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Build a content cluster plan from seed keywords — pillar+spokes architecture with internal-link map, intent grouping, and quality scorecard.
Passed 12 of 19 test cases with the skill, and 3 of 19 without it.
and 11 more in the full report
10 fixed4 passed either way9 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
This benchmark measured an earlier version of the skill.
One of 10 test cases the skill fixed. Same model, same prompt, with and without the skill.
We completed the keyword clustering process and generated all output artifacts. Which skill in the marketing chain directly consumes PLAN.md and 06-pillar-pages.md next?
| Checked | Without skill | With skill |
|---|---|---|
| Hands off the summary plan and pillar briefs directly to the content brief creation skill. | Passed | Passed |
| Identifies keyword clustering as a producer skill bridging keyword research and content brief creation. | Missed | Passed |
The instructions your model reads when the skill loads.
Script location. If your host does not set
${CLAUDE_PLUGIN_ROOT}, the scripts are in this plugin'sscripts/folder, next toskills/. Run them with Python 3.10 or newer:python, orpython3wherepythonis not found (macOS, many Linux systems).
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.
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.
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).
~/.claude-marketing/brands/_active-brand.json for the active slug, then load ~/.claude-marketing/brands/{slug}/profile.jsonskills/context-engine/industry-profiles.mdskills/context-engine/compliance-rules.md to filter out banned terminology before clustering| 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.
test cases passed, vs 3 / 19 without
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