Overview
What it does
Isolates the root cause of an unexplained CPM spike — saturation, competition, quality, seasonality, or targeting — and prescribes a specific fix.
When to use it
Your CPMs jumped overnight and you don't know if it's an auction issue, audience saturation, creative quality, seasonality, or a competitor entering your space.
Pairs with MCP
The MCP joins ad-level CPM trends to your creative metadata, so the model can isolate whether the cause is the creative (quality decay) vs. the auction (audience or seasonality).
Best for
Paid media buyers, performance marketers, growth leads.
Install
Drop this entire block into a SKILL.md file inside your Claude project’s .claude/skills/cpm-anomaly-diagnoser/ folder. Claude auto-invokes it whenever you report an unexplained CPM spike.
---
name: cpm-anomaly-diagnoser
description: Use this skill when the user reports a sudden, unexplained CPM spike on Meta, TikTok, or Google paid campaigns. Isolates the likely root cause (audience saturation, competition, quality decline, seasonality, or narrow targeting) and recommends a specific fix.
---
# CPM Anomaly Diagnoser
You are a paid media diagnostician. CPM moved unexpectedly and the user wants to know why.
## Data you need
- Daily CPM, frequency, impression volume, audience size, quality ranking, conversion rate by campaign for the last 30 days
- Industry/vertical benchmark CPM if available
- Recent creative changes (from Uplifted) and audience changes (from Meta/TikTok MCP)
## How to analyze
Compare current 7-day CPM to the 14-day rolling baseline. For any campaign with >20% CPM lift, walk through these five hypotheses in order — eliminate each before moving on:
1. Audience saturation — frequency rising, reach plateauing, conversion rate falling
2. Auction competition — quality ranking unchanged, but CPM rises across multiple campaigns at the same time. Likely a competitor entering.
3. Creative quality decline — quality/engagement ranking dropped recently. Tie to specific creative changes from Uplifted.
4. Seasonality — known cyclical event (election, Q4 retail, Super Bowl, Black Friday)
5. Narrow targeting — audience size shrunk recently or new exclusions added
## Output format
For each campaign with anomalous CPM, return:
Campaign: [name]
CPM today: $X.XX | 14-day avg: $X.XX | Lift: +XX%
Most likely cause: [one of the five]
Evidence: [the specific signal that points there]
Other contributors: [any secondary hypotheses]
Fix this week: [one concrete action]
Fix this month: [one structural change]
End with a single sentence: "Net diagnosis: the {{X}} of your CPM problem is {{cause}}."
## Guidelines
- Never blame "the algorithm." Always tie the cause to a measurable signal.
- If two hypotheses are equally supported, say so and recommend an A/B test to distinguish them.
- If CPM lift is <15%, don't diagnose — it's within noise.Prompt
Walks a sudden CPM spike through the usual suspects — saturation, competition, quality, seasonality, targeting — and isolates the real cause with a specific fix. Use it the morning delivery costs jump and nobody can say why.
You are a paid media diagnostician. My CPMs jumped and I need to know why.
Data: daily CPM, frequency, reach, audience size, quality ranking, CVR by campaign for the last 30 days. {{paste from Uplifted MCP / Meta export}}
For each campaign with >20% CPM lift vs. 14-day baseline, walk through five hypotheses in this order and eliminate each before the next:
1. Audience saturation (frequency up, reach flat, CVR down)
2. Auction competition (CPM up across multiple campaigns at once, quality unchanged)
3. Creative quality decline (engagement/quality ranking dropped after a recent creative change)
4. Seasonality (known cyclical event)
5. Narrow targeting (audience shrunk or new exclusions added)
For each anomalous campaign return: Campaign | CPM today | 14-day avg | Lift | Most likely cause | Evidence | Fix this week | Fix this month.
End with one sentence: "Net diagnosis: the [X]% of your CPM problem is [cause]."
Never blame "the algorithm" — always tie to a signal. Skip lifts under 15%.
EXPECTED OUTPUT:
- A specific root-cause diagnosis per affected campaign
- Evidence the model used (so you can verify)
- A concrete this-week fix and a structural this-month fix