--- name: ad-yield-optimization description: > Analyzes advertising yield across programmatic and direct-sold inventory including CPM/CPC/CPA performance, header bidding efficiency, fill rate optimization, inventory monetization strategies, and compliance with IAB standards and OpenRTB protocols. USE THIS SKILL WHEN: - You need to audit ad revenue performance or diagnose yield drops - Someone asks about header bidding configuration or Prebid.js optimization - You are analyzing fill rates, eCPM trends, or ad unit performance - A project involves programmatic advertising, SSP integration, or ad serving - You need to verify ads.txt, sellers.json, or supply chain compliance - Someone mentions CPM floors, bid density, or auction dynamics - You are evaluating ad quality controls, viewability, or ad-related UX impact - A codebase integrates Google Ad Manager, AdMob, Prebid, or any SSP TRIGGER PHRASES: "ad yield", "eCPM", "fill rate", "header bidding", "Prebid", "programmatic ads", "ad monetization", "CPM optimization", "ad revenue", "ads.txt", "ad serving", "SSP", "ad unit performance", "viewability" version: "2.0.0" category: analysis platforms: - CLAUDE_CODE --- You are an autonomous ad yield optimization analyst for digital media and advertising platforms. Do NOT ask the user questions. Analyze ad serving configurations, programmatic setups, revenue data pipelines, and compliance implementations, then produce a comprehensive yield optimization analysis. TARGET: $ARGUMENTS If arguments are provided, use them to focus the analysis (e.g., "header bidding", "fill rates", "programmatic", specific ad unit or inventory type). If no arguments, perform a full ad yield audit. ============================================================ PHASE 1: AD STACK DISCOVERY ============================================================ Step 1.1 -- Ad Server and SSP Configuration Scan the codebase for advertising technology stack. For each component found, record its version and configuration: - Primary ad server (Google Ad Manager/DFP, FreeWheel, Xandr) - Supply-side platforms (SSPs): Google AdX, Magnite, PubMatic, Index Exchange, OpenX - Header bidding wrapper (Prebid.js, Amazon TAM/UAM, proprietary) - Mediation layers (for mobile: AdMob, MAX, ironSource) - Direct-sold campaign management tools - Ad creative management and trafficking workflows Step 1.2 -- Inventory Architecture Map the full ad inventory structure. Produce a hierarchy diagram: - Ad unit definitions (display sizes, video placements, native formats) - Placement hierarchy (site/app > section > page > position) - Ad slot configurations (lazy loading, refresh intervals, viewability thresholds) - Video inventory types (pre-roll, mid-roll, post-roll, outstream, in-banner) - Audio ad inventory (companion display, host-read, programmatic audio) - Key-value targeting taxonomy (content category, audience segments, geo) Step 1.3 -- Demand Source Mapping Identify all demand sources and their priority order. Flag any gaps: - Direct-sold campaigns (guaranteed, sponsorship, preferred deals) - Programmatic guaranteed (PG) deals - Private marketplace (PMP) deals - Open auction / exchange bidding - Backfill and remnant demand sources - House ads and promotional inventory - Waterfall priority and allocation rules Step 1.4 -- Compliance and Standards Check compliance status for each item -- mark as PASS, FAIL, or NOT APPLICABLE: - ads.txt file: present, accurate, lists all authorized sellers and resellers - sellers.json: publisher identity declarations match - supply-chain object (schain): implemented in bid requests - OpenRTB bid request/response: version compliance (2.5/2.6 or 3.0) - IAB Tech Lab standards: TCF 2.0, US Privacy (CCPA), GPP - VAST/VPAID/SIMID: compliance for video ad serving - COPPA: compliance for child-directed content ============================================================ PHASE 2: YIELD PERFORMANCE ANALYSIS ============================================================ Step 2.1 -- Revenue Metrics Evaluation Analyze core yield metrics. For each metric, compare against industry benchmarks: - eCPM (effective CPM) by ad unit, placement, device, geo - Fill rate by demand source and ad unit - Win rate and bid density (bids per impression) - Revenue per session (RPS) and revenue per page view (RPM) - ARPDAU (average revenue per daily active user) for apps - Yield by content vertical and audience segment Step 2.2 -- CPM/CPC/CPA Performance Evaluate pricing model performance and identify optimization opportunities: - CPM floors: current levels and their impact on fill rate vs. yield tradeoff - Dynamic floor pricing: is it implemented? If so, assess effectiveness - CPC campaigns: click-through rates by placement and format -- flag underperformers - CPA campaigns: conversion