Topic clusters
See every topic a site covers, how much of the site each takes up, and where the gaps are, straight from its sitemap.
What it shows
Topic clusters maps a site's content into topics by reading its own sitemap, which it finds through robots.txt. For each topic you get its share of the site, the page that most likely acts as its pillar, and example pages.
It answers questions like: what does this site actually write about, which topics does it cover in depth, and which only get a page or two?
Where to find it: Topic Clusters in the dashboard, and the topic_clusters tool in your AI assistant. It is free: it reads the site directly and uses no credits.
Why topics matter
Search engines and AI engines trust sites that cover a subject thoroughly. Ten connected pages about coffee brewing, linked from a strong guide, usually beat one long page on the same subject. This is the idea behind topic clusters: a broad pillar page that links to detailed pages on each subtopic, which link back.
Run it
- Enter a domain, such as
brewlab.coffee, or the address of a sitemap. - Read the topic map. The biggest areas are the topics the site invests in most.
If the tool cannot find a sitemap, check that your robots.txt has a Sitemap: line, or pass the sitemap address directly.
How to use the results
- Check your focus. If your biggest topic is not the one that makes you money, your content has drifted.
- Find thin topics. Topics with only one or two pages are candidates for a proper cluster. Plan three to five supporting pages and link them to a pillar.
- Check your pillars. Each important topic should have one page that covers it broadly and links to the details. If the suggested pillar is a random post, write a real one.
- Compare with a competitor. Run it on a competitor's site to see which topics they cover that you do not. Then confirm the demand with Content gap or Keyword research.
Limits
Topic clusters works from the sitemap, so pages missing from the sitemap are missing from the map. Very large sitemaps are sampled. The grouping is based on page addresses and titles, so a site with unclear addresses such as /p?id=123 gives a rougher map.
From your AI assistant
Try: "Map the topics on brewlab.coffee and beanhouse.com, and tell me which topics beanhouse covers that we do not." The topic_clusters tool accepts a site or a sitemap address and an optional number of example pages per topic.