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

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", "tools", "examples", "guide", "template", "mistakes", "checklist"
4. Question mining — Generate who/what/when/where/why/how variants
5. Intent modifiers — Add commercial modifiers: "pricing", "review", "alternative", "comparison", "free", "top"

Deduplication: Normalize variants (lowercase, strip articles), remove exact duplicates. Target: 30-50 unique keyword variants. If under 30, run a second expansion pass with the top PAA questions as seeds.

Step 2: SERP Overlap Clustering

This is the core differentiator. Load references/serp-overlap-methodology.md for the full algorithm.

Process:

1. Group keywords by initial intent guess (reduces pairwise comparisons)
2. For each candidate pair within a group, WebSearch both keywords
3. Count shared URLs in the top 10 organic results (ignore ads, featured snippets, PAA)
4. Apply thresholds:

Optimization: With 40 keywords, full pairwise = 780 comparisons. Instead:

• Pre-group by intent (4 groups of ~10 = 4 x 45 = 180 comparisons)
• Only cross-check group boundary keywords
• Skip pairs where both are long-tail variants of the same head term (assume same cluster)

DataForSEO integration: If DataForSEO MCP is available, use serp_organic_live_advanced instead of WebSearch for SERP data. Run python scripts/dataforseo_costs.py check serp_organic_live_advanced --count N before each batch. If "status": "needs_approval", show cost estimate and ask user. If "status": "blocked", fall back to WebSearch.

Step 3: Intent Classification

Classify each keyword into one of four intent categories:

Remove navigational keywords from clustering. Flag borderline cases for manual review. Keywords can have mixed intent (e.g., "best CRM software" is both commercial and informational) -- classify by dominant intent.

Step 4: Hub-and-Spoke Architecture

Load references/hub-spoke-architecture.md for full specifications.

Design the cluster structure:

1. Select the pillar keyword — Highest volume, broadest intent, most SERP overlap with other keywords
2. Group spokes into clusters — Each cluster is a subtopic area (2-5 clusters per pillar)
3. Assign posts to clusters — Each cluster gets 2-4 spoke posts
4. Select templates per post — Based on intent classification:

5. Set word count targets:

   • Pillar page: 2500-4000 words
   • Spoke posts: 1200-1800 words

6. Cannibalization check — No two posts share the same primary keyword. If SERP overlap is 7+, merge those keywords into a single post targeting both.

Step 5: Internal Link Matrix

Design the bidirectional linking structure:

Rules:

• Every post must have minimum 3 incoming internal links
• No orphan pages (every post reachable from pillar in 2 clicks)
• Anchor text must use target keyword or close variant (no "click here")
• Link placement: within body content, not just navigation/sidebar

Generate the link matrix as a JSON adjacency list:

{
  "links": [
    { "from": "pillar", "to": "cluster-0-post-0", "type": "mandatory", "anchor": "keyword" },
    { "from": "cluster-0-post-0", "to": "pillar", "type": "mandatory", "anchor": "keyword" }
  ]
}

Step 6: Interactive Cluster Map

Generate cluster-map.html using the template at templates/cluster-map.html.

1. Read the template file
2. Build the CLUSTER_DATA JSON object from the cluster plan:

   {
     pillar: { title, keyword, volume, template, wordCount, url },
     clusters: [{ name, color, posts: [{ title, keyword, volume, template, wordCount, url, status }] }],
     links: [{ from, to, type }],
     meta: { totalPosts, totalClusters, totalLinks, estimatedWords }
   }
   

3. Replace the CLUSTER_DATA placeholder in the template with the actual JSON
4. Write the completed HTML file to the output directory
5. Inform user: "Open cluster-map.html in a browser to explore the interactive cluster map."

Strategy Import

When invoked with --from strategy:

1. Look for the most recent /seo plan output in the current directory (search for files matching *SEO*Plan*, *strategy*, *content-strategy*)
2. Parse markdown tables for: keywords, page types, content pillars, URL structures
3. Validate extracted data: check for duplicates, missing keywords, incomplete entries
4. Enrich with SERP data: run SERP overlap analysis on extracted keywords
5. Build cluster plan using the imported keywords as the starting set (skip Step 1)

If no strategy file is found, prompt the user: "No existing SEO plan found in the current directory. Run /seo plan first, or provide a seed keyword for fresh clustering."

Execution Workflow

When /seo cluster execute is invoked:

Check for claude-blog

Test: Does ~/.claude/skills/blog/SKILL.md exist?

If claude-blog IS installed:

1. Load references/execution-workflow.md for the full algorithm
2. Read cluster-plan.json from the current directory
3. Check for resume state: scan output directory for already-written posts
4. Execute in priority order: pillar first, then spokes by volume (highest first)
5. For each post, invoke the blog-write skill with cluster context:
   • Cluster role (pillar or spoke)
   • Position in cluster (cluster index, post index)
   • Target keyword and secondary keywords
   • Template type and word count target
   • Internal links to includ