Setup

Read and follow ../shared/preamble.md (MCP detection, account selection) and ../shared/analysis-principles.md (evidence requirement, guardrails). Both apply throughout this skill — every dimension below is a measurement, not an opinion.

Landing Page Scoring + Diagnostic

Google Ads campaigns fail on the landing page more often than in the auction. A great RSA that sends traffic to a slow, unfocused, or mismatched page burns budget twice — once on the click, once on the lost conversion. This skill scores landing pages on 5 weighted dimensions and emits concrete fixes.

Only score pages that actually run ad traffic. Don't score random marketing pages. Run this on direct request, on auto-handoff from /google-ads-audit (high-CTR / low-CVR ad groups), when QS diagnosis flags "Landing Page Experience: Below Average", or as a preflight before /google-ads-copy writes new copy for a page nobody's validated.

When the question is about ad-to-page fit, high CTR / low CVR, LPX, or testing ads and landing pages together, read references/message-chain-testing.md before scoring. It keeps the diagnosis focused on the paid-search message chain instead of drifting into a generic web-design audit.

Reference

• references/scoring-rubric.md — the 5-dimension weighted rubric, thresholds, and evidence fields. Read before scoring.
• references/message-chain-testing.md — query → ad → page message-chain diagnosis and ad+LP test design.
• ../manage/references/quality-score-framework.md — only when the user's explicit goal is QS improvement.

Phase 1: Resolve the target pages

Figure out which URLs to score. In priority order:

1. User supplied a URL — score that page, skip discovery.
2. User supplied an ad group or campaign name — runScript a GAQL query against ad_group_ad filtered to that ad group; extract unique final_urls. Normalize (strip tracking params, preserve path + query that affects routing).
3. Auto-handoff from /google-ads-audit — the handoff passes the specific ad groups flagged. Pull their final URLs the same way.
4. No arguments — runScript an ad_group_ad query across the account ranking final URLs by last-30-day spend, propose the top 3, ask the user to confirm.

De-duplicate aggressively. Many ads point to the same final URL — score each unique URL once, then map back to every ad group that uses it.

Phase 2: Gather signal (parallel)

Do all of these in a single tool-use turn:

1. WebFetch the landing page — capture visible headline, subheadline, primary CTA text, form fields, trust signals, body copy tone. Capture the full HTML so we can spot script bloat and above-the-fold content.
2. PageSpeed Insights API call — https://www.googleapis.com/pagespeedonline/v5/runPagespeed?url={url}&strategy=mobile&category=performance&category=accessibility&category=best-practices&category=seo via WebFetch. No API key needed for single-URL queries. Extract LCP, CLS, INP, TTI, performance score, and the top 3 opportunities from lighthouseResult.audits.
3. Pull the referring ad copy and the ad group's conversion metrics — one runScript call with ads.gaqlParallel against ad_group_ad (for headline/description text — the message-match baseline) and ad_group or keyword_view (for clicks, conversions, CVR — used to ground the dollar-impact estimate). One call covers both.
4. Read {data_dir}/business-context.json — for brand voice, differentiators, offers, target audience. If missing, point the user to /google-ads-audit first. Don't guess the business.

If any single call fails, continue — note the gap in the report rather than blocking. PageSpeed Insights can rate-limit; if it does, fall back to a manual timing annotation ("PSI unavailable — could not score Page Speed") and deflate the final report's confidence rather than skipping the dimension.

Phase 3: Score the page

Read references/scoring-rubric.md and score each dimension 0-100 with evidence. The dimension scores are real measurements (PageSpeed Insights numbers, word-for-word copy comparison, form field counts, etc.) — they're not artificial ratings, they're observations.

Compute the weighted composite only as an internal reference number for the dollar-lift formula below. Do not surface it as a letter grade. The user sees the dimension-level measurements and the estimated dollar lift — the composite is plumbing.

internal_composite = 0.25 * Message Match
                   + 0.25 * Page Speed
                   + 0.20 * Mobile Experience
                   + 0.15 * Trust Signals
                   + 0.15 * Form & CTA

Dollar lift is the headline. If business-context.json.unit_economics has aov_usd + profit_margin, compute the estimated monthly lift from raising the composite by 15 points (see ../shared/ppc-math.md):

Target lift           = min(+15, 90 - internal_composite)    # cap at 90 internal
Assumed CVR lift      = target_lift / 100 * 0.5              # cap at 50% relative lift
Current conversions   = ad group conversions from last 30d
Additional conversions = current_conversions * assumed_CVR_lift
Additional revenue    = additional_conversions * AOV
Additional profit     = additional_conversions * AOV * profit_margin

Present the lift as fixing this page is worth ~$X/mo in profit — never as a guarantee. The 50% cap on CVR lift and the 15-point cap on score improvement keep estimates out of fantasy territory. If unit_economics isn't available, skip the dollar line entirely rather than making up a number — the dimension measurements still stand on their own.

Phase 4: Deliver the report

Max 60 lines. Lead with the dollar lift (when available) and the single biggest fix. No letter grade.

# Landing Page — [URL]
Ads sending traffic here: [N ad groups] · [X clicks/mo] · [$Y spent/mo] · CVR [Z%]
[If unit_economics available] **Estimated lift from top 3 fixes: ~$X/mo in profit**
[If unit_economics is missing] _(Dollar lift unavailable — no verified AOV/margin. Confirm unit economics in business-context.json for sharper estimates.)_

**Biggest leak:** [one sentence naming the dimension and the specific observation, e.g. "LCP is 5.8s on mobile — 2.8s slower than the 3s threshold that kills conversion rate."]

## Measurements
| Dimension | Measurement | Top Finding |
|-----------|-------------|-------------|
| Message Match | [word-for-word verdict: Match / Drift / Broken] | [one line citing ad H1 vs page H1] |
| Page Speed | LCP Xs · INP Xms · CLS X · PSI perf score X | [top blocking audit from Lighthouse] |
| Mobile Experience | PSI accessibility X · [mobile-specific issue count] | [one line: e.g. "No click-to-call, form below fold"] |
| Trust Signals | [review count, years in business, cert count] | [one line: e.g. "Zero named testimonials, copyright 2023"] |
| Form & CTA | [field count] fields · CTA text: "[button]" · [above/below fold] | [one line: e.g. "11 fields for a free quote"] |

## Fix First (top 3, ranked by estimated $ lift)
1. **[Action]** — est. +$X/mo · `<time_to_fix>`
   Evidence: [the actual text/number from the page or PSI audit]
2. **[Action]** — est. +$X/mo · `<time_to_fix>`
   Evidence: [...]
3. **[Action]** — est. +$X/mo · `<time_to_fix>`
   Evidence: [...]

## Message Match Detail
Ad headline: "[actual headline from top-spending ad]"
Page H1:    "[actual H1 from landing page]"
Observation: [Match / Drift / Broken] — [one-line rationale citing the specific words that match or don't]

## Handoff
[Pick one:]
- Page speed dominates the problem → "Share these fixes with your developer: [list]"
- Message mismatch dominates → "Run /google-ads-copy to rewrite ads to match the page, or update the page to match the ads"
- Form friction dominates → "Reduce form to [specific fields]. Every removed field is ~10% more conversions"

Writing back to history

Append the score to {data_dir}/landing-page-history.json so re-audits can show deltas: