Cluster content topics by SERP overlap for content architecture planning.
npx skills add https://github.com/AgriciDaniel/codex-seo --skill seo-cluster-agricidaniel
Please help me install this Agent Skill. Skill: seo-cluster Source: https://github.com/AgriciDaniel/codex-seo/tree/main/skills/seo-cluster Command: npx skills add https://github.com/AgriciDaniel/codex-seo --skill seo-cluster-agricidaniel
SERP-based topic clustering enables content strategists to design scalable content architectures by grouping keywords according to actual Google SERP overlap rather than pure text similarity, enabling hub-and-spoke structures with robust internal linking and clear visualization.
Provide a seed keyword or URL to start clustering and see pillar/spoke plan generated.
High-intent search queries and answers about installing and using this skill.
SERP overlap clustering groups keywords by analyzing how many identical URLs rank across their Google search results, revealing actual search intent relationships. This method builds accurate hub-and-spoke content architectures with robust internal linking.
To create a hub-and-spoke architecture, input a seed keyword or URL to generate a cluster map. The workflow identifies pillar pages and supporting spokes, outputting an internal link matrix to connect the grouped topics.
SERP-based topic clustering supports SERP data ingestion from either DataForSEO or WebSearch. Providing this search result data is necessary to calculate URL overlap and generate the keyword groups for your content strategy.
You can generate content briefs or posts automatically after mapping SEO clusters when the optional codex-blog integration is installed. This extends the clustering workflow directly into content creation and execution.
The best way to plan internal links for a large content strategy is using SERP overlap-driven clustering to produce an internal link matrix. This maps pillar pages to supporting spokes based on actual search result page overlap.
SERP overlap clustering is used instead of text similarity because it groups keywords based on actual Google search results, reflecting true search intent. This approach ensures your content architecture matches how search engines categorize topics.
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