meta-ads-analyzer

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Diagnose Meta Ads campaign performance using Meta's actual system mechanics — Breakdown Effect, Learning Phase, Auction Overlap, Pacing, and Creative Fatigue — and produce structured, testable recommendations that avoid judging segments by average CPA instead of marginal efficiency.

AI & Automation 1,055 stars 194 forks Updated 3 days ago MIT

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Skill Content

# Meta Ads Analyzer Most "Meta Ads analysis" stops at "this CPA is high, pause it." That's wrong more often than it's right. Meta's delivery system optimizes for **marginal efficiency** — the cost of the *next* conversion — not average efficiency across a snapshot. A segment with a higher average CPA is often the one keeping your overall campaign cheap. Pausing it makes things worse. This skill diagnoses Meta campaigns the way a senior media buyer would: at the right evaluation level, accounting for learning state, separating noise from signal, and explaining *why* the system is making the decisions it's making before recommending any change. **Core principle:** Holistic first, then drill down. Marginal over average. Dynamic over static. Every recommendation is a testable hypothesis with expected impact, not a directive. ## When to Use - "Analyze my Meta Ads campaign performance" - "Why is the system spending more on the higher-CPA placement?" - "Diagnose what's wrong with this ad set" - "Should I pause this audience / placement / ad?" - "My CPA jumped — is this normal or a real problem?" - "Audit this campaign before I scale budget" - "I exported my Meta data — what does it actually mean?" ## Phase 0: Intake 1. **Campaign data** — One of: - CSV export from Meta Ads Manager (Campaign / Ad Set / Ad level + breakdowns) - Pasted performance table - Screenshots (we'll extract the metrics) - Live data via your existing Meta Marketing API connection 2. **Campaign...

Details

Author
gooseworks-ai
Repository
gooseworks-ai/goose-skills
Created
4 months ago
Last Updated
3 days ago
Language
Python
License
MIT

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