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seo-keyword-cluster Checked

Build a content cluster plan from seed keywords — intent-grouped clusters, pillar+spokes architec…
✍️ Writing skills By seranking Version v1.0.0 Added 2026-10-21 Updated 2026-10-21 Source ↗ Part of SEO & Search Growth →
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Installation

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Install the "seo-keyword-cluster" skill by following the instructions at https://skill123.me/install/seo-keyword-cluster.
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⬇ Download v1.0.0 · 7 KB
📊 Static rating
8.3/10
Trigger
9.0
Structure
0.0
Workflow
8.0
Content
0.0
Engineering
0.0
Security
8.0

Six-dimension rubric with reviewer rationale.

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🛡 Dynamic test
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About this skill

Build a content cluster plan from seed keywords — intent-grouped clusters, pillar+spokes architecture with H1/H2 suggestions per spoke, prioritised build order, and an internal-linking map. Plans a content tier across many articles (vs seo-content-brief which produces a single article from a topic; vs seo-page which audits one existing URL). Use when the user asks for keyword clustering, topical map, pillar content strategy, content cluster plan, or content calendar from a keyword list.

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

Documentation

Build a content cluster plan from seed keywords — intent-grouped clusters, pillar+spokes architecture with H1/H2 suggestions per spoke, prioritised build order, and an internal-linking map. Plans a content tier across many articles (vs seo-content-brief which produces a single article from a topic; vs seo-page which audits one existing URL). Use when the user asks for keyword clustering, topical map, pillar content strategy, content cluster plan, or content calendar from a keyword list.

> Example output: examples/seo-keyword-cluster-headless-cms-20260514/PLAN.md (../../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.
View full documentation

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.

Output format

Create a folder seo-keyword-cluster-{target-slug}-{YYYYMMDD}/ with:


seo-keyword-cluster-{target-slug}-{YYYYMMDD}/
├── 01-seed-expansion.md
├── 02-filtered-keywords.md
├── 03-cluster-assignment.md      (SERP overlap matrix + cluster groupings)
├── 06-quality-scorecard.md       (evidence) — 4-metric gate result; written every run
├── keywords.csv
└── PLAN.md

PLAN.md follows this shape:


# Cluster Plan: {topic} {(needs review — N quality-gate failures) if step 7 flagged any}
Market: {country}
Seeds: {seed list}

## Summary
- Keywords analysed: {n}
- Clusters formed: {n}
- Estimated combined monthly volume: {n}
- Pillars: {n}, spokes: {n}
- Clustering method: SERP-overlap top-10 (mode: {standard | advanced}, ~{credits} credits)

## Build order

### Cluster 1: {cluster name} [PILLAR]
- Primary keyword: {kw} ({volume}/mo, KD {kd})
- Secondary: {list}
- Total volume: {n}/mo
- Priority score: {n}

#### Pillar page
- H1: {H1}
- H2s: {list}

#### Spoke articles
1. **{spoke title}**
   - H1: {H1}
   - H2s: {list}
   - Target keyword: {kw} ({volume})
2. **{spoke title}** ...

### Cluster 2: {cluster name} [SPOKE-ONLY]
...

## Internal linking map
- Pillar A links to: spokes A1, A2, A3
- Spoke A1 links back to: pillar A, and cross-links to spoke B2 (topical overlap)
...

## Quality scorecard
{If all four gates pass:}
All gates passed (cannibalisation/orphan/coverage/anchor-diversity).

{If any fail, render this table instead:}
| Gate | Status | Detail |
|---|---|---|
| Cannibalisation (no two clusters ≥40% SERP overlap) | RED / YELLOW / GREEN | {detail} |
| Orphan (every spoke linked from its pillar) | RED / YELLOW / GREEN | {detail} |
| Coverage (pillar covers ≥70% of cluster's high-volume keywords) | RED / YELLOW / GREEN | {detail} |
| Anchor diversity (no anchor used >40% of internal links per cluster) | RED / YELLOW / GREEN | {detail} |

## Raw data
- keywords.csv: full enriched keyword list
- 03-cluster-assignment.md: every keyword and its cluster (incl. SERP overlap matrix)
- 06-quality-scorecard.md: standalone copy of the scorecard above (evidence)

keywords.csv columns:

keyword,volume,kd,cpc,intent,cluster,role_in_cluster

Tips

  • Respect Data API rate limit: 10 requests per seco

Score breakdown

Trigger
9.0
Structure
0.0
Workflow
8.0
Content
0.0
Engineering
0.0
Security
8.0