/keyword-cluster

name: keyword-cluster
description: "Build a pillar+spokes content cluster plan from seed keywords — SERP-overlap clustering via keyword_cluster.py, intent grouping, a priority-scored build order, an internal-link map with anchor suggestions, and a four-gate quality scorecard (cannibalisation, orphan, coverage, anchor diversity), delivered as a numbered file set ending in PLAN.md. Triggers on \"/digital-marketing-pro:keyword-cluster\", \"cluster these keywords\", \"design our topical hub\", \"are these pages cannibalising each other\", \"plan the pillar pages\". Reads the brand profile and compliance rules to filter banned terms; consumes seeds from /digital-marketing-pro:keyword-research and hands PLAN.md to /digital-marketing-pro:content-brief."
argument-hint: "[brand-name or path/to/seeds.csv]"
user-invocable: true

/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 consu