seo-cluster

SERP overlap clustering groups keywords by shared top-10 organic results rather than text similarity. It transforms scattered keywords into hub-and-spoke content clusters that match how Google ranks pages, preventing keyword cannibalization.

You can generate hub-and-spoke content clusters by running a seed keyword through SERP-based semantic clustering. The process selects pillar keywords, assigns spoke clusters, and defines templates with word-count targets for your content architecture.

SERP data collection requires WebSearch or DataForSEO when available. The Skill uses this data to classify search intent and group keywords by actual organic result overlap, ensuring clusters reflect real ranking behavior.

Internal link matrix generation creates a bidirectional link plan between pillar and spoke pages. It maps the hub-and-spoke relationships identified during keyword clustering to define the required internal linking structure for your content architecture.

You can output detailed content briefs after SERP clustering, or execute automated drafting if the claude-blog tool is installed. Without it, the Skill provides comprehensive briefs to guide manual content creation.

Grouping by SERP overlap reflects how Google ranks results together, ensuring your content architecture matches actual search intent. Text similarity grouping often misses semantic relevance, leading to keyword cannibalization and poorly targeted pages.