/creative-health

name: creative-health
description: "Assess ad creative fatigue across active campaigns — scores each creative 0-100 via creative-fatigue-predictor.py (CTR, CPM, and engagement vs baseline, frequency saturation, time running), predicts days until fatigue, and delivers refresh briefs, A/B test plans, and a priority list ranked by estimated spend waste. Triggers on \"/digital-marketing-pro:creative-health\", \"are our ads fatiguing\", \"CTR is dropping on our ads\", \"when should we refresh creatives\", \"which ads are wasting spend\". Needs per-creative performance data (exported or from connected ad platform MCPs). Analysis and recommendations only — it does not pause or launch ads. Reads the brand profile and visual-identity restrictions for refresh constraints."

/digital-marketing-pro:creative-health

Purpose

Monitor creative health across all active advertising. Score each creative for fatigue risk, predict when fatigue will impact performance, generate refresh recommendations with specific change suggestions, and create A/B test plans for fatiguing creatives. Creative fatigue is one of the fastest ways to waste ad spend — a high-performing ad that ran too long silently bleeds money as CTR drops, CPM rises, and engagement fades. This command catches fatigue before it costs you, tells you exactly what to change, and gives you a structured test plan to validate the refresh. It covers the full creative lifecycle from launch through maturity to decline, so you always know which creatives are earning their spend and which need attention.

Input Required

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

• **Active creatives with performance data**: A list of currently running creatives, each with: creative ID or name, channel (paid social, display, search, email, video), current performance metrics (impressions served, frequency or average times shown per user, current CTR, current CPM or CPC, current engagement rate), baseline metrics from the creative's first 7-14 days (baseline CTR, baseline CPM, baseline engagement rate), days running since launch, audience size the creative is serving, and current daily or weekly spend. Can be provided as exported data or pulled from connected ad platform MCPs
• **Channel context**: Which advertising channels the creatives run on — Meta Ads, Google Ads, TikTok Ads, LinkedIn Ads, display networks, programmatic, or email. Each channel has different fatigue dynamics (social fatigues faster than search, video fatigues differently than static)
• **Refresh constraints (optional)**: Any constraints on creative refreshes — brand guidelines that limit visual changes, approval processes that add lead time, production capacity (how many new creatives can be produced per week), or budget for creative production. These constraints shape the refresh recommendations to be actionable within real-world limits
• **Performance thresholds (optional)**: Brand-specific thresholds for when a creative is considered "fatigued" — e.g., "CTR drops 30% from baseline" or "CPM increases 25% from baseline." If not provided, industry-standard thresholds are applied per channel

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 creative guidelines, historical creative performance benchmarks, known fatigue patterns from past campaigns, and production capacity constraints. Also check for guidelines at `~/.claude-marketing/brands/{slug}/guidelines/_manifest.json` — if present, load visual identity restrictions and messaging guardrails that constrain refresh options. 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 industry defaults. 2. **Score each creative's health**: Execute `creative-fatigue-predictor.py` with the performance data for each creative. The scoring model evaluates five fatigue signals — CTR ratio (current vs baseline, weighted 30%), CPM ratio (current vs baseline, weighted 20%), engagement ratio (current vs baseline, weighted 20%), frequency or impression saturation (weighted 20%), and time running relative to channel norms (weighted 10%). Each creative receives a health score from 0-100 where 100 is peak health and 0 is fully fatigued, plus a risk-level classification: Healthy (75-100), Watch (50-74), Fatiguing (25-49), or Critical (0-24). 3. **Predict fatigue timeline**: For each creative not yet at Critical risk, project the estimated days until fatigue based on the current trajectory of decline — rate of CTR decay, CPM acceleration, and engagement erosion. Factor in audience size (smaller audiences fatigue faster), frequency rate (higher frequency accelerates fatigue), channel dynamics (social fatigues 2-3x faster than search), and seasonality effects. Output a "days remaining" estimate with confidence range for each creative. 4. **Generate refresh briefs for fatiguing creatives**: For each creative at Fatiguing or Critical risk, produce a specific refresh brief — what to keep (elements that drove initial performance: hook, value proposition, social proof, CTA that still resonates), what to change (elements contributing to fatigue: visual treatment, headline angle, color scheme, format, opening hook for video), and what to test (new angles or approaches worth experimenting with based on competitor creative trends and brand positioning). Ensure all refresh suggestions comply with brand guidelines. 5. **Create A/B test plan for each creative needing refresh**: For each refresh brief, generate a structured A/B test plan — control (current creative), variant(s) with the recommended changes, hypothesis for why the variant should outperform, primary metric to evaluate (CTR, CPC, conversion rate depending on campaign objective), minimum sample size for statistical significance, expected test duration, and decision cri