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.
Best use case
ads-audit is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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.
Teams using ads-audit should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/ads-audit/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How ads-audit Compares
| Feature / Agent | ads-audit | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
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.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
Related Guides
SKILL.md Source
# Full Multi-Platform Ads Audit ## 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 (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 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 ```
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