ads-audit

name	ads-audit
description	Full multi-platform paid advertising audit via parallel subagents across Google, Meta, LinkedIn, TikTok, Microsoft, and Apple Ads, with per-platform health scores and an aggregate 0-100 score. Use when user says audit my ads, account health check, paid media audit, ad spend audit, or PPC audit.
user-invokable	false
tested_date	"2026-05-17T00:00:00.000Z"
tested_with	claude-code v2.x

Full Multi-Platform Ads Audit

This audit operates under the 10-Principle Thinking Framework (see ads/references/thinking-framework.md). OBSERVE (External + Internal) dominates data collection, THINK + CONNECT (Lateral) dominate analysis, CONNECT (System) + ACCEPT dominate synthesis and prioritization. If the audit feels mechanical, you are skipping a principle.

Process

1. Collect account data: request exports, screenshots, or API access
2. Validate: confirm at least one platform's data is available before proceeding
3. Detect business type: analyze account signals per ads orchestrator
4. Identify active platforms: determine which platforms are in use
5. Delegate to subagents (if available, otherwise run inline sequentially):
   • audit-google: Conversion tracking, wasted spend, structure, keywords, ads, settings (80 checks; G01-G61 + 19 hyphenated v1.5+ IDs incl. AI Max)
   • audit-meta: Pixel/CAPI health, creative fatigue, structure, audience (50 checks; M01-M40 + 10 hyphenated v1.5+ IDs incl. Andromeda)
   • audit-creative: LinkedIn, TikTok, Microsoft creative checks + cross-platform synthesis
   • audit-tracking: LinkedIn, TikTok, Microsoft tracking + cross-platform tracking health
   • audit-budget: LinkedIn, TikTok, Microsoft budget/bidding + cross-platform allocation
   • audit-compliance: All-platform compliance, settings, performance benchmarks
6. Validate: verify each subagent returned valid scores with required fields before aggregating
7. Score: calculate per-platform and aggregate Ads Health Score (0-100)
8. Report: generate prioritized action plan with Quick Wins

Data Collection

Ask the user for available data. Accept any combination:

• Google Ads: account export, Change History, Search Terms Report
• Meta Ads: Ads Manager export, Events Manager screenshot, EMQ scores
• LinkedIn Ads: Campaign Manager export, Insight Tag status
• TikTok Ads: Ads Manager export, Pixel/Events API status
• Microsoft Ads: account export, UET tag status, import validation results

If no exports available, audit from screenshots or manual data entry.

Scoring

Read ads/references/scoring-system.md for full algorithm.

Per-Platform Weights

Platform	Category Weights
Google	Conversion 25%, Waste 20%, Structure 15%, Keywords 15%, Ads 15%, Settings 10%
Meta	Pixel/CAPI 30%, Creative 30%, Structure 20%, Audience 20%
LinkedIn	Tech 25%, Audience 25%, Creative 20%, Lead Gen 15%, Budget 15%
TikTok	Creative 30%, Tech 25%, Bidding 20%, Structure 15%, Performance 10%
Microsoft	Tech 25%, Syndication 20%, Structure 20%, Creative 20%, Settings 15%

Aggregate Score

Aggregate = Sum(Platform_Score x Platform_Budget_Share)
Grade: A (90-100), B (75-89), C (60-74), D (40-59), F (<40)

Output Files

• ADS-AUDIT-REPORT.md: Comprehensive multi-platform findings
• ADS-ACTION-PLAN.md: Prioritized recommendations (Critical > High > Medium > Low)
• ADS-QUICK-WINS.md: Items fixable in <15 minutes with high impact

Report Structure

Executive Summary

• Aggregate Ads Health Score (0-100) with grade
• Per-platform scores
• Business type detected
• Active platforms identified
• Top 5 critical issues across all platforms
• Top 5 quick wins across all platforms

Per-Platform Sections

Each platform section includes:

• Platform Health Score with grade
• Category breakdown with pass/warning/fail per check
• Platform-specific Quick Wins
• Detailed findings with remediation steps

Cross-Platform Analysis

• Budget allocation assessment (actual vs recommended)
• Tracking consistency (are all platforms tracking the same events?)
• Creative consistency (is messaging aligned across platforms?)
• Attribution overlap (are platforms double-counting conversions?)

Strategic Recommendations

• Platform prioritization based on business type
• Budget reallocation recommendations
• Scaling opportunities (platforms/campaigns ready to scale)
• Kill list (campaigns/ad groups to pause immediately)

Priority Definitions

• Critical: Revenue/data loss risk (fix immediately)
• High: Significant performance drag (fix within 7 days)
• Medium: Optimization opportunity (fix within 30 days)
• Low: Best practice, minor impact (backlog)

Quick Wins Criteria

IF severity == "Critical" OR severity == "High"
AND estimated_fix_time < 15 minutes
THEN flag as Quick Win
SORT BY (severity_multiplier x estimated_impact) DESC