Agent skill

Google Ads Landing Review

by TheMattBerman in TheMattBerman/google-ads-copilot

Diagnose landing page → conversion path problems for Google Ads traffic.

MITAuto-check passedMarketing & SEO

Install Google Ads Landing Review

skills CLI
$ npx skills add TheMattBerman/google-ads-copilot --skill google-ads-landing-review -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install TheMattBerman/google-ads-copilot google-ads-landing-review --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/TheMattBerman/google-ads-copilot.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/google-ads-landing-review .claude/skills/google-ads-landing-review && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
google-ads-landing-review
GitHub stars
238
Token cost
~4.1k tokens
SKILL.md length
1,505 words
Files
1
Skills in repo
16
Repo updated
First seen
Licence
MIT

At a glance

Diagnose landing page → conversion path problems for Google Ads traffic.

  • Works in 2 steps: Tracking problem — conversions ARE… → Path problem — the visitor arrives but…
  • Tasks that involve Paid advertising
  • SKILL.md covers Why this skill exists, Diagnostic Model: Two Forks, Fork A: Tracking Diagnosis (Is… and Fork B: Path/UX Diagnosis (Is…, plus 5 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Google Ads Landing Review is an agent skill from TheMattBerman/google-ads-copilot. Diagnose landing page → conversion path problems for Google Ads traffic. Separates tracking failures from UX/path failures — the two most commonly confused sources of "low conversion rate." Pulls ad and campaign data via MCP, then reviews landing pages via browser or URL fetch to assess message match, load issues, form friction, and conversion path completeness. Produces a landing-review draft when problems are found.

Its SKILL.md is about 4.1k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Marketing & SEO, covering Paid advertising and Landing pages. It works with Google Ads and Model Context Protocol. The repository describes itself as: Google Ads Copilot: operator kit for audits, MCP-connected reads, export-mode analysis, and draft/apply workflows. The licence is MIT.

When your agent uses it

  • Tasks that involve Paid advertising
  • Tasks that involve Landing pages

Example prompts

  • “low conversion rate.”
  • “/google-ads-landing-review”

Workflow steps

2 steps, taken from the first numbered list in SKILL.md.

  1. Tracking problem — conversions ARE happening but aren't being counted.
  2. Path problem — the visitor arrives but the page fails them.

What it can do on your machine

Read from SKILL.md and the folder at commit 2c253ee. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are sql and markdown).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Google Ads Landing Review loads about 4.1k tokens when it runs. Until then it costs about 112 tokens; SKILL.md has 1,505 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~112
When it runs · the whole SKILL.md, loaded when a task matches
~4.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from TheMattBerman/google-ads-copilot at commit 2c253ee, republished under its MIT licence (© TheMattBerman). 1,505 words, ~4,052 tokens.

Download SKILL.mdSave it as .claude/skills/google-ads-landing-review/SKILL.md (or your agent's skills folder).
name
google-ads-landing-review
description
Diagnose landing page → conversion path problems for Google Ads traffic. Separates tracking failures from UX/path failures — the two most commonly confused sources of "low conversion rate." Pulls ad and campaign data via MCP, then reviews landing pages via browser or URL fetch to assess message match, load issues, form friction, and conversion path completeness. Produces a landing-review draft when problems are found.
argument-hint
[campaign name or URL]

Google Ads Landing Review

Why this skill exists

"The landing page isn't converting" is the most common complaint in Google Ads. But it hides two completely different root causes:

  1. Tracking problem — conversions ARE happening but aren't being counted.
  2. Path problem — the visitor arrives but the page fails them.

Most operators (and most clients) assume path failure when the numbers look bad. Half the time, it's a tracking failure. Conflating the two wastes months.

This skill separates them.

Read first:

  • google-ads/references/operator-thesis.md
  • google-ads/references/tracking-playbook.md
  • google-ads/references/structure-playbook.md

Read workspace if available:

  • workspace/ads/account.md
  • workspace/ads/goals.md
  • workspace/ads/findings.md
  • workspace/ads/drafts/_index.md — check for existing tracking drafts

Diagnostic Model: Two Forks

"Landing page isn't converting"
         │
    ┌────┴────┐
    │         │
  FORK A    FORK B
  Tracking  Path/UX
    │         │
  Is the     Is the page
  signal     actually
  correct?   failing?

Always run Fork A first. If tracking is broken, Fork B conclusions are unreliable.


Fork A: Tracking Diagnosis (Is the signal trustworthy?)

What to check

1. Does a conversion action exist for the goal on this page?

  • Is there a conversion action configured for the form/call/purchase that this landing page is supposed to produce?
  • Is it set as primary (include_in_conversions_metric = TRUE)?
  • Is the counting type correct?

2. Does the tag actually fire?

  • Is the Google Ads conversion tag (or GA4 event imported to Google Ads) present on the confirmation/thank-you page?
  • Does the tag fire when the user completes the action? (Google Tag Assistant, network tab, or manual test)
  • Is the tag firing on the WRONG page? (e.g., firing on page load of the form page instead of the thank-you page)

3. Is the conversion path end-to-end intact?

  • Click on ad → landing page → form/CTA → thank-you page → tag fires → conversion recorded
  • Where does the chain break?

4. Cross-domain / redirect issues?

  • Does the landing page redirect to a different domain for the form/checkout?
  • If so, is cross-domain tracking configured?
  • Are UTM parameters / GCLID surviving the redirect?

5. Auto-tagging?

  • Is auto-tagging enabled?
  • Are there URL parameters being stripped by the landing page CMS or CDN?

6. Attribution window?

  • Conversion window too short (e.g., 1 day for a B2B lead that takes a week to decide)?
  • Multiple touchpoints lost?
Fork A Data Acquisition (Connected Mode)

Conversion actions for this campaign:

sql
SELECT
  conversion_action.name,
  conversion_action.type,
  conversion_action.category,
  conversion_action.counting_type,
  conversion_action.include_in_conversions_metric,
  conversion_action.status,
  metrics.conversions,
  metrics.all_conversions
FROM conversion_action
WHERE segments.date DURING LAST_30_DAYS
  AND conversion_action.status = 'ENABLED'
ORDER BY metrics.conversions DESC

Campaign-level conversion data:

sql
SELECT
  campaign.name,
  campaign.final_url_suffix,
  metrics.clicks,
  metrics.conversions,
  metrics.all_conversions,
  metrics.cost_micros,
  metrics.cost_per_conversion
FROM campaign
WHERE campaign.status = 'ENABLED'
  AND segments.date DURING LAST_30_DAYS
ORDER BY metrics.clicks DESC

Ad-level landing page URLs and performance:

sql
SELECT
  campaign.name,
  ad_group.name,
  ad_group_ad.ad.final_urls,
  ad_group_ad.ad.type,
  metrics.clicks,
  metrics.impressions,
  metrics.conversions,
  metrics.cost_micros
FROM ad_group_ad
WHERE campaign.status = 'ENABLED'
  AND ad_group_ad.status = 'ENABLED'
  AND segments.date DURING LAST_30_DAYS
ORDER BY metrics.clicks DESC
LIMIT 50

Landing page experience (quality score indicators):

sql
SELECT
  campaign.name,
  ad_group.name,
  ad_group_criterion.keyword.text,
  ad_group_criterion.quality_info.quality_score,
  ad_group_criterion.quality_info.post_click_quality_score,
  ad_group_criterion.quality_info.creative_quality_score,
  ad_group_criterion.quality_info.search_predicted_ctr
FROM keyword_view
WHERE campaign.status = 'ENABLED'
  AND ad_group.status = 'ENABLED'
  AND ad_group_criterion.status = 'ENABLED'
ORDER BY ad_group_criterion.quality_info.post_click_quality_score ASC
LIMIT 50
Fork A Verdict
Tracking StatusMeaningNext Step
CleanTag fires correctly, conversion action configured right, GCLID passesProceed to Fork B
SuspiciousTag exists but volume seems too low or too high vs. realityInvestigate specific break, then Fork B
BrokenNo tag, wrong tag, or GCLID strippedFix tracking FIRST. Fork B is premature.
UnknownCan't verify from API alone — needs manual tag inspectionRecommend Tag Assistant audit, then Fork B

Fork B: Path/UX Diagnosis (Is the page actually failing?)

Only meaningful if Fork A shows tracking is Clean or Suspicious.

The Message Match Test

1. Search intent → Ad promise → Landing page delivery

The #1 conversion killer in Google Ads is message mismatch:

  • User searches "roll-off dumpster rental near me" (specific, purchase-intent)
  • Ad says "Container Solutions for Your Business" (vague)
  • Landing page is a generic homepage with 12 menu items

Each handoff is a potential drop:

HandoffQuestionFailure Mode
Search → AdDoes the ad answer the specific search?Generic ad for specific intent
Ad → LandingDoes the LP deliver what the ad promised?"Request a Quote" ad → page with no form
Landing → CTAIs the CTA visible, clear, and low-friction?Form buried below fold, 15 fields
CTA → CompletionCan the user actually complete the action?Broken form, redirect fails, captcha blocks

2. Specific checks (via browser or URL fetch):

  • Above-the-fold message: Does the H1/hero text match the ad's promise? Does it match the search intent?
  • CTA visibility: Can the user see what to do within 3 seconds? Is the CTA above the fold?
  • Form friction: How many fields? Required fields? Captcha? Multi-step?
  • Mobile experience: Does it work on mobile? (Most Google Ads clicks are mobile)
  • Page speed: Does it load in <3 seconds? (Slow = bounced)
  • Trust signals: Phone number, reviews, certifications, real photos?
  • Specificity: Does the page serve ONE intent, or is it a homepage trying to serve all intents?
The Intent Routing Test

3. Are different intent classes landing on different pages?

Intent ClassShould Land OnCommon Failure
Buyer ("buy X now")Product/service page with CTAHomepage
Comparison ("X vs Y")Comparison contentProduct page with no comparison
Research ("how does X work")Educational contentSales page
Local ("X near me")Location/service area pageNational homepage
Brand ("company name")Homepage or brand pageGeneric product page

If multiple intent classes all route to the same generic page, that's a structure problem (→ recommend in structure draft), not a landing page problem.

The Conversion Path Walk

4. Walk the actual path the user takes:

Click on ad
  → Landing page loads (check: speed, mobile rendering)
    → User reads headline (check: message match)
      → User finds CTA (check: visibility, clarity)
        → User clicks CTA (check: does it work?)
          → Form/checkout loads (check: friction, fields)
            → User submits (check: confirmation page loads)
              → Tag fires (check: conversion recorded)

Each step is a potential break. Document where the break is.

Fork B Scoring

Rate each dimension:

DimensionScoreNotes
Message match (search→ad→page)Strong / Weak / Missing
CTA clarityClear / Buried / Missing
Form frictionLow (≤4 fields) / Medium (5-8) / High (9+)
Mobile experienceGood / Adequate / Broken
Page speedFast (<3s) / Slow (3-6s) / Broken (>6s)
Trust signalsStrong / Some / None
Intent specificityFocused / Mixed / Generic
Conversion path completenessComplete / Partially broken / Broken

Data Acquisition — Landing Page Review

Connected Mode (MCP + Browser/Fetch)
  1. Pull ad final URLs and campaign data via GAQL (see queries above)
  2. For each unique landing URL:
    • Fetch with web_fetch for content analysis (H1, CTA text, form fields, page structure)
    • Use browser for interactive checks if needed (JavaScript-rendered pages, form testing)
    • Check mobile rendering if concerns arise
  3. Cross-reference landing pages against search term intent classes from workspace/ads/intent-map.md
Show full SKILL.md (598 more words)Show less
Export Mode

Ask the user for:

  • Landing page URL(s)
  • Which campaigns/ad groups point to which pages
  • Conversion action name and how it fires (page load, event, etc.)
  • Any known issues (form complaints, mobile problems)
  • Recent conversion volume (or "we don't know" — that's data too)

Differential Diagnosis Summary

After running both forks, produce a clear classification:

Scenario 1: Tracking Problem Masquerading as UX Problem

Symptoms: Low/zero conversions, but page looks fine, form works, users seem to engage Diagnosis: Tag not firing, wrong conversion action, GCLID stripped, cross-domain break Action: Fix tracking → then reassess conversion rate with clean data Draft type: Tracking fix (use drafts/templates/tracking-draft.md)

Scenario 2: UX/Path Problem (Tracking Is Fine)

Symptoms: Tracking verified clean, but conversion rate is genuinely low Diagnosis: Message mismatch, buried CTA, excessive form friction, wrong page for intent Action: Landing page improvements or intent routing changes Draft type: Landing review draft (use template below) and/or structure draft

Scenario 3: Both Problems

Symptoms: Tracking has issues AND the page has UX problems Diagnosis: Two independent failures compounding Action: Fix tracking first (P0), then address UX (P1) — in that order Draft types: Tracking fix draft + Landing review draft

Scenario 4: Traffic Quality Problem (Page and Tracking Are Fine)

Symptoms: Tracking clean, page is good, but conversion rate still low Diagnosis: The traffic is wrong — keywords matching wrong intent, broad match pulling junk, PMax sending Display/YouTube traffic to a Search landing page Action: Search terms analysis, negative keywords, or structure changes Draft type: Negative draft and/or structure draft (this is NOT a landing page problem)


Draft Output

Landing Review Draft

Trigger: Fork B finds meaningful path/UX issues (at least 2 dimensions scored Weak/Missing/Broken)

Create using the template below:

  • Write to workspace/ads/drafts/YYYY-MM-DD-[account-slug]-landing-review.md
  • Update workspace/ads/drafts/_index.md

If Fork A also finds issues, create a SEPARATE tracking fix draft — don't mix them.

Landing Review Draft Template
markdown
# Draft: Landing Page Review — [DATE]
Status: proposed
Skill: /google-ads landing-review
Account: [Customer ID / Name]

## Summary
[One paragraph: which pages were reviewed, the primary diagnosis (tracking vs path vs both),
and the highest-priority fix.]

## Diagnostic Classification
**Primary issue:** Tracking Problem | Path/UX Problem | Both | Traffic Quality Problem

## Fork A: Tracking Status
- **Conversion action:** [Name and status]
- **Tag status:** [Fires correctly / Suspicious / Broken / Unknown]
- **GCLID passing:** [Yes / No / Unknown]
- **Auto-tagging:** [Enabled / Disabled]
- **Verdict:** [Clean / Suspicious / Broken / Unknown]

## Fork B: Path/UX Assessment

### Page: [URL]
- **Campaigns pointing here:** [list]
- **Clicks (30d):** [N]
- **Conversions (30d):** [N]
- **Implied conversion rate:** [X%]

#### Scores
| Dimension | Score | Detail |
|-----------|-------|--------|
| Message match | [Strong/Weak/Missing] | [specific observation] |
| CTA clarity | [Clear/Buried/Missing] | [specific observation] |
| Form friction | [Low/Medium/High] | [field count, issues] |
| Mobile experience | [Good/Adequate/Broken] | [specific observation] |
| Page speed | [Fast/Slow/Broken] | [load time if available] |
| Trust signals | [Strong/Some/None] | [specific observation] |
| Intent specificity | [Focused/Mixed/Generic] | [specific observation] |
| Path completeness | [Complete/Partial/Broken] | [where it breaks] |

### Proposed Changes

#### Change 1: [Specific recommendation]
- **Current state:** [what's wrong]
- **Proposed state:** [what to do]
- **Expected impact:** [on conversion rate, quality score]
- **Risk:** [what could go wrong]
- **Priority:** P0 / P1 / P2

[repeat for each change]

## Intent Routing Assessment
- **Are different intent classes landing on the right pages?** [Yes / No — detail]
- **Should new landing pages be created?** [If so, for which intent classes]
- **Cross-reference with intent map:** [link to workspace/ads/intent-map.md findings]

## Dependencies
- [e.g., "Fix tracking (2026-03-15-acme-tracking-fix.md) before assessing conversion rate improvement"]

## Confidence
[High / Medium / Low] — [reasoning]

## Review
- [ ] Evidence checked
- [ ] Collateral risk checked
- [ ] Dependencies checked
- **Decision:** approve | defer | reject
- **Decision reason:** ____
- **Reviewed by:** ____
- **Reviewed on:** ____
- **Applied on:** ____
- **Notes:** ____
Always update workspace memory:
  • workspace/ads/findings.md — landing page diagnosis and classification
  • workspace/ads/learnings.md — what we learned about this account's conversion path
  • Update existing tracking drafts if Fork A reveals new tracking problems

Output Shape

  1. Account Status block — name, CID, mode, date range, tracking confidence
  2. Diagnostic classification — tracking problem vs path problem vs both vs traffic quality
  3. Fork A summary — tracking status for each page/campaign reviewed
  4. Fork B summary — path/UX scores for each landing page reviewed
  5. Differential diagnosis — which scenario applies (1, 2, 3, or 4)
  6. Prioritized recommendations — fix order matters (always tracking before UX)
  7. Drafts created — with file paths and summaries
  8. Memory updates

Rules

  • Always run Fork A first. If tracking is broken, do NOT produce detailed UX recommendations — they will be based on phantom conversion data.
  • Distinguish clearly: A page with a broken tag and 0% conversion rate is a tracking problem, not a UX problem. Say it explicitly.
  • Don't blame the landing page for traffic quality problems. If the keywords are sending the wrong people, the page can be perfect and still not convert. Route to negatives/structure skills.
  • Walk the actual path. Don't just look at the page — follow the entire click → conversion chain.
  • Be specific. "The form has too many fields" is vague. "The form has 12 required fields including 'company size' and 'annual revenue' which are unnecessary for an initial quote request" is useful.
  • Mobile first. Most Google Ads clicks are mobile. If you can only check one thing, check mobile.
  • One page at a time. Don't try to review 10 pages at once. Start with the highest-spend page.
  • Message match is almost always the problem. When in doubt, check whether the H1 matches the search intent. If there's a mismatch, that's usually the answer.

© TheMattBerman, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/google-ads-landing-review of TheMattBerman/google-ads-copilot.

Open the folder on GitHubat commit 2c253ee

Compare with similar skills

Google Ads Landing Review next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Google Ads Landing Review compared with similar skills
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Brand Kitcognyai/claude-code-marketing-skills104—~3.4kAutomated safety check: NotesNone
Ad Extension Auditirinabuht12-oss/marketing-skills4.1k—~708Automated safety check: PassNone
Quality Score Breakdownirinabuht12-oss/marketing-skills4.1k—~688Automated safety check: PassNone
03 Performance Eval Globalminhnv0807/ai-business-skills609—~3.5kAutomated safety check: PassMIT

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Questions about Google Ads Landing Review

What does Google Ads Landing Review do?

Diagnose landing page → conversion path problems for Google Ads traffic. Google Ads Landing Review is an agent skill from TheMattBerman/google-ads-copilot. Diagnose landing page → conversion path problems for Google Ads traffic.

When should I use Google Ads Landing Review?

Google Ads Landing Review fits situations like: tasks that involve Paid advertising; tasks that involve Landing pages.

How do I install Google Ads Landing Review in Claude Code?

Run `npx skills add TheMattBerman/google-ads-copilot --skill google-ads-landing-review -a claude-code`. Or copy the skill folder (skills/google-ads-landing-review in TheMattBerman/google-ads-copilot) into .claude/skills/google-ads-landing-review in your project. Claude Code loads it when a task matches its description.

How do I install Google Ads Landing Review in Codex?

Run `npx skills add TheMattBerman/google-ads-copilot --skill google-ads-landing-review -a codex`. Or copy the skill folder (skills/google-ads-landing-review in TheMattBerman/google-ads-copilot) into .agents/skills/google-ads-landing-review in your project. Codex loads it when a task matches its description.

Can I use Google Ads Landing Review in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add TheMattBerman/google-ads-copilot --skill google-ads-landing-review -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/google-ads-landing-review, .gemini/skills/google-ads-landing-review, .github/skills/google-ads-landing-review and .opencode/skills/google-ads-landing-review in your project.

What does Google Ads Landing Review need to run?

SKILL.md names no scripts, command-line tools or credentials: Google Ads Landing Review is instructions for the agent only.

Does Google Ads Landing Review access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Google Ads Landing Review safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Google Ads Landing Review use?

Google Ads Landing Review is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Google Ads Landing Review use?

About 4.1k tokens (SKILL.md is roughly 16k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Google Ads Landing Review?

Skills that share tags, products or a category with Google Ads Landing Review: Google Ads Landing Page Audit (nowork-studio/notfair-plugin, 3.9k stars), Brand Kit (cognyai/claude-code-marketing-skills, 104 stars), Ad Extension Audit (irinabuht12-oss/marketing-skills, 4.1k stars) and Quality Score Breakdown (irinabuht12-oss/marketing-skills, 4.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Google Ads Landing Review?

TheMattBerman (a GitHub user) maintains it in TheMattBerman/google-ads-copilot, which has 238 GitHub stars. The repository holds 16 skills in this directory. The repository was last updated on June 10, 2026.

Source: TheMattBerman/google-ads-copilot on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.