ab-test-plan
Design a statistically rigorous A/B or multivariate test plan — If/Then/Because hypothesis, control and variant specs, required sample size per variant…
Generate a C-suite-ready executive summary of marketing performance — ROI, CAC, LTV, top-3 wins and risks, strategic recommendations, and budget utilization — for a single brand or the whole portfolio, adapted to CEO, CMO, CFO, or board audiences. Triggers on
$ npx -y skills add indranilbanerjee/digital-marketing-pro --skill exec-summary --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/exec-summaryContext preview
The summary Claude sees to decide when to auto-load this skill.
Generate a C-suite-ready executive summary of marketing performance — ROI, CAC, LTV, top-3 wins and risks, strategic recommendations, and budget utilization — for a single brand or the whole portfolio, adapted to CEO, CMO, CFO, or board audiences. Triggers on
name: exec-summary description: "Generate a C-suite-ready executive summary of marketing performance — ROI, CAC, LTV, top-3 wins and risks, strategic recommendations, and budget utilization — for a single brand or the whole portfolio, adapted to CEO, CMO, CFO, or board audiences. Triggers on \"/digital-marketing-pro:exec-summary\", \"prepare the board report\", \"summarize this quarter for the CEO\", \"portfolio ROI summary\", \"what do I tell leadership\". Computes KPIs via the CLV, ROI, forecaster, and budget-optimizer scripts, pulls data from connected analytics and CRM MCPs, reads the brand profile, and pairs with /digital-marketing-pro:competitor-analysis for competitive context."
Generate a concise, C-suite-ready executive summary of marketing performance. Focuses on business-level metrics — ROI, CAC, LTV, market share — rather than operational detail. Synthesizes strategic wins and risks, competitive positioning, and high-level recommendations into a format designed for executive decision-making. Supports single-brand summaries or portfolio-wide aggregation across all managed brands, and adapts depth and terminology to the target audience (CEO, CMO, board).
Designed to be the single artifact an executive needs to understand marketing's business impact, make resource allocation decisions, and assess strategic direction — without requiring follow-up questions or supplementary reports.
The user must provide (or will be prompted for):
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 (/digital-marketing-pro:brand-setup)?" — or proceed with defaults. 2. **Gather performance data**: For single brand — pull metrics from all connected MCPs (Google Analytics, Google Ads, Meta Ads, HubSpot, Salesforce, etc.), load campaign history from execution logs, load prior performance snapshots for trend comparison. For portfolio — iterate all brands in `~/.claude-marketing/brands/`, aggregate metrics per brand, then roll up to portfolio totals with currency normalization if brands operate in different regions. 3. **Calculate executive KPIs**: Compute the five core executive metrics — Total Marketing ROI (revenue attributed to marketing / total marketing spend), Customer Acquisition Cost (total spend / new customers acquired), Customer Lifetime Value (average revenue per customer x average retention period), estimated market share trend (if competitive data available), and Brand Health Score (composite of awareness, sentiment, engagement, and loyalty indicators). Use the scripts for standardized computation (all have required args):
python "${CLAUDE_PLUGIN_ROOT}/scripts/clv-calculator.py" --model simple --avg-purchase-value {value} --purchase-frequency {per-year} --customer-lifespan {years} --margin 30
python "${CLAUDE_PLUGIN_ROOT}/scripts/roi-calculator.py" --channels '[{"name":"google_ads","spend":10000,"revenue":42000}]' --attribution last_touch --period "{YYYY-MM}"4. **Analyze trends and comparisons**: Compare each KPI against the prior period (MoM or QoQ depending on time range), the same period last year (YoY), and targets or plan if set. Calculate percentage change and flag significant movements — improvement above 10% as a win, decline above 10% as a risk. Project end-of-period trajectory from the current run rate: `python "${CLAUDE_PLUGIN_ROOT}/scripts/revenue-forecaster.py" --historical '[{"period":"2026-05","revenue":120000}]' --forecast-months 3 --growth-assumption 0.05` (SYNTHETIC EXAMPLE — fabricated for illustration; never reuse these numbers). 5. **Review prior summary continuity**: If a prio
Your agency just signed a 50-brand client. The previous agency left no playbook. Three brands are bleeding budget, two have stale positioning, one is launching in a regulated jurisdiction next month. Where do you start?
Repo: indranilbanerjee/digital-marketing-pro
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