| name | meta-ads-analyzer |
| description | Provides expert-level analysis and diagnosis for Meta Ads campaigns. Use this skill to interpret performance data, identify root causes of issues, and generate actionable recommendations, with a special focus on correctly handling the 'Breakdown Effect'. |
Meta Ads Analysis & Diagnosis Skill
When to Use This Skill
Use this skill when you need to analyze and diagnose Meta Ads campaign performance, including:
- Interpreting campaign, ad set, or ad-level performance data
- Identifying root causes of performance issues
- Generating actionable optimization recommendations
- Understanding why Meta's system makes certain budget allocation decisions
Result Recommendations (MANDATORY for Final Reports)
IMPORTANT: The following rules are MANDATORY and MUST be strictly followed when writing the final analysis report. These are not optional guidelines—they define the required standards for all deliverables.
- NEVER recommend pausing or reducing budget for any segment based solely on higher average CPA/CPM in breakdown reports. Higher average cost does NOT mean poor performance—it often reflects the system capturing low marginal cost opportunities earlier. Removing segments may increase overall costs. Always frame changes as testable hypotheses, not directives.
- ALWAYS justify recommendations with data evidence, Meta's system mechanics, and expected impact on overall campaign performance.
- EVERY insight must include data evidence and explanation. Every recommendation must be actionable and verifiable.
- USE QUALIFIED LANGUAGE. Say "Estimated Reach of ~1,000" not "You reached 1,000 people." All metrics are estimates.
- ALIGN WITH OFFICIAL RECOMMENDATIONS. Check
get_recommendations API first. If diverging, explicitly acknowledge and explain why.
- Disambiguate clicks. Never use the term "clicks" alone. Use "Clicks (all)" for total interactions (likes, shares, page clicks, link clicks) or "Link Clicks" for clicks that lead offsite; these are distinct metrics with different meanings.**
Core Principles
- Holistic First: Evaluate at aggregate level before drilling down. The system optimizes for the whole, not the parts.
- Dynamic over Static: Analyze performance over time, not single snapshots.
- Marginal over Average: The system prioritizes marginal CPA (cost of the next result), not average CPA. A higher average CPA segment might be preventing even higher marginal costs elsewhere.
Analysis Workflow
Reference Documents:
references/breakdown_effect.md - The Breakdown Effect with examples (read this first)references/core_concepts.md - Ad Auction, Pacing, Learning Phase overviewreferences/learning_phase.md - Learning phase mechanicsreferences/ad_relevance_diagnostics.md - Quality, Engagement, Conversion rankingsreferences/auction_overlap.md - Diagnosing auction overlapreferences/pacing.md - Budget and bid pacingreferences/bid_strategies.md - Spend-based, goal-based, manual biddingreferences/ad_auctions.md - How auction winners are determinedreferences/performance_fluctuations.md - Normal vs. concerning fluctuations
Step 1: Identify the Correct Evaluation Level
This is the most critical step to avoid the Breakdown Effect.
| Campaign Setup | Correct Evaluation Level |
|---|
| Advantage+ Campaign Budget (CBO) | Campaign Level |
| Automatic Placements (without CBO) | Ad Set Level |
| Multiple Ads within a single Ad Set | Ad Set Level |
Step 2: Analyze with Meta-Specific Lens
Focus on these Meta-specific analytical angles:
- Marginal Efficiency Analysis: Infer marginal CPA trends from time-series data. A segment with low average CPA but rising marginal CPA explains why the system shifts budget away.
- Ad Relevance Diagnostics: Check Quality, Engagement, and Conversion Rate Rankings to diagnose creative, targeting, or post-click issues.
- Learning Phase Status: Determine if ad sets are still in learning phase (~50 results needed to exit).
Step 3: Synthesize Findings Through Breakdown Effect Lens
Interpret all findings through the Breakdown Effect. Explain why the system makes certain decisions.
Example: "While Placement A shows $10 average CPA vs Placement B's $15, time-series analysis reveals Placement A's CPA rising sharply—its marginal CPA likely exceeds Placement B's. The system correctly shifts budget to secure more conversions at lower marginal cost."
