seo-cluster

# Semantic Topic Clustering (v1.9.0)
## Shared Data Cache

**Step 0 -- Check shared data cache:**

Before gathering, check `.seo-cache/` for reusable context from related SEO skills.
Reference: `../seo/references/shared-data-cache.md` for schemas and dependency map.

Check these cache files when present:
- `.seo-cache/site-meta.json` for domain, business type, industry, and crawl context
- `.seo-cache/audit-scores.json` for prior full-audit priorities
- `.seo-cache/pages/{url-slug}/page-analysis.json` for page-level context when a URL is provided

- If found: parse and use clearly valid fields (note "Using cached [X] from [date]")
- If missing, corrupt, or irrelevant: continue with fresh evidence
- If the user says "refresh" or "re-run": ignore cache reads and overwrite on write

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.

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## 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 codex-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 |