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".
What this skill does
# 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", "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:
| Shared Results | Relationship | Action |
|---------------|-------------|--------|
| 7-10 | Same post | Merge into single target page |
| 4-6 | Same cluster | Group under same spoke cluster |
| 2-3 | Interlink | Place in adjacent clusters, add cross-links |
| 0-1 | Separate | Assign to different clusters or exclude |
**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:
| Intent | Signals | Include in Clusters? |
|--------|---------|---------------------|
| Informational | how, what, why, guide, tutorial, learn | Yes |
| Commercial | best, top, review, comparison, vs, alternative | Yes |
| Transactional | buy, price, discount, coupon, order, sign up | Yes |
| Navigational | brand names, specific product names, login | No (exclude) |
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:
| Intent Pattern | Template Options |
|---------------|-----------------|
| Informational (broad) | ultimate-guide |
| Informational (how) | how-to |
| Informational (list) | listicle |
| Informational (concept) | explainer |
| Commercial (compare) | comparison |
| Commercial (evaluate) | review |
| Commercial (rank) | best-of |
| Transactional | landing-page |
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:
| Link Type | Direction | Requirement |
|-----------|-----------|-------------|
| Spoke to pillar | spoke -> pillar | Mandatory (every spoke) |
| Pillar to spoke | pillar -> spoke | Mandatory (every spoke) |
| Spoke to spoke (within cluster) | spoke <-> spoke | 2-3 links per post |
| Cross-cluster | spoke -> spoke (other cluster) | 0-1 links per post |
**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:
```json
{
"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:
```javascript
{
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
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