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 claude-blog is installed.
SkillScore78Maintained
Automatic
◆Marketing & Content pack
Last verified 9/1/2026
What it changes
Passed 16 of 21 test cases with the skill, and 7 of 21 without it.
Without skill35%
With skill78%
Checks it passed that the model alone missed
Sets the pillar page target word count between 2500 and 4000 words.
Saves the machine-readable plan data to a file named cluster-plan.json.
Runs python3 scripts/render_page.py with the --mode auto flag for URL fetching.
and 13 more in the full report
10 fixed8 passed either way5 still failing0 broke
Pass-rate lift
+43 pp
Tokens
+86%
Agent turns
0%
Response time
-40%
Model gemini-3.6-flashMeasured 9/1/2026v118 of 23 cases passed with skill · 21 comparable
Tokens, turns and time describe resource use, not a dollar price.
See it in action
One of 10 test cases the skill fixed. Same model, same prompt, with and without the skill.
1 of 10
Prompt
We previously generated a cluster map HTML visualization. After writing 4 out of 10 articles, we ran the command to regenerate the interactive cluster map. What file does it read to update post statuses, and what completion percentage metric is reported?
What was checked, without and with the skill
Checked
Without skill
With skill
Reads cluster-plan.json from the current working directory to update post statuses.
Missed
Passed
Reports the link completion percentage alongside published versus planned post counts.
Missed
Passed
case-17 · gemini-3.6-flash · 9/1/2026 · answers are capped and redacted
What's inside
The instructions your model reads when the skill loads.
SKILL.md302 lines5 files
Semantic Topic Clustering (v1.9.0)
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 claude-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:
Related searches — Search the seed, extract "related searches" and "people also search for"
People Also Ask (PAA) — Extract all PAA questions from SERP results
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.