Keyword cluster

/digital-marketing-pro:keyword-cluster

Purpose

Take a set of seed keywords and produce a publication-ready cluster plan: pillar pages with their spokes, intent-grouped, prioritised by an opinionated scoring formula, with an internal-link map and a four-gate quality scorecard. Output is structured for direct hand-off to /digital-marketing-pro:content-brief or /digital-marketing-pro:content-engine.

Context efficiency

Heavy skill. Grep before Read any referenced file, then Read only matched ranges with offset + limit. List ${CLAUDE_PLUGIN_DATA}/<brand>/ before opening files. On re-invocation mid-session, skip files already in context.

When to Use

• Onboarding a new content programme — turn a 20-keyword brief into a structured topical hub
• Auditing an existing content library for cannibalisation (two pages competing for the same intent)
• Designing a pillar+spokes architecture before any writing begins
• Staging programmatic SEO across hundreds of variants (use this once per topic family)
• Reorganising an existing site's internal-link graph

Don't use when you just need keyword expansion (use /digital-marketing-pro:keyword-research) or when you need ranking / SERP-feature analysis (use /digital-marketing-pro:rank-monitor, with --features for SERP features).

Brand context (auto-applied)

1. Read ~/.claude-marketing/brands/_active-brand.json for the active slug, then load ~/.claude-marketing/brands/{slug}/profile.json
2. If no brand exists: ask "Set up a brand first (/digital-marketing-pro:brand-setup)?" — or proceed with defaults
3. Apply industry-specific guidance from skills/context-engine/industry-profiles.md
4. Apply skills/context-engine/compliance-rules.md to filter out banned terminology before clustering

Inputs

Input	Source	Required?
Seed keywords (3–500)	CSV with keyword column (optional: volume, kd, intent)	yes
SERP results per keyword	JSON: {keyword: [top result URLs]} from any rank-tracker / Ahrefs / Semrush export	strongly recommended — without this the script falls back to lexical clustering, which is lower-confidence
Target country / language	From brand profile	optional override
Min volume / max KD filters	CLI flags	optional
Overlap threshold	CLI flag --overlap (default 0.4 for SERP mode, 0.3 for lexical)	optional

If SERPs JSON is unavailable, you can build one quickly by running the brand's connected rank-tracker MCP (Ahrefs / SE Ranking / Semrush) for each seed and saving the top 10 URLs. Skip this step only if the seeds are too numerous to justify the API spend — but flag the lower-confidence mode in the final deliverable.

Process (10 steps, numbered-file output)

All outputs go to ${CLAUDE_PLUGIN_DATA}/{brand}/seo/keyword-cluster/{YYYY-MM-DD}/.

1. 00-input.md — capture seeds, source, filters, brand context, run timestamp
2. 01-seed-expansion.md — if seeds < 20, expand via brand's keyword-research MCP (Ahrefs getRelatedKeywords, etc.) to ~50–200; otherwise skip. Document expansion source.
3. 02-filtered.csv — apply min-volume / max-KD / banned-word filters. Save the filtered set as CSV (this is what the script consumes).
4. 03-serps.json — fetch top-10 SERP URLs per keyword via the connected rank-tracker (skip if SERPs already provided). Budget guard: if estimated cost > 500 credits, surface the cost and ask "Continue? (y/N — default N)" before fetching.
5. 04-cluster-run.json — run the script:

   python "${CLAUDE_PLUGIN_ROOT}/scripts/keyword_cluster.py" \
       --keywords "${CLAUDE_PLUGIN_DATA}/{brand}/seo/keyword-cluster/{date}/02-filtered.csv" \
       --serps "${CLAUDE_PLUGIN_DATA}/{brand}/seo/keyword-cluster/{date}/03-serps.json" \
       --overlap 0.4 \
       --min-volume {profile.min_volume or 0} \
       --max-kd {profile.max_kd or 100} \
       --out "${CLAUDE_PLUGIN_DATA}/{brand}/seo/keyword-cluster/{date}/04-cluster-run.json"
   

6. 05-quality-scorecard.md — read the quality_scorecard block from 04-cluster-run.json. If status: needs_review, diagnose:
   • cannibalisation: fail → two clusters share pillar+intent. Merge them or reassign the lower-priority cluster's pillar.
   • orphan: fail → a multi-keyword cluster has 0 spokes. Re-tokenise its members or lower --overlap.
   • coverage: fail → < 80% of seeds clustered. Lower --overlap to 0.3 or expand seeds.
   • anchor_diversity: fail → pillar names too similar. Rewrite cluster names with synonym variation.
   • fragmentation_warning: true (pillar-only > 50%) → overlap threshold too strict. Try --overlap 0.3 first.
7. 06-pillar-pages.md — for each cluster with priority_score >= 0.5, draft a one-paragraph pillar page brief (intent, audience, length target, key questions to answer). These feed /digital-marketing-pro:content-brief.
8. 07-internal-link-map.md — table view of internal_link_targets from the script output. Per cluster: which other clusters to link out to + suggested anchor text. This is the file your dev team or CMS template should consume.
9. 08-build-order.md — sorted by priority_score descending. Recommended build cadence: top 10% in Q1, next 30% in Q2, remainder backlog.
10. PLAN.md — single-page summary: stats + scorecard + top 5 priority clusters + handoff to next skill in chain.

Output format

${CLAUDE_PLUGIN_DATA}/{brand}/seo/keyword-cluster/2026-06-04/
├── 00-input.md
├── 01-seed-expansion.md      (only if seeds expanded)
├── 02-filtered.csv
├── 03-serps.json             (if SERP mode)
├── 04-cluster-run.json       (raw script output)
├── 05-quality-scorecard.md
├── 06-pillar-pages.md
├── 07-internal-link-map.md
├── 08-build-order.md
└── PLAN.md                   (the deliverable)

PLAN.md is what you hand to the brand / client / next skill. Everything else is auditable intermediate state.

Quality scorecard (the four gates)

Every run produces a scorecard from scripts/keyword_cluster.py. All four must pass for status: ready:

Gate	What it checks	Why it matters
cannibalisation	No two clusters share the same (pillar, primary_intent) pair	Prevents you from writing two pages competing for the same SERP
orphan	Every multi-keyword cluster has ≥1 spoke (pillar-only clusters are exempt and tagged)	Catches clustering bugs where a cluster head has no supporting topics
coverage	≥ 80% of input seeds are assigned to at least one cluster	Catches "junk" seeds and overly strict thresholds
anchor_diversity	Each multi-keyword cluster has ≥ 2 anchor-text variants suggested	Stops anchor-text over-optimisation across the internal-link graph

A fragmentation_warning: true (pillar-only > 50%) is a soft signal — the run is valid but you should consider lowering --overlap and re-running.

Chain handoffs

This skill is a producer in the chain:

1. /digital-marketing-pro:keyword-research — generate seeds
2. /digital-marketing-pro:keyword-cluster — this skill
3. /digital-marketing-pro:content-brief — consumes PLAN.md + 06-pillar-pages.md to brief each pillar
4. /digital-marketing-pro:content-engine — drafts the content
5. /digital-marketing-pro:seo-implement — applies the internal-link map to the CMS

Tips & caveats

• SERP mode is strictly better than lexical mode. Lexical clustering can't see that "shopify seo" and "ecommerce platform seo" target overlapping SERPs while "shopify themes" doesn't.
• Overlap threshold defaults are conservative. If you get fragmentation_warning: true, lower to 0.3 first. If you get cannibalisation: fail with too few clusters, raise to 0.5.
• The priority score isn't a ranking — it's a starting build order. A cluster with priority_score: 0.3 may still be your highest-conversion opportunity if it maps to a high-margin product line. Use the brand profile's business_goals to override mechanically.
• Don't run this on raw GSC query exports without filtering first. GSC dumps thousands of long-tail variants of the same query — they'll all cluster together and produce a single mega-cluster.
• Pillar-only clusters are valid — they represent distinct intents that simply lack spoke candidates in your seed set. Add seeds via Step 2 expansion if you want spokes.
• The internal-link map is suggestions, not commands. Final anchor text should be reviewed for brand voice (apply the brand profile's voice fields + skills/context-engine/guidelines-framework.md).

Agents used

• seo-specialist (primary) — interpretation + final pillar-page recommendations
• competitive-intel — for SERP-overlap reasoning when results look surprising
• brand-guardian — anchor-text review against banned-term lists

See also

• /digital-marketing-pro:keyword-research — generates seeds (use first)
• /digital-marketing-pro:content-brief — consumes the cluster plan (use next)
• /digital-marketing-pro:seo-implement — applies internal-link map to CMS
• /digital-marketing-pro:seo-drift — re-run quarterly to detect cluster drift
• scripts/keyword_cluster.py — the underlying clustering engine