliminates the need for recurring report generation by giving stakeholders a self-service, real-time view of the metrics that matter most to their business model, with drill-down capability and date range controls built in.

Execution gate (MANDATORY — cannot be skipped)

By default this skill produces a dashboard specification for review. It must NOT create, publish, share, or connect a live data source to any external dashboard without passing this gate first:

1. Present the full spec — data sources, connected accounts, sharing scope, refresh cadence — as an Execution Summary.
2. The user must type yes (or an equivalent explicit approval) before any external dashboard is created or shared. ANY other input — ambiguous, implied, partial, or absent approval — cancels; the spec is saved but nothing is created externally.
3. Never proceed on ambiguous input. Never auto-retry a failed creation.
4. Only after the user types yes, record it: python "${CLAUDE_PLUGIN_ROOT}/scripts/approval-manager.py" --brand {slug} --action create-approval --data '{"type":"live-dashboard","platform":"<platform>","content_summary":"<one line from the Execution Summary>","risk_level":"<tier>"}', then python "${CLAUDE_PLUGIN_ROOT}/scripts/approval-manager.py" --brand {slug} --action approve --id {approval_id}. If either command errors, stop and report the error; never work around it with another tool. The record proves the approval step ran for this action; it cannot prove who typed yes.
5. Execute. A write sent through connector_executor.py needs --approval-id and fires only against a matching, unused, unexpired record (see /digital-marketing-pro:execute-action). A write through a connected MCP server tool is outside that code check: it relies on this typed yes and on your host's permission prompt. Afterwards run python "${CLAUDE_PLUGIN_ROOT}/scripts/approval-manager.py" --brand {slug} --action mark-executed --id {approval_id} --data '{"execution_result":"success"}' (or failure).

Input Required

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

• Business model: saas (recurring revenue focus — MRR, churn, activation, expansion), ecommerce (transaction focus — revenue, AOV, conversion rate, product performance), b2b-lead-gen (pipeline focus — MQLs, SQLs, pipeline value, CPL), or agency (multi-client focus — client health scores, utilization, cross-client performance). Determines the default metric set, layout template, and visualization priorities
• Data sources to connect: Which platforms to pull into the dashboard — Google Analytics (traffic, behavior, conversions), Google Ads (paid search performance, spend), Meta Ads (paid social performance, spend), CRM (pipeline, deal data, customer lifecycle), email platform (campaign performance, list health). Multiple sources can be combined into unified views with cross-platform calculated fields
• Primary KPIs to feature: The 3-5 headline metrics to display prominently at the top of the dashboard — e.g., MRR and churn rate for SaaS, revenue and ROAS for eCommerce, SQLs and pipeline value for B2B. These appear as scorecard widgets with trend indicators and target comparisons
• Dashboard audience: executives (high-level scorecards with trend arrows, minimal drill-down, focused on business outcomes), marketing-team (full operational detail with channel breakdowns, campaign-level data, and diagnostic dimensions), or client (branded presentation view with performance against stated objectives, competitive context, and clean visual design)
• Refresh frequency: How often the data should update — real-time (streaming where supported), daily (standard for most use cases), weekly (for executive dashboards with less granular needs). Determines data source caching configuration and extract schedule

Process

1. Load brand context: Read ~/.claude-marketing/brands/_active-brand.json for the active slug, then load ~/.claude-marketing/brands/{slug}/profile.json. Extract business model, key metrics, industry vertical, brand colors for dashboard theming, and connected platform credentials. Check for guidelines at ~/.claude-marketing/brands/{slug}/guidelines/_manifest.json. If no brand exists, ask: "Set up a brand first (/digital-marketing-pro:brand-setup)?" — or proceed with defaults.
2. Design dashboard layout based on business model template: Select the appropriate metric hierarchy and page structure. For SaaS: page 1 overview (MRR scorecard, churn rate, CAC, LTV, CAC:LTV ratio), page 2 acquisition funnel (traffic to trial to activation to paid, by channel), page 3 retention (cohort retention curves, expansion revenue, net revenue retention). For eCommerce: page 1 overview (revenue, AOV, conversion rate, ROAS), page 2 product performance (top products, category breakdown, inventory velocity), page 3 channel mix (attributed revenue by channel, campaign-level ROAS). For B2B: page 1 overview (MQLs, SQLs, pipeline value, win rate), page 2 funnel (lead to MQL to SQL to opportunity to closed, conversion rates per stage), page 3 channel efficiency (CPL, cost per SQL, cost per opportunity by channel). For Agency: page 1 portfolio overview (client health scores, total managed spend, utilization), page 2 per-client drill-down (selectable client filter with full KPI set), page 3 cross-client benchmarks.
3. Map data sources to dashboard widgets: For each widget in the layout, identify which connected MCP provides the required data — Google Analytics MCP for traffic and behavior metrics, Google Ads MCP for paid search data, Meta MCP for paid social data, CRM MCP for pipeline and deal metrics, email MCP for campaign performance. Flag any widgets that require data sources not yet connected and provide connection guidance.
4. Generate Looker Studio configuration: Produce the complete dashboard specification — data source connection parameters (account IDs, property IDs, date ranges), calculated field formulas (blended ROAS across platforms, weighted conversion rates, custom KPI calculations), chart specifications (chart type, dimensions, metrics, sort order, conditional formatting), filter controls (date range selector, channel filter, campaign filter, audience segment filter), and page layout with widget positioning and sizing.
5. Create dashboard setup instructions: Generate step-by-step guidance for implementing the dashboard in Looker Studio — how to create each data source connection, how to build each page and widget matching the specification, how to configure calculated fields with exact formulas, how to set up filter controls and their interactions, and how to apply brand theming (colors, fonts, logo placement). Include screenshots or visual references where helpful.
6. Provide dashboard template link or export configuration: No Looker Studio MCP server ships with this plugin (there is no google-looker-studio connector in the registry). By default, export the complete configuration as a structured specification document that can be implemented manually, with each widget fully defined and data source mappings documented. Only if the user has independently connected a Looker Studio MCP server may direct creation be attempted — and only after the Execution gate above passes.

Output

A structured dashboard delivery containing: