landing-page-audit

name: landing-page-auditdescription: "Audit one landing page on six conversion dimensions, incl. ad message match. \"why isn't this page converting\""argument-hint: "[URL]"

/digital-marketing-pro:landing-page-audit

Script location. If your host does not set ${CLAUDE_PLUGIN_ROOT}, the scripts are in this plugin's scripts/ folder, next to skills/. Run them with Python 3.10 or newer: python, or python3 where python is not found (macOS, many Linux systems).

Purpose

Evaluate a landing page across six key conversion dimensions and deliver a scored assessment with specific, actionable recommendations to improve conversion rate.

Input Required

The user must provide (or will be prompted for):

• Landing page URL: The page to audit
• Traffic source: Where visitors come from (paid search, social ads, email, organic)
• Target action: Desired conversion (form submit, purchase, signup, download, call)
• Ad copy or email: The upstream message driving traffic (for message match analysis)
• Current conversion rate: If known, for benchmarking

Process

1. Load brand context: Read ~/.claude-marketing/brands/_active-brand.json for the active slug, then load ~/.claude-marketing/brands/{slug}/profile.json. Apply brand voice, compliance rules for target markets (skills/context-engine/compliance-rules.md), and industry context. Also check for guidelines at ~/.claude-marketing/brands/{slug}/guidelines/_manifest.json — if present, load restrictions and relevant category files. Check for custom templates at ~/.claude-marketing/brands/{slug}/templates/. Check for agency SOPs at ~/.claude-marketing/sops/. If no brand exists, ask: "Set up a brand first (/digital-marketing-pro:brand-setup)?" — or proceed with defaults.
2. Above-fold clarity (score 1-10): Headline clarity, value proposition, visual hierarchy, CTA visibility within first viewport
3. Trust signals (score 1-10): Social proof, testimonials, logos, security badges, guarantees, reviews
4. Form friction (score 1-10): Number of fields, field labels, error handling, progressive disclosure, mobile form UX
5. Message match (score 1-10): Alignment between traffic source (ad/email) and landing page headline, imagery, offer
6. Page speed (score 1-10): Load time, Core Web Vitals, render-blocking resources, image optimization
7. Mobile experience (score 1-10): Responsive design, tap targets, scroll depth, mobile-specific CTAs
8. Calculate overall score and benchmark against industry averages
9. Prioritize recommendations by expected conversion impact

How you'll know it worked

Every recommendation gets a metric it should move, the direction it should move in, and a date to check it. Add this table to the report, one row per recommendation:

Then record the rows so the continuous-improvement loop checks them when they fall due:

python "${CLAUDE_PLUGIN_ROOT}/scripts/recommendation-tracker.py" --brand {slug} --action add --source landing-page-audit --data '[{"recommendation": "...", "metric": "...", "direction": "up", "check_by": "YYYY-MM-DD"}]'

A recommendation without a metric, a direction (up or down) and a future check-by date is refused (exit 2): if you can't say how you'd know it worked, rewrite it until you can.

Output

A structured landing page audit containing:

• Overall conversion score (1-10) with industry benchmark comparison
• Dimension-by-dimension scoring with evidence and screenshots/notes
• Top 5 priority fixes ranked by expected impact
• Detailed recommendations per dimension with implementation guidance
• Message match analysis with specific misalignment callouts
• Mobile-specific issues and fixes
• Quick wins vs. major redesign items

Agents Used

• analytics-analyst — Performance scoring, conversion benchmarking, data-driven recommendations
• brand-guardian — Brand consistency, trust signal assessment, message alignment
• cro-specialist — Conversion scoring, form friction analysis, A/B test sample size calculation, above-fold hierarchy, CTA optimization