--- name: nowork-studio/google-ads-landing source: https://app.decimal.ai/s/nowork-studio-google-ads-landing@1/SKILL.md source_sha256: e5636b37ea86 --- ## 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. +$X/mo · `` Evidence: [the actual text/number from the page or PSI audit] 2. **[Action]** — est. +$X/mo · `` Evidence: [...] 3. **[Action]** — est. +$X/mo · `` 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: ```json { "pages": { "https://example.com/services/roofing": { "history": [ { "date": "2026-04-14", "internal_composite": 67, "dimensions": { "message_match": 72, "page_speed": 45, "mobile": 80, "trust": 70, "form_cta": 65 }, "psi_mobile_lcp_s": 4.2, "psi_mobile_cls": 0.15, "psi_mobile_inp_ms": 320, "estimated_lift_usd_per_month": 380, "ad_groups": ["Example City Search - Roofing"], "monthly_spend": 1240.50, "monthly_cvr": 2.1, "biggest_leak": "Page Speed — LCP 4.2s on mobile" } ] } } } ``` `internal_composite` is stored for trend tracking only — it's the internal reference number used by the dollar-lift formula, never shown to the user as a letter grade. On subsequent runs against the same URL, diff the raw dimension measurements and the dollar lift: `LCP 4.2s → 2.1s · Page Speed 45 → 78 · estimated lift $380/mo → $120/mo remaining`. Three measurements moved, no artificial grade flip. ## Rules 1. **Never score a page without WebFetch'ing it.** The rubric demands evidence. No WebFetch = no score. Ask the user to help if the page is gated or requires auth. 2. **Never report a PSI number you didn't measure.** If PSI failed, say "PSI unavailable" — don't estimate. 3. **One page at a time unless the user asks for multiple.** Scoring three pages in one turn creates unreadable reports. Batch only when explicitly requested. 4. **Don't rewrite copy here.** This skill diagnoses the page. Handoff to `/google-ads-copy` for new headlines or `/google-ads` for bid/negative/budget moves. 5. **Margin-aware dollar impact requires verified unit economics.** If `unit_economics.source == "inferred_from_template"`, append `_(using industry defaults — confirm your AOV/margin for sharper estimates)_` to the lift line. 6. **Always persist.** Every scored page goes into `landing-page-history.json`, even if the user doesn't ask — future audits depend on the baseline.