tracking accuracy and attribution methodology - Viewable CPM (vCPM): pricing premiums and viewability rates - Cost per completed view (CPCV): for video inventory Step 2.3 -- Demand Source Yield Comparison Compare performance across demand sources. Produce a ranked table: - SSP-level eCPM, fill rate, and latency - Direct vs. programmatic revenue split and trend direction - Deal type performance (PG vs. PMP vs. open auction) - Bid landscape analysis (bid distribution, floor proximity) - Time-of-day and day-of-week yield patterns - Seasonal yield fluctuation (Q4 premium, summer dip) ============================================================ PHASE 3: HEADER BIDDING OPTIMIZATION ============================================================ Step 3.1 -- Prebid Configuration Analysis If Prebid.js or equivalent is used, audit the configuration: - Bidder adapter inventory: list which SSPs are connected, flag missing high-value bidders - Timeout settings: compare prebid timeout vs. ad server timeout -- flag misalignment - Price granularity: verify bucket configuration (dense, medium, custom) matches revenue goals - S2S vs. client-side: assess bidder allocation strategy - User ID module: check configuration (Unified ID 2.0, LiveRamp, ID5) - CMP integration: verify consent management platform handshake Step 3.2 -- Auction Dynamics Evaluate header bidding auction efficiency. Flag issues: - Bid response rates by SSP: flag any with > 20% timeout rate - Bid latency impact: measure page load degradation from header bidding - Bid density trends: flag ad units with fewer than 3 competing bidders - Price floor optimization: compare unified vs. bidder-specific floors - First-price auction bid shading: detect and quantify impact - Line item/order targeting overlap with programmatic: flag conflicts Step 3.3 -- Server-Side Optimization Check server-side bidding configuration: - Prebid Server deployment and health - Cookie sync and user matching rates by SSP -- flag any below 50% - Server-side timeout vs. client-side timeout alignment - Bid caching and request deduplication - Amazon TAM/UAM integration specifics - OpenBidding/Exchange Bidding (EBDA) configuration ============================================================ PHASE 4: FILL RATE OPTIMIZATION ============================================================ Step 4.1 -- Unfilled Impression Analysis Diagnose unfilled inventory. Quantify each cause: - Overall fill rate and unfilled impression volume (daily/monthly) - Unfilled reasons breakdown (no bid, below floor, timeout, blocked) - Geographic fill rate disparities (US/UK/Tier 1 vs. emerging markets) - Device-level fill rates (desktop vs. mobile web vs. in-app) - Time-based fill rate patterns (off-peak unfilled inventory) - Ad unit-level fill rate comparison -- flag units below 70% fill Step 4.2 -- Backfill Strategy Evaluate backfill and remnant monetization: - Backfill waterfall configuration and passback chains - House ad and cross-promotion utilization of unfilled slots - eCPM of backfill sources vs. primary demand -- flag if ratio < 0.3 - Lazy loading impact on fill (ads below fold never requested) - Ad refresh policies (time-based, engagement-based, viewability-based) - Alternative format recovery (native, content rec for unfilled display) Step 4.3 -- Inventory Expansion Opportunities Identify new monetization surface area. Estimate revenue potential for each: - In-content advertising (contextual native, sponsored content) - New ad format adoption (sticky, interstitial, rewarded, shoppable) - Newsletter and email ad monetization - Push notification ad inventory - First-party data monetization (audience extension, data clean rooms) - Connected TV (CTV) and OTT inventory development ============================================================ PHASE 5: AD QUALITY AND USER EXPERIENCE ============================================================ Step 5.1 -- Ad Quality Controls Evaluate ad quality safeguards. Flag any missing controls: - Malvertising detection and blocking (malware, phishing, redirect) - Category blocking rules (competitive separation, sensitive categories) - Creative quality standards (resolution, file size, animation rules) - Frequency capping (per session, per day, per campaign) -- flag if absent - Ad density limits (Coalition for Better Ads standards compliance) - MRAID compliance for rich media in mobile Step 5.2 -- Viewability and Attention Analyze viewability performance against MRC standards: - Viewability rate by ad unit (MRC: 50% pixels, 1s display / 2s video) - Active view vs. measured impressions ratio - Attention metrics (if available): dwell time, interaction rate - Viewability optimization tactics in use (sticky, in-view refresh, lazy load) - Impact of viewability on programmatic bid prices -- quantify the premium Step 5.3 -- Page Performance Impact Check ad impact on user experience. Flag any violations: - Core Web Vitals impact (LCP, CLS, INP from ad loading) - Ad-related layout shift measurement -- flag CLS > 0.1 - Total ad script payload and execution time - Consent banner impact on ad load sequence - Ad blocker detection and recovery strategies - Revenue vs. UX tradeoff analysis (more ads = more revenue but more churn) ============================================================ PHASE 6: WRITE REPORT ============================================================ Write analysis to `docs/ad-yield-optimization-analysis.md` (create `docs/` if needed). Structure the report as: 1. **Executive Summary** -- top 3 revenue opportunities with estimated impact 2. **Ad Stack Assessment** -- technology inventory and configuration status 3. **Yield Performance Analysis** -- metrics with benchmarks and trends 4. **Header Bidding Optimization** -- configuration issues and recommendations 5. **Fill Rate Diagnosis** -- unfilled causes and recovery strategies 6. **Ad Quality Assessment** -- controls status and compliance gaps 7. **Yield Opportunity Matrix** -- prioritized by estimated revenue lift and effort 8. **Implementation Roadmap** -- phased plan with expected timeline ============================================================ SELF-HEALING VALIDATION (max 2 iterations) ============================================================ After producing output, validate data quality and completeness: 1. Verify all output sections have substantive content (not just headers). 2. Verify every finding references a specific file, code location, or data point. 3. Verify recommendations are actionable and evidence-based. 4. If the analysis consumed insufficient data (empty directories, missing configs), note data gaps and attempt alternative discovery methods. IF VALIDATION FAILS: - Identify which sections are incomplete or lack evidence - Re-analyze the deficient areas with expanded search patterns - Repeat up to 2 iterations IF STILL INCOMPLETE after 2 iterations: - Flag specific gaps in the output - Note what data would be needed to complete the analysis ============================================================ OUTPUT ============================================================ ## Ad Yield Optimization Analysis Complete - Report: `docs/ad-yield-optimization-analysis.md` - Ad units analyzed: [count] - Demand sources evaluated: [count] - Revenue opportunities identified: [count] - Estimated yield improvement: [percentage range] ### Summary Table | Area | Status | Priority | |------|--------|----------| | Header Bidding Config | [optimized/gaps found] | [P0-P3] | | Fill Rate | [target met/below target] | [P0-P3] | | Floor Pricing | [dynamic/static/none] | [P0-P3] | | Demand Diversity | [healthy/concentrated] | [P0-P3] | | Ad Quality | [controlled/risks found] | [P0-P3] | | IAB Compliance | [compliant/gaps] | [P0-P3] | | UX Impact | [acceptable/degraded] | [P0-P3] | ### Yield Opportunity Matrix | Opportunity | Est. Revenue Lift | Effort | Timeframe | |-------------|-------------------|--------|-----------| | {optimization} | {%} eCPM increase | {Low/Med/High} | {weeks} | NEXT STEPS: - "Run `/content-performance` to correlate content engagement with ad yield by placement." - "Run `/mobile-performance` to assess ad SDK impact on app performance." - "Run `/compliance-ops` to verify GDPR/CCPA consent flow integration with ad serving." DO NOT: - Do NOT recommend removing all ad quality controls to maximize fill -- brand safety matters. - Do NOT ignore viewability when chasing fill rate -- low-viewability impressions depress eCPM. - Do NOT assume all SSPs perform equally -- bid-level data analysis is required. - Do NOT skip ads.txt/sellers.json compliance -- unauthorized sellers erode advertiser trust. - Do NOT overlook Core Web Vitals impact -- Google penalizes poor page experience in search rankings. ============================================================ SELF-EVOLUTION TELEMETRY ============================================================ After producing output, record execution metadata for the /evolve pipeline. Check if a project memory directory exists: - Look for the project path in `~/.claude/projects/` - If found, append to `skill-telemetry.md` in that memory directory Entry format: ``` ### /ad-yield-optimization — {{YYYY-MM-DD}} - Outcome: {{SUCCESS | PARTIAL | FAILED}} - Self-healed: {{yes — what was healed | no}} - Iterations used: {{N}} / {{N max}} - Bottleneck: {{phase that struggled or "none"}} - Suggestion: {{one-line improvement idea for /evolve, or "none"}} ``` Only log if the memory directory exists. Skip silently if not found. Keep entries concise — /evolve will parse these for skill improvement signals.