SEO Keyword Cluster

Example output: examples/seo-keyword-cluster-headless-cms-20260514/PLAN.md

Keyword Cluster

Transform seed keywords into a prioritised cluster plan: each cluster grouped by search intent and theme, with volume totals, a pillar concept, spoke articles, and suggested H1/H2 for each spoke.

Prerequisites

• SE Ranking MCP server connected.
• User provides: (a) 3 to 20 seed keywords, (b) target market country (default: us), and optionally (c) minimum volume threshold (default: 100/mo), (d) maximum KD (default: 60).

Process

1. Expand seeds DATA_getRelatedKeywords, DATA_getSimilarKeywords, DATA_getLongTailKeywords

   • For each seed, pull related + similar + long-tail variants in the target country.
   • Target at least 100 candidate keywords per seed; de-duplicate across seeds.

2. Question-based expansion DATA_getKeywordQuestions

   • Pull question-intent keywords for the top 5 seeds.
   • These usually become spoke articles with PAA/featured-snippet potential.

3. Clean and filter

   • Remove keywords below min volume and above max KD.
   • Strip branded terms the target does not own.
   • Tag each keyword with detected intent: informational, commercial, transactional, navigational.

4. Cluster by SERP overlap DATA_getSerpResults (or DATA_getSerpTaskAdvancedResults)

   • Group keywords by how Google actually ranks them — shared top-10 organic URLs — not by text similarity. Token-overlap clustering manufactures cannibalisation; see references/serp-overlap-methodology.md for the full algorithm and anti-pattern callouts.
   • Budget guard before running. Compute estimated_credits = num_candidate_keywords × per_keyword_cost where per_keyword_cost = 3 (SERP-standard, default) or 10 (SERP-advanced, only if downstream needs AIO/PAA). Standard is sufficient for clustering. If estimated_credits > 500, surface the figure to the user and offer two paths: (a) proceed with SERP-standard, (b) trim the candidate set by raising the min-volume / lowering the max-KD thresholds in step 3 and re-running. If the user already requested SERP-advanced and the estimate exceeds 500, additionally offer SERP-standard as a cheaper fallback.
   • Fetch SERPs (one call per unique candidate keyword, cached for the session) — see references/serp-overlap-methodology.md § "Caching". Total SERP fetches = number of keywords, not number of pairs.
   • Pairwise overlap scoring. For each pair within an intent pre-group (see references/serp-overlap-methodology.md § "Pre-Grouping" for the optimisation that avoids full O(N²)), count shared URLs in the top 10 organic. Apply thresholds: 7-10 shared = same post (merge keywords), 4-6 = same cluster, 2-3 = interlink across clusters, 0-1 = separate clusters or exclude.
   • Form clusters from the connected components in the 4-6+ overlap graph. Target 5 to 12 clusters. Each cluster gets a name, primary keyword, secondary keywords, total volume, weighted KD.
   • Classify each cluster as pillar-worthy (broad, high volume, informational) or spoke-only (narrow, specific).

5. Pillar plus spokes architecture

   • For each pillar cluster, nominate 3 to 7 spoke articles (each one from a sub-cluster or question).
   • For each spoke, draft an H1 and 3 to 5 H2s.
   • Map internal-link structure: pillar links to all spokes, spokes link back to pillar, spokes cross-link where topically adjacent.

6. Prioritise

   • Applied after clusters are formed via SERP-overlap in step 4 — the formula scores already-grouped clusters, it does not influence which keywords cluster together.
   • Score each cluster: volume (40%) + inverse KD (30%) + commercial intent weighting (30%).
   • Output a prioritised build order.

7. Quality scorecard (post-synthesis validation)

   • After PLAN.md is written, run a 4-metric quality scorecard against the produced plan and warn the user if any metric fails. Inspired by theirs' post-execution scorecard model — adapted to our cluster-plan output (we score the plan, not generated content, since seo-keyword-cluster stops at the architecture).
   • Cannibalisation (zero tolerance). No two clusters in the plan should share ≥ 40% SERP overlap with each other (computed from the cached SERP matrix in step 4). If two clusters trip this gate, re-merge them and re-run from step 5 onward.
   • Orphan (zero tolerance). Every spoke article in the plan must be linked from its pillar in the internal-link map produced in step 5. Any spoke without an inbound link from its pillar is an orphan.
   • Coverage. The pillar page in each cluster must cover ≥ 70% of the cluster's high-volume keywords (top half of the cluster by volume) in its primary keyword + secondary keyword set, or via the H2s drafted in step 5. Below 70% means the pillar is too narrow for the cluster it heads.
   • Anchor diversity. Across all internal links inside a cluster (pillar↔spoke + spoke↔spoke), no single anchor text should be used > 40% of the time. Concentration above 40% is an over-optimisation signal.
   • Output. If all four metrics pass, append a single line to PLAN.md under "## Quality scorecard": All gates passed (cannibalisation/orphan/coverage/anchor-diversity). If any metric fails, append a "## Quality scorecard" section to PLAN.md with red/yellow/green rows for each metric (red = fail, yellow = within 10% of threshold, green = pass), and annotate the verdict header at the top of PLAN.md with (needs review — N quality-gate failures). Also write the same scorecard verbatim to 06-quality-scorecard.md in the output folder so it's auditable independently.