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Agent skill

Keyword Clustering

by petera2c in petera2c/simple-table

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

MITAuto-check passedMarketing & SEO

Install Keyword Clustering

skills CLI
$ npx skills add petera2c/simple-table --skill keyword-clustering -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install petera2c/simple-table keyword-clustering --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/petera2c/simple-table.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/keyword-clustering .claude/skills/keyword-clustering && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
keyword-clustering
GitHub stars
229
Used in
3 other repos
Token cost
~936 tokens
SKILL.md length
467 words
Files
1
Skills in repo
9
Repo updated
First seen
Licence
MIT

At a glance

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

  • Works in 7 steps: Gather the candidate keyword set. → Remove duplicates, irrelevant terms, and… → Build clusters around intent and page type → …
  • Tasks that involve Keyword research
  • SKILL.md covers Goal, Required inputs, OpenSEO MCP tools and Workflow, plus 2 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Keyword Clustering is an agent skill from petera2c/simple-table. Cluster keywords by intent and map them to existing or proposed pages.

Its SKILL.md is about 940 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Marketing & SEO, covering Keyword research. It works with Google Search Console. The repository describes itself as: Lightweight data grid/table for fast, modern web apps. The licence is MIT.

When your agent uses it

  • Tasks that involve Keyword research

Example prompts

  • “/keyword-clustering”

Workflow steps

7 steps, taken from the first numbered list in SKILL.md.

  1. Gather the candidate keyword set.
  2. Remove duplicates, irrelevant terms, and terms that clearly require a different product or audience.
  3. Build clusters around intent and page type
  4. For important borderline terms, use a small get_serp_results batch to check overlap.
  5. Assign each cluster to
  6. Identify cannibalization risk when multiple pages would target the same intent. When Search Console is connected, confirm it from real…
  7. Ask before applying cluster tags with save_keywords.

What it can do on your machine

Read from SKILL.md and the folder at commit df2dda2. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Keyword Clustering loads about 936 tokens when it runs. Until then it costs about 22 tokens; SKILL.md has 467 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~22
When it runs · the whole SKILL.md, loaded when a task matches
~936

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from petera2c/simple-table at commit df2dda2, republished under its MIT licence (© petera2c). 467 words, ~936 tokens.

Download SKILL.mdSave it as .claude/skills/keyword-clustering/SKILL.md (or your agent's skills folder).
name
keyword-clustering
description
Cluster keywords by intent and map them to existing or proposed pages.

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.
Show full SKILL.md (100 more words)Show less

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.

© petera2c, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .agents/skills/keyword-clustering of petera2c/simple-table.

Open the folder on GitHubat commit df2dda2

Used in 3 other repositories

We found 4 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 3 other GitHub owners. This page covers the copy in petera2c/simple-table, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Keyword Clustering next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Keyword Clustering compared with similar skills
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Keyword Clustering this skillpetera2c/simple-table2293 repos~936Automated safety check: PassMIT
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SEONexus-JPF/note-companion870—~2.2kAutomated safety check: PassMIT
SEO Keywordrampstackco/claude-skills941—~2kAutomated safety check: PassMIT
Mkt SEO Opsevolution-foundation/evo-nexus545—~1.1kAutomated safety check: NotesCustom licence
Google SEO APIsAgriciDaniel/claude-seo19k1 repos~4.2kAutomated safety check: PassMIT

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Works with

Categories

Questions about Keyword Clustering

What does Keyword Clustering do?

Cluster keywords by intent and map them to existing or proposed pages. Keyword Clustering is an agent skill from petera2c/simple-table. Cluster keywords by intent and map them to existing or proposed pages.

When should I use Keyword Clustering?

Keyword Clustering fits situations like: tasks that involve Keyword research.

How do I install Keyword Clustering in Claude Code?

Run `npx skills add petera2c/simple-table --skill keyword-clustering -a claude-code`. Or copy the skill folder (.agents/skills/keyword-clustering in petera2c/simple-table) into .claude/skills/keyword-clustering in your project. Claude Code loads it when a task matches its description.

How do I install Keyword Clustering in Codex?

Run `npx skills add petera2c/simple-table --skill keyword-clustering -a codex`. Or copy the skill folder (.agents/skills/keyword-clustering in petera2c/simple-table) into .agents/skills/keyword-clustering in your project. Codex loads it when a task matches its description.

Can I use Keyword Clustering in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add petera2c/simple-table --skill keyword-clustering -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/keyword-clustering, .gemini/skills/keyword-clustering, .github/skills/keyword-clustering and .opencode/skills/keyword-clustering in your project.

What does Keyword Clustering need to run?

SKILL.md names no scripts, command-line tools or credentials: Keyword Clustering is instructions for the agent only.

Does Keyword Clustering access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Keyword Clustering safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Keyword Clustering use?

Keyword Clustering is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Keyword Clustering use?

About 936 tokens (SKILL.md is roughly 3.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Keyword Clustering?

Skills that share tags, products or a category with Keyword Clustering: Blog Google (AgriciDaniel/claude-blog, 2.3k stars), SEO (Nexus-JPF/note-companion, 870 stars), SEO Keyword (rampstackco/claude-skills, 941 stars) and Mkt SEO Ops (evolution-foundation/evo-nexus, 545 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Keyword Clustering?

petera2c (a GitHub user) maintains it in petera2c/simple-table, which has 229 GitHub stars. The repository holds 9 skills in this directory. The repository was last updated on October 4, 2026.

Source: petera2c/simple-table on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.