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Keyword Cluster

Community
indranilbanerjee
keyword-cluster

Cluster keywords into pillar and spoke pages by SERP overlap via script. "cluster these keywords"

Overview

Publisherindranilbanerjee
Repositorydigital-marketing-pro
Skill namekeyword-cluster
Stars
866
Forks
138
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by indranilbanerjee on GitHub. Read the source before you install it.

Installation

Install the Keyword Cluster AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/indranilbanerjee/digital-marketing-pro/tree/main/skills/keyword-cluster
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/indranilbanerjee/digital-marketing-pro.git /tmp/digital-marketing-pro
mkdir -p .claude/skills
cp -r /tmp/digital-marketing-pro/skills/keyword-cluster .claude/skills/keyword-cluster
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Keyword Cluster in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Keyword Cluster on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Keyword Cluster is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

/digital-marketing-pro:keyword-cluster

Script location. If your host does not set ${CLAUDE_PLUGIN_ROOT}, the scripts are in this plugin's scripts/ folder, next to skills/. Run them with Python 3.10 or newer: python, or python3 where python is not found (macOS, many Linux systems).

Purpose

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.

Context efficiency

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.

When to Use

  • Onboarding a new content programme — turn a 20-keyword brief into a structured topical hub
  • Auditing an existing content library for cannibalisation (two pages competing for the same intent)
  • Designing a pillar+spokes architecture before any writing begins
  • Staging programmatic SEO across hundreds of variants (use this once per topic family)
  • Reorganising an existing site's internal-link graph

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).

Brand context (auto-applied)

  1. Read ~/.claude-marketing/brands/_active-brand.json for the active slug, then load ~/.claude-marketing/brands/{slug}/profile.json
  2. If no brand exists: ask "Set up a brand first (/digital-marketing-pro:brand-setup)?" — or proceed with defaults
  3. Apply industry-specific guidance from skills/context-engine/industry-profiles.md
  4. Apply skills/context-engine/compliance-rules.md to filter out banned terminology before clustering

Inputs

InputSourceRequired?
Seed keywords (3–500)CSV with keyword column (optional: volume, kd, intent)yes
SERP results per keywordJSON: {keyword: [top result URLs]} from any rank-tracker / Ahrefs / Semrush exportstrongly recommended — without this the script falls back to lexical clustering, which is lower-confidence
Target country / languageFrom brand profileoptional override
Min volume / max KD filtersCLI flagsoptional
Overlap thresholdCLI 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.

Process (10 steps, numbered-file output)

All outputs go to ${CLAUDE_PLUGIN_DATA}/{brand}/seo/keyword-cluster/{YYYY-MM-DD}/.

  1. 00-input.md — capture seeds, source, filters, brand context, run timestamp
  2. 01-seed-expansion.md — if seeds < 20, expand via brand's keyword-research MCP (Ahrefs getRelatedKeywords, etc.) to ~50–200; otherwise skip. Document expansion source.
  3. 02-filtered.csv — apply min-volume / max-KD / banned-word filters. Save the filtered set as CSV (this is what the script consumes).
  4. 03-serps.json — fetch top-10 SERP URLs per keyword via the connected rank-tracker (skip if SERPs already provided). Budget guard: if estimated cost > 500 credits, surface the cost and ask "Continue? (y/N — default N)" before fetching.
  5. 04-cluster-run.json — run the script:
    bash
    python "${CLAUDE_PLUGIN_ROOT}/scripts/keyword_cluster.py" \
        --keywords "${CLAUDE_PLUGIN_DATA}/{brand}/seo/keyword-cluster/{date}/02-filtered.csv" \
        --serps "${CLAUDE_PLUGIN_DATA}/{brand}/seo/keyword-cluster/{date}/03-serps.json" \
        --overlap 0.4 \
        --min-volume {profile.min_volume or 0} \
        --max-kd {profile.max_kd or 100} \
        --out "${CLAUDE_PLUGIN_DATA}/{brand}/seo/keyword-cluster/{date}/04-cluster-run.json"
  6. 05-quality-scorecard.md — read the quality_scorecard block from 04-cluster-run.json. If status: needs_review, diagnose:
    • cannibalisation: fail → two clusters share pillar+intent. Merge them or reassign the lower-priority cluster's pillar.
    • orphan: fail → a multi-keyword cluster has 0 spokes. Re-tokenise its members or lower --overlap.
    • coverage: fail → < 80% of seeds clustered. Lower --overlap to 0.3 or expand seeds.
    • anchor_diversity: fail → pillar names too similar. Rewrite cluster names with synonym variation.
    • fragmentation_warning: true (pillar-only > 50%) → overlap threshold too strict. Try --overlap 0.3 first.
  7. 06-pillar-pages.md — for each cluster with priority_score >= 0.5, draft a one-paragraph pillar page brief (intent, audience, length target, key questions to answer). These feed /digital-marketing-pro:content-brief.
  8. 07-internal-link-map.md — table view of internal_link_targets from the script output. Per cluster: which other clusters to link out to + suggested anchor text. This is the file your dev team or CMS template should consume.
  9. 08-build-order.md — sorted by priority_score descending. Recommended build cadence: top 10% in Q1, next 30% in Q2, remainder backlog.
  10. PLAN.md — single-page summary: stats + scorecard + top 5 priority clusters + handoff to next skill in chain.

