Content Quality
23%Depth, E-E-A-T, citations, author trust, thin pages, and whether the page can stand as the best result.
Platform features
ClaudeSkill SEO does not spend the report teaching basic SEO vocabulary. It scores the page and site, identifies the weighted categories dragging visibility down, and turns the findings into a severity-ranked fix plan for SEO, marketing, and engineering.
Each audit produces a 0-100 SEO Health Score from seven weighted categories. The weights prioritize the work most likely to affect rankings, mobile-first search performance, AI citation readiness, and trust signals.
Depth, E-E-A-T, citations, author trust, thin pages, and whether the page can stand as the best result.
Crawlability, indexability, redirects, canonicals, robots, sitemap quality, status codes, and security headers.
Search intent fit, titles, headings, internal links, semantic coverage, and page-template consistency.
Valid JSON-LD, entity clarity, rich-result eligibility, and contradictions between visible copy and structured data.
Mobile-first LCP, INP, CLS, PageSpeed evidence, and Search Console or CrUX field data where configured.
llms.txt, AI crawler access, citable passages, brand/entity signals, and AI platform visibility gaps.
Alt text, screenshots, report previews, dimensions, compression, author photos, and visual proof for users.
A 72/100 score is not enough. The report separates urgent ranking blockers from backlog improvements so the next sprint starts with the highest impact work.
24-48 hours
Indexing blockers, severe trust gaps, broken public claims, or issues likely to suppress visibility now.
Within 1 week
Problems with strong ranking or conversion impact: CWV regressions, canonical drift, thin priority pages, missing llms.txt.
Within 1 month
Optimization work that improves topical depth, internal linking, image coverage, schema completeness, or AI citability.
Backlog
Nice-to-have improvements such as optional protocols, polish, and longer-term monitoring enhancements.
Google has used mobile-first indexing for years, so the audit treats mobile rendering, mobile UX, and Core Web Vitals as ranking-critical inputs. Performance checks focus on LCP, INP, and CLS instead of outdated FID language.
When Google APIs are configured, reports can include PageSpeed, Search Console, CrUX field data, index coverage, and organic performance context. Without those connections, the audit still inspects source, templates, rendering, and likely performance risks so teams know what to verify next.
AI visibility is no longer just FAQ copy. The audit checks whether AI crawlers can access the site, whether llms.txt exists, whether passages are long enough to cite, and whether brand/entity signals are strong enough for ChatGPT, Perplexity, Bing Copilot, and Google AI Overviews.
llms.txt gives AI systems a structured map of content, citation preferences, and brand context. It is not a ranking guarantee, but it helps search and AI teams make crawler access intentional instead of accidental.
The report is designed to become a working backlog. SEO leads can defend priorities, marketers can see trust and citation gaps, and developers can inspect technical evidence before changing templates.
Illustrative benchmark
Illustrative positioning graphic, not independently verified market benchmark data. It shows the product goal: scoring plus fix priority, not only crawling or rank tracking.
Schedule repeat audits after launches, migrations, content updates, and template changes. The goal is to catch the issues that quietly suppress search visibility: a canonical changing from www to non-www, missing security headers, Core Web Vitals moving from good to needs improvement, a blog cluster becoming thin, or AI crawler guidance returning a 404. The report history gives teams a record of what changed and what still needs action.