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42-serp-cluster

Cluster keywords by overlapping SERP URLs into topic groups.

Updated Aug 27, 2026
Search Tags:#keyword-clustering#topic-clusters#serp#cannibalization#hub-pages#serp-cluster
One-click install
npx skills add https://github.com/chapter42/SEO-Skills-42 --skill 42-serp-cluster
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: 42-serp-cluster
Source: https://github.com/chapter42/SEO-Skills-42/tree/main/skills/42-serp-cluster
Command: npx skills add https://github.com/chapter42/SEO-Skills-42 --skill 42-serp-cluster

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

SERP clustering helps SEO professionals group hundreds or thousands of keywords into topic clusters by shared ranking URLs, reducing cannibalization and guiding hub-page strategy.

Core Features & Use Cases

  • Automatic clustering of keywords by overlapping top URLs to reveal topic groups.
  • Cannibalization detection and hub-page identification to optimize content plans.
  • Scalable for large keyword sets and adaptable to different top-N configurations.

Quick Start

Run python3 scripts/serp_cluster.py --input serp-data.csv --top-n 10 --output serp-clusters.json to generate clustering results.

Frequently Asked Questions about 42-serp-cluster

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I cluster keywords by SERP overlap to find topic groups?▼

Cluster keywords by SERP overlap by computing pairwise URL intersections from your keyword inventory, grouping keywords that share top ranking URLs to reveal distinct topic clusters and cannibalization risks.

What is SERP clustering and how does it detect keyword cannibalization?▼

SERP clustering groups keywords by shared ranking URLs to identify topic clusters. It detects keyword cannibalization by flagging multiple keywords targeting your site that rank for overlapping URLs, indicating competing pages.

How can I scale keyword clustering for a large SERP dataset?▼

Scale keyword clustering for large SERP datasets by processing a CSV input file and configuring the top-N ranking URLs to include, allowing the script to handle hundreds or thousands of keywords efficiently.

Does this keyword clustering approach support different grouping algorithms?▼

Yes, this keyword clustering approach supports three distinct clustering algorithms to group your keywords, allowing you to adapt the URL overlap analysis to different topical structures.

How do I identify hub pages from clustered SERP data?▼

Identify hub pages from clustered SERP data by reviewing the script output, which highlights central hub URLs and calculates average Jaccard coherence to measure topical group strength.

What is Jaccard coherence in SERP-based topic clustering?▼

Jaccard coherence in SERP-based topic clustering measures the similarity between keyword URL sets, calculating the average overlap strength to determine how tightly grouped a keyword cluster is.

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