# SEO Cluster

> Use when building semantic keyword clusters from SERP overlap for pillar/cluster content architecture.

- Skill: `fusengine/seo-cluster` (Agent Skill)
- Install (CLI): `npx skillmds@latest add fusengine/seo-cluster`
- Raw SKILL.md: https://api.skillmd.com/api/skills/fusengine/seo-cluster/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Marketing & Growth
- Author: fusengine (https://skillmd.com/u/fusengine)
- Updated: 2026-09-10
- Page: https://skillmd.com/skills/fusengine/seo-cluster

---


<objective>
Builds semantic keyword clusters by expanding a seed keyword (autocomplete + "People Also Ask"), fetching each candidate's SERP, and grouping keywords whose SERP overlaps the seed's by Jaccard index ≥30% into one cluster (pillar = highest-volume keyword). Layers clusters by buyer state (L1 awareness through L4 decision) rather than lexical similarity, splits local vs global intent into separate pages, and runs an anti-cannibalization check against existing pages before proposing new cluster pages. Does not write the content itself — hands off to seo-content-brief per cluster page and seo-internal-linking for the resulting link mesh.
</objective>

# Semantic Clustering

## Method

1. Take seed keyword (e.g. "claude code")
2. Fetch SERP for seed via WebFetch/fuse-browser (top 10 results)
3. For each related keyword (autocomplete + "People Also Ask"):
   - Fetch its SERP
   - Compute overlap with seed's SERP (Jaccard index)
4. Group keywords where SERP overlap ≥ 30% → same cluster
5. Cluster center = highest-volume keyword

## Output

```markdown
# Cluster: "claude code"

## Pillar: claude code (vol: 12K, KD: 45)
- Intent: informational
- Featured: AI Overview, video

## Cluster pages
1. claude code installation (vol: 2.4K)
2. claude code vs cursor (vol: 1.8K)
3. claude code mcp servers (vol: 900)
4. claude code hooks (vol: 720)
```

## Cluster by Buyer State (2026)

SERP overlap is the mechanical signal; the strategic axis is **buyer state + intent**, not surface similarity. Map each cluster keyword to a layer, then group by layer:

| Layer | State | Intent signal |
|-------|-------|---------------|
| **L1** | Awareness | "what is", "why", problem framing |
| **L2** | Comparison | "vs", "alternatives", "best for" |
| **L3** | Evaluation | "pricing", "reviews", "worth it" |
| **L4** | Decision | "buy", "near me", "demo", "signup" |

Two keywords with high SERP overlap but different buyer states belong to different pages. Never merge clusters on lexical similarity alone.

### Citation eligibility

AI Overviews capture ~30-60% of informational (L1/L2) CTR. For those layers, prioritize pages that produce verbatim-extractable answers per section over raw ranking — the goal is the LLM citation, not only the blue link.

## Local vs Global Intent (2026)

| Axis | LOCAL intent | GLOBAL intent |
|------|--------------|---------------|
| Type | Proximity transactional/navigational ("near me", "[service] [city]") | Informational / comparative |
| SERP feature | Triggers the Map Pack | AI Overviews-heavy |
| AI Overviews exposure | Resists (local results stay link-driven) | CTR eroded -40% to -58% on informational keywords |
| Target page | Local page / city hub | Global pillar |

**One intent = one URL.** Split a local page from the global/pillar page when local volume and content justify it. **Do not split** if local volume is below ~30 searches/month, or if you cannot write 1200+ words genuinely distinct from the pillar.

## Anti-Cannibalization Check

Before creating cluster pages, verify no existing page targets the same buyer state + intent. Use `seo-content` skill. The primary keyword is exclusive per page — pillar = `[service]` (no city), local = `[service] [city]`. See `seo-internal-linking` for the pillar/local/region URL architecture and link mesh.