Agent skill · Content & Marketing

seo-cluster

SERP-based semantic topic clustering for content architecture planning. Groups keywords by actual Google SERP overlap (not text similarity), designs hub-and-spoke content clusters with internal link matrices, and generates interactive visualizations. Optionally executes content creation if claude-blog is installed. Use when user says "topic cluster", "content cluster", "semantic clustering", "pillar page", "hub and spoke", "content architecture", "keyword grouping", or "cluster plan".

Infrasity-Labsgithub.com/Infrasity-LabsGitHub ↗
claude-codeMIT
Install
npx skills add Infrasity-Labs/dev-gtm-claude-skills --skill seo-cluster --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 4
SKILL.md size: 13 KB
Bundled scripts: none
Path: .claude/skills/seo-cluster/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 97
Language: Python

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Semantic Topic Clustering (v1.9.0) SERP-overlap-driven keyword clustering for content architecture. Groups keywords by how Google actually ranks them (shared top-10 results), not by text similarity. Designs hub-and-spoke content clusters with internal link matrices and generates interactive cluster map visualizations. **Scripts:** Located at the plugin root `scripts/` directory. --- ## Quick Reference | Command | What it does | |---------|-------------| | `/seo cluster plan <seed-keyword>` | Full planning workflow: expand, cluster, architect, visualize | | `/seo cluster plan --from strategy` | Import from existing `/seo plan` output | | `/seo cluster execute` | Execute plan: create content via claude-blog or output briefs | | `/seo cluster map` | Regenerate the interactive cluster visualization | --- ## Planning Workflow ### Step 1: Seed Keyword Expansion Expand the seed keyword into 30-50 variants using WebSearch: 1. **Related searches** — Search the seed, extract "related searches" and "people also search for" 2. **People Also Ask (PAA)** — Extract all PAA questions from SERP results 3. **Long-tail modifiers** — Append common modifiers: "best", "how to", "vs", "for beginners",

What's inside
Steps it walks through
  1. Quick Reference
  2. Planning Workflow
  3. Step 1: Seed Keyword Expansion
  4. Step 2: SERP Overlap Clustering
  5. Step 3: Intent Classification
  6. Step 4: Hub-and-Spoke Architecture
  7. Step 5: Internal Link Matrix
  8. Step 6: Interactive Cluster Map
  9. Strategy Import
  10. Execution Workflow
  11. Check for claude-blog
  12. Cluster Scorecard
  13. Map Regeneration
  14. Output Files
Ships with 3 files
  • references/execution-workflow.md
  • references/hub-spoke-architecture.md
  • references/serp-overlap-methodology.md
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About this skill
What does the seo-cluster skill do?

SERP-based semantic topic clustering for content architecture planning. Groups keywords by actual Google SERP overlap (not text similarity), designs hub-and-spoke content clusters with internal link matrices, and generates interactive visualizations. Optionally executes content creation if claude-blog is installed. Use when user says "topic cluster", "content cluster", "semantic clustering", "pillar page", "hub and spoke", "content architecture", "keyword grouping", or "cluster plan".

How do I install it?

Run `npx skills add Infrasity-Labs/dev-gtm-claude-skills --skill seo-cluster --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From Infrasity-Labs/dev-gtm-claude-skills, a repository with 97 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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