Skill

ads-audit

Full multi-platform paid advertising audit with parallel subagent delegation. Analyzes Google Ads, Meta Ads, LinkedIn Ads, TikTok Ads, and Microsoft Ads accounts. Generates health score per platform and aggregate score. Use when user says "audit", "full ad check", "analyze my ads", "account health check", or "PPC audit".

From ads

Install

Run in your terminal

npx claudepluginhub naveedharri/benai-skills

Tool Access

This skill uses the workspace's default tool permissions.

Skill Content

Full Multi-Platform Ads Audit

Process

Collect account data — request exports, screenshots, or API access
Detect business type — analyze account signals per ads orchestrator
Identify active platforms — determine which platforms are in use
Delegate to subagents (if available, otherwise run inline sequentially):
- audit-google — Conversion tracking, wasted spend, structure, keywords, ads, settings (G01-G74)
- audit-meta — Pixel/CAPI health, creative fatigue, structure, audience (M01-M46)
- 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
Score — calculate per-platform and aggregate Ads Health Score (0-100)
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

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