landing-page-auditor
Audits any landing or service page across 48 checks in 10 categories for LLM/AI discoverability, GEO readiness, content clarity, schema markup, internal linking, freshness signals, and technical crawlability. Produces an overall score out of 100, a per-category score out of 100, pass/warn/fail/ manual counts, and a prioritised fix list — all rendered as a standalone HTML report saved to disk. Use when a user provides a URL and asks to audit it, score it, check its GEO readiness, test its AI discoverability, run a landing page audit, or evaluate it for LLM citation potential. Also trigger on ph
npx skills add Infrasity-Labs/dev-gtm-claude-skills --skill landing-page-auditor --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Audits landing or service pages across 48 checks in 10 categories, given a URL, fetches data, grades each check, computes category and overall scores, and renders a standalone HTML report saved to disk. It supports scoring, GEO readiness, AI-discoverability checks, and LLM citation evaluation.
How it works
Step 0 — Parse and Confirm the URL: If a URL is provided in the invocation, use it; normalize to https://, strip trailing slashes, preserve full path for filename, and extract bare domain and domain root. Echo the resolved URL.
Step 1 — Fetch All Required Data in Parallel: perform 1a through 1f fetches concurrently: 1a — Page Content Overview: request complete page text including headings, paragraphs, lists, specific body text slices, FAQ/testimonials/pricing/stats, audience language, comparison language, negative scoping, internal/external links, dates, breadcrumbs, and comparison tables. 1b — Page Schema and Technical: request JSON-LD blocks, image tags (src and alt), meta title, meta description, canonical URL, robots meta, BreadcrumbList, specific schema types (Service/ProfessionalService/SoftwareApplication/Product/FAQPage), and visibility of content in raw HTML. 1c — robots.txt: fetch content listing all user-agent rules and sitemap directive. 1d — sitemap.xml: check for target URL presence, with lastmod, changefreq, priority; report total URLs; follow sub-sitemaps if needed. 1e — llms.txt: fetch full content, list all URLs/paths mentioned, and check presence of target path or full URL. 1f — Server Response Time: measure TTFB using curl with specified command; if unavailable, mark as warning.
Step 2 — Grade All 48 Checks: assign one of Pass, Warning, Fail, or Manual with evidence note for each check; weights determine overall score per category.
When to use it
Use when a user provides a URL and asks to audit it, score it, check GEO readiness, test AI discoverability, run a landing page audit, or evaluate it for LLM citation potential. Also trigger on phrases like “audit this page”, “score this URL”, “check GEO for”, etc.
What it can touch
The skill uses the Claude Code tool to perform fetches and analysis as described in Step 1. It touches page content, schema, robots, sitemap, llms.txt, and response time data as part of data collection for scoring.
Caveats
The review reflects the explicit steps and data collection prompts defined in the skill; outcomes depend on the fetched content and cannot assume results beyond stated checks. License: MIT.
# Landing Page GEO & LLM Auditor Audits any landing or service page across 48 checks in 10 categories. Given a URL, fetch all required data, grade every check, calculate scores per category and overall, then render a standalone HTML report. --- ## Step 0 — Parse and Confirm the URL If the user provided a URL in the invocation arguments, use it directly. If not, ask: > "Please provide the URL of the page you'd like to audit." **Normalisation rules:** - Ensure the URL starts with `https://` - Strip trailing slashes - Keep the full path (e.g. `https://infrasity.com/claude-skills`, not just `infrasity.com`) - Extract the bare domain for file naming (e.g. `infrasity.com`) - Extract the domain root for auxiliary files (strip subdomains only if they differ — check both) Echo back the resolved URL before proceeding: > "Auditing `{URL}` — fetching data now..." --- ## Step 1 — Fetch All Required Data in Parallel Run ALL of the following fetches simultaneously. Do not wait for one to complete before starting the next. ### 1a — Page Content Overview (WebFetch pass 1) Fetch `{URL}` with this prompt: > "Return the complete text content of the page. Include: all headings (H1, H2, H3, H4) with the
- Step 0 — Parse and Confirm the URL
- Step 1 — Fetch All Required Data in Parallel
- 1a — Page Content Overview (WebFetch pass 1)
- 1b — Page Schema and Technical (WebFetch pass 2)
- 1c — robots.txt (WebFetch)
- 1d — sitemap.xml (WebFetch)
- 1e — llms.txt (WebFetch)
- 1f — Server Response Time (Bash / curl)
- Step 2 — Grade All 48 Checks
- Category 1 — Content Clarity & Directness (5 checks)
- Category 2 — Structure & Scannability (5 checks)
- Category 3 — Entity & Fact Signal (5 checks)
- Category 4 — Trust & Authority Signals (4 checks)
- Category 5 — FAQ & Schema Signals (5 checks)
echo "TTFB: ${RESPONSE_MS}ms"What does the landing-page-auditor skill do?
Audits any landing or service page across 48 checks in 10 categories for LLM/AI discoverability, GEO readiness, content clarity, schema markup, internal linking, freshness signals, and technical crawlability. Produces an overall score out of 100, a per-category score out of 100, pass/warn/fail/ manual counts, and a prioritised fix list — all rendered as a standalone HTML report saved to disk. Use when a user provides a URL and asks to audit it, score it, check its GEO readiness, test its AI discoverability, run a landing page audit, or evaluate it for LLM citation potential. Also trigger on ph
How do I install it?
Run `npx skills add Infrasity-Labs/dev-gtm-claude-skills --skill landing-page-auditor --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From Infrasity-Labs/dev-gtm-claude-skills, a repository with 97 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.