Generate SERP-overlap semantic topic clusters for hub-and-spoke content planning.
npx skills add https://github.com/avalonreset/gemini-seo --skill seo-cluster-avalonreset
Please help me install this Agent Skill. Skill: seo-cluster Source: https://github.com/avalonreset/gemini-seo/tree/main/skills/seo-cluster Command: npx skills add https://github.com/avalonreset/gemini-seo --skill seo-cluster-avalonreset
This Skill eliminates guesswork in content architecture by clustering keywords based on actual Google SERP overlap, so each page targets what search engines already treat as the same result intent.
Use the seo-cluster skill with a single seed keyword to generate a full cluster plan and an interactive cluster map for your content architecture.
High-intent search queries and answers about installing and using this skill.
SERP overlap clustering groups keywords by shared top-10 organic results to design page themes matching ranking reality rather than text similarity. This ensures each page targets what search engines treat as the same result intent for hub-and-spoke planning.
You can generate a full cluster plan from a single seed keyword by applying SERP clustering to build pillar-and-spoke structures. This includes intent classification, template selection, and cannibalization safeguards for unique primary keywords.
Generate a bidirectional internal link matrix by computing a pillar and spoke plan from SERP-overlap clusters. This produces an interactive cluster map and stakeholder-ready navigation based on actual search engine result intent overlap.
You need WebSearch or DataForSEO to satisfy SERP collection requirements for keyword grouping. These dependencies provide the actual top-10 organic results needed to compute semantic topic clusters for your content architecture.
SERP clustering applies to informational, commercial, and transactional intents within content calendars. It groups keywords by shared organic results to produce execution-ready briefs and pillar structures across multiple intent types.
SERP overlap groups keywords based on actual Google ranking reality rather than text similarity. This approach prevents keyword cannibalization by ensuring each page targets what search engines already treat as the same result intent.
shenxingyCluster seed keywords by shared SERP rankings into hub-and-spoke architectures with link matrices and HTML maps.
martingaam-hueSegment keywords into SERP-driven topic clusters and generate cluster maps.
itdevwork-gitGenerate SEO content briefs with word counts, competitor scoring, and keyword placement rules.
Im5tuGenerate structured content pillars, topic clusters, and production briefs.
