ads-audit
🤖 AI Summary
**ads-audit** performs a multi-platform advertising audit by collecting account data (exports, screenshots, or API access), validating availability, detecting business type and active platforms, then delegating to platform-specific subagents (e.g., `audit-google`) for conversion tracking analysis—all guided by a 10-principle thinking framework to avoid mechanical execution.
How to Install
Claude Code:
git clone --depth 1 https://github.com/AgriciDaniel/claude-ads.git && cp claude-ads/skills/ads-audit ~/.claude/skills/ads-audit -r# 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 > Hig
Details
| Category | Security → vulnerability |
| Source | AgriciDaniel/claude-ads |
| SKILL.md | View on GitHub → |
| Repo Stars | ★ 6.4K |
| Est. per Skill | N/A (shared across 22 skills from this repo) |
| Difficulty | Intermediate |
| Risk Level | N/A |
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