meta-ads-analyzer
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.
npx skills add gooseworks-ai/goose-skills --skill meta-ads-analyzer --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 actual
- When to Use
- Phase 0: Intake
- Phase 1: Identify the Correct Evaluation Level
- Phase 2: Check Learning Phase Status
- Phase 3: Diagnose with Meta-Specific Lenses
- 3A: Marginal Efficiency Analysis (Breakdown Effect)
- 3B: Ad Relevance Diagnostics
- 3C: Auction Overlap Check
- 3D: Pacing Analysis
- 3E: Performance Fluctuation Assessment
- Phase 4: Synthesize Through the Breakdown Effect Lens
- Phase 5: Generate the Report
- Output Standards (Mandatory)
- Metric Naming Standard
What does the meta-ads-analyzer skill do?
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.
How do I install it?
Run `npx skills add gooseworks-ai/goose-skills --skill meta-ads-analyzer --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From gooseworks-ai/goose-skills, a repository with 1,091 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.