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indranilbanerjee
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keyword-cluster

Clusters seed keywords into pillar-and-spoke content plans using SERP-overlap analysis.

787|132|Updated Feb 11, 2026
Search Tags:#internal linking#content strategy#seo#pillar pages#keyword clustering#topical authority#serp analysis
One-click install
npx skills add https://github.com/indranilbanerjee/digital-marketing-pro --skill keyword-cluster
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: keyword-cluster
Source: https://github.com/indranilbanerjee/digital-marketing-pro/tree/main/skills/keyword-cluster
Command: npx skills add https://github.com/indranilbanerjee/digital-marketing-pro --skill keyword-cluster

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

Turning a raw list of seed keywords into a coherent content architecture is slow and error-prone: pages end up competing for the same SERP, topics get orphaned, and internal linking is improvised. This Skill automates SERP-overlap clustering and produces an auditable, prioritized pillar+spokes plan ready for content production.

Core Features & Use Cases

  • SERP-Overlap Clustering: Runs keyword_cluster.py against top-10 SERP URLs per keyword (with a lexical fallback) to group keywords by shared ranking intent.
  • Four-Gate Quality Scorecard: Validates every run against cannibalisation, orphan, coverage, and anchor-diversity gates, with concrete remediation steps for each failure.
  • Prioritized Build Order & Link Map: Outputs a priority-scored build order, pillar page briefs, and an internal-link map with suggested anchor text as numbered files ending in PLAN.md.
  • Use Case: An agency onboarding a new brand takes 50 seed keywords from keyword research, runs this Skill, and hands the resulting PLAN.md and pillar briefs directly to the content-brief step — with compliance-filtered terms and a quarterly re-run path for drift detection.

Quick Start

Ask the AI to cluster the keywords in your seeds CSV into a pillar-and-spoke content plan for your brand, for example: run /digital-marketing-pro:keyword-cluster with my seeds.csv to build the topical hub plan.

Frequently Asked Questions about keyword-cluster

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

FAQPage Schema
How do I cluster keywords into pillar pages for SEO?▼

Provide a CSV of seed keywords plus top-10 SERP URLs per keyword, then run the keyword_cluster.py script which groups keywords by SERP overlap. The output includes pillar assignments, spokes, a priority-scored build order, and an internal-link map.

What is SERP-overlap clustering vs lexical clustering?▼

SERP-overlap clustering groups keywords that share ranking URLs in search results, reflecting true search intent. Lexical clustering only compares word similarity and misses cases like 'shopify seo' and 'ecommerce platform seo' targeting the same SERP, so SERP mode is strongly recommended.

Can I use keyword clustering without SERP data from Ahrefs or Semrush?▼

Yes, the script falls back to lexical clustering when no SERPs JSON is provided, using a default overlap threshold of 0.3. The lower-confidence mode should be flagged in the final deliverable since results are less reliable.

Why does keyword clustering fail the cannibalisation check?▼

Cannibalisation fails when two clusters share the same pillar and primary intent, meaning two planned pages would compete for the same SERP. Fix it by merging the clusters or reassigning the lower-priority cluster's pillar.

When should I not use keyword clustering?▼

Skip clustering when you only need keyword expansion (use keyword research instead) or ranking and SERP-feature analysis (use rank monitoring). Also avoid running it on raw Google Search Console exports, which produce one mega-cluster of long-tail variants.

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