/seo-cluster

name: seo-cluster
description: >
  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 codex-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".
user-invokable: true
argument-hint: "<seed-keyword or url>"
license: MIT
metadata:
  author: AgriciDaniel
  original_author: "Lutfiya Miller (Pro Hub Challenge Winner)"
  version: "1.9.6"
  category: seo

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.

---

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 | | Commerci