google-ads-landing

## 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 m