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jwuthri-cloudjwuthri-cloud/Tracely-ai/.claude/skills/keyword-clustering/SKILL.md

keyword-clustering

Skill1.2k starsChanged 20 days agoMITView on GitHub

Cluster keywords by intent and map them to existing or proposed pages.

We split this skill into the rules it gives an agent

12 rules. 4 are common, 3 are distinctive, and 5 are very specific to this project.

  • 4

    Common

    Most of the 8 edited versions we read keep these.

  • 3

    Distinctive

    This author's own choices. Worth a look.

  • 5

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    If keywords are not provided, use list_saved_keywords for saved sets, research_keywords for seed discovery, or get_ranked_keywords when the …

    line 18
  2. Very specific:

    Use get_search_console_performance (dimensions ["query","page"]) when Search Console is connected to start from real queries and the pages …

    line 33
  3. Very specific:

    Use get_ranked_keywords for domain/page-driven clustering.

    line 34
  4. Very specific:

    Use search_local_businesses and get_local_serp_results when proximity, local packs, or Google Business results determine whether terms …

    line 35
  5. Common:

    Remove duplicates, irrelevant terms, and terms that clearly require a different product or audience.

    line 36
  6. Distinctive:

    Do-not-target / later bucket, if weak or off-strategy

    line 45
  7. Very specific:

    Ask before applying cluster tags with save_keywords.

    line 47
  8. Distinctive:

    Start with a short mapping summary:

    line 51
  9. Common:

    Do not over-cluster tiny keyword sets.

    line 73
  10. Common:

    Do not rely on lexical similarity alone.

    line 74
  11. Distinctive:

    Do not replace tags broadly without explicit confirmation.

    line 75
  12. Common:

    If existing URL data is missing, label target pages as proposed.

    line 76

“Common” is measured: kept by at least 6 in 10 of the 8 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

OpenSEO Keyword Clustering

Goal

Group keywords into page-level clusters and decide which existing or new page should target each cluster. This is a keyword mapping workflow, not just a semantic grouping exercise.

Required inputs

  • projectId
  • A keyword list, saved keyword tag, seed topic, or target domain
  • Optional existing URLs/pages to map against

If keywords are not provided, use list_saved_keywords for saved sets, research_keywords for seed discovery, or get_ranked_keywords when the user starts from a target domain.

OpenSEO MCP tools

  • list_saved_keywords: fetch an existing keyword set, optionally filtered by tags.
  • research_keywords: expand a seed when the user starts from a topic.
  • get_ranked_keywords: gather exact ranking keywords and URLs when the user starts from a domain or page.
  • get_search_console_performance: when Search Console is connected, pull real queries with dimensions: ["query","page"] to map terms to the pages already earning impressions and to surface cannibalization (one query splitting clicks across multiple URLs).
  • get_serp_results: validate whether keywords belong on the same page by checking SERP overlap and intent.
  • get_local_serp_results: use for local SEO clusters when Maps/local-pack intent should affect page mapping.
  • save_keywords: optionally tag final clusters after user confirmation.

Workflow

  1. Gather the candidate keyword set.
    • Use get_search_console_performance (dimensions ["query","page"]) when Search Console is connected to start from real queries and the pages already ranking for them.
    • Use get_ranked_keywords for domain/page-driven clustering.
    • Use search_local_businesses and get_local_serp_results when proximity, local packs, or Google Business results determine whether terms belong on location pages.
  2. Remove duplicates, irrelevant terms, and terms that clearly require a different product or audience.
  3. Build clusters around intent and page type:
    • Same SERP intent and similar ranking pages belong together.
    • Different intent, buyer stage, or SERP format should be split.
    • Similar words do not guarantee the same cluster.
  4. For important borderline terms, use a small get_serp_results batch to check overlap.
  5. Assign each cluster to:
    • Existing URL, if supplied and appropriate
    • New page recommendation, if no existing page fits
    • Do-not-target / later bucket, if weak or off-strategy
  6. Identify cannibalization risk when multiple pages would target the same intent. When Search Console is connected, confirm it from real data with get_search_console_performance (dimensions: ["query","page"]) — the same query sending impressions to multiple URLs.
  7. Ask before applying cluster tags with save_keywords.

Output format

Start with a short mapping summary:

  • Number of clusters
  • Pages to create
  • Existing pages to update
  • Cannibalization or consolidation issues

Then include:

ClusterPrimary keywordSecondary keywordsIntentTarget pagePriorityNotes

For each cluster, include a recommended page brief:

  • Page type
  • Searcher problem
  • Required sections
  • Internal-link opportunities
  • Save/tag suggestion

Guardrails

  • Do not over-cluster tiny keyword sets. If there are fewer than 10 usable terms, produce a simple map.
  • Do not rely on lexical similarity alone. SERP intent wins.
  • Do not replace tags broadly without explicit confirmation.
  • If existing URL data is missing, label target pages as proposed.

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