Kelpi

Quality Score Fixer

Match intent to the pageLive reads need a Google Ads MCP

Use Google Ads Quality Score components to find the weakest link behind a valuable keyword and draft the smallest useful test. Use when relevance, CTR, or landing-page experience may be holding Search back.

Install this skill

npx skills add kelpi-ai/google-ads-skills --skill quality-score-fixer

Install it with the open skills CLI, copy the whole file, or paste the prompt into a Claude conversation. No Kelpi account is needed.

View source on GitHub

Why this skill exists

Quality Score is a diagnostic, not a KPI and not a direct auction input. The useful part is the three component ratings: expected click-through rate, ad relevance, and landing-page experience. Diagnose the component, weigh it against real business performance, and test the smallest credible fix. Do not chase a prettier 1-to-10 number at the expense of conversions.

When to use it

  • When valuable keywords show below-average Quality Score components or weak Search performance.
  • Use a connected Google Ads MCP, or paste keyword metrics, component ratings, triggered queries, current ads, and landing-page copy.
  • Bring the primary conversion, target CPA or ROAS, conversion delay, and downstream quality where available.

Run it

Paste an export, or use a Google Ads MCP for live reads.

The copy button includes the task and expected output format, ready for Claude.

Run a READ-ONLY Quality Score diagnosis. Use live Google Ads data if connected; otherwise use what I paste. Change nothing.

1. Confirm the date range, primary conversion, target, and keyword-level spend, clicks, conversions, value, Quality Score, expected CTR, ad relevance, and landing-page experience.
2. Prioritize commercially important keywords with below-average components. Do not rank work by the 1-to-10 score alone.
3. For each diagnosis show: weak component, business importance, observed evidence, competing evidence, root-cause hypothesis, confidence, smallest useful test, and success measure.
4. Use triggered queries, current ad assets, and page copy to test message match before proposing a rewrite.
5. Keep fixes component-specific:
   - expected CTR: query fit and distinct, credible ad assets;
   - ad relevance: tighter alignment among query, keyword, ad group, and ad promise;
   - landing-page experience: message match, usefulness, usability, speed evidence, and one clear next step.
6. Measure both the component rating and business results after enough data and conversion delay. A component can improve while CPA gets worse.
7. If asked for savings, say they cannot be estimated from Quality Score alone. Only calculate a scenario from user-supplied CPC, traffic, conversion, and validated assumptions, labeled as a scenario rather than a forecast.

Do not change bids, keywords, ads, or pages. Do not promise a future score, rank, CPC reduction, or savings. End with "No changes were made."

Guardrails

  • Quality Score helps diagnose user experience. It is not a business outcome and the displayed score is not a direct auction input.
  • Never pause a profitable or strategically valuable keyword only because its score is low.
  • A below-average component points to where to investigate, not the exact root cause.
  • Do not claim that moving from one score to another cuts CPC by a fixed percentage.
  • Fix message and experience problems before treating a higher bid as the answer.

Good output looks like

A short list of component-level diagnoses tied to valuable keywords, each with evidence, one test, and an honest success measure. No fake savings forecast and no bid change.

Use it yourself, or let Kelpi keep it moving

Kelpi carries the query, ad, and page context together so each recommendation stays tied to the promise the customer actually sees. The skill stays free either way.

Try Kelpi

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