Output format

${CLAUDE_PLUGIN_DATA}/{brand}/seo/keyword-cluster/2026-06-04/
├── 00-input.md
├── 01-seed-expansion.md      (only if seeds expanded)
├── 02-filtered.csv
├── 03-serps.json             (if SERP mode)
├── 04-cluster-run.json       (raw script output)
├── 05-quality-scorecard.md
├── 06-pillar-pages.md
├── 07-internal-link-map.md
├── 08-build-order.md
└── PLAN.md                   (the deliverable)

PLAN.md is what you hand to the brand / client / next skill. Everything else is auditable intermediate state.

Quality scorecard (the four gates)

Every run produces a scorecard from scripts/keyword_cluster.py. All four must pass for status: ready:

GateWhat it checksWhy it matters
cannibalisationNo two clusters share the same (pillar, primary_intent) pairPrevents you from writing two pages competing for the same SERP
orphanEvery multi-keyword cluster has ≥1 spoke (pillar-only clusters are exempt and tagged)Catches clustering bugs where a cluster head has no supporting topics
coverage≥ 80% of input seeds are assigned to at least one clusterCatches "junk" seeds and overly strict thresholds
anchor_diversityEach multi-keyword cluster has ≥ 2 anchor-text variants suggestedStops anchor-text over-optimisation across the internal-link graph

A fragmentation_warning: true (pillar-only > 50%) is a soft signal — the run is valid but you should consider lowering --overlap and re-running.

After the cluster

Ask: "Would you like me to:

  • Brief the top pillar pages? (/digital-marketing-pro:content-brief)
  • Start writing the highest-priority pillar? (/digital-marketing-pro:content-engine)
  • Apply the internal-link map to your CMS? (/digital-marketing-pro:seo-implement)
  • Schedule a quarterly re-run via /digital-marketing-pro:seo-drift?"

Chain handoffs

This skill is a producer in the chain:

  1. /digital-marketing-pro:keyword-research — generate seeds
  2. /digital-marketing-pro:keyword-cluster — this skill
  3. /digital-marketing-pro:content-brief — consumes PLAN.md + 06-pillar-pages.md to brief each pillar
  4. /digital-marketing-pro:content-engine — drafts the content
  5. /digital-marketing-pro:seo-implement — applies the internal-link map to the CMS

Tips & caveats

  • SERP mode is strictly better than lexical mode. Lexical clustering can't see that "shopify seo" and "ecommerce platform seo" target overlapping SERPs while "shopify themes" doesn't.
  • Overlap threshold defaults are conservative. If you get fragmentation_warning: true, lower to 0.3 first. If you get cannibalisation: fail with too few clusters, raise to 0.5.
  • The priority score isn't a ranking — it's a starting build order. A cluster with priority_score: 0.3 may still be your highest-conversion opportunity if it maps to a high-margin product line. Use the brand profile's business_goals to override mechanically.
  • Don't run this on raw GSC query exports without filtering first. GSC dumps thousands of long-tail variants of the same query — they'll all cluster together and produce a single mega-cluster.
  • Pillar-only clusters are valid — they represent distinct intents that simply lack spoke candidates in your seed set. Add seeds via Step 2 expansion if you want spokes.
  • The internal-link map is suggestions, not commands. Final anchor text should be reviewed for brand voice (apply the brand profile's voice fields + skills/context-engine/guidelines-framework.md).

Agents used

  • seo-specialist (primary) — interpretation + final pillar-page recommendations
  • competitive-intel — for SERP-overlap reasoning when results look surprising
  • brand-guardian — anchor-text review against banned-term lists

See also

  • /digital-marketing-pro:keyword-research — generates seeds (use first)
  • /digital-marketing-pro:content-brief — consumes the cluster plan (use next)
  • /digital-marketing-pro:seo-implement — applies internal-link map to CMS
  • /digital-marketing-pro:seo-drift — re-run quarterly to detect cluster drift
  • scripts/keyword_cluster.py — the underlying clustering engine

Frequently asked questions

What does the Keyword Cluster AI skill do?

Cluster keywords into pillar and spoke pages by SERP overlap via script. "cluster these keywords"

Why use Keyword Cluster on TypingMind?

Because you install it once and use it with any model. Keyword Cluster is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Keyword Cluster in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/indranilbanerjee/digital-marketing-pro/tree/main/skills/keyword-cluster. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Keyword Cluster?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Keyword Cluster?

As many as you like. As long as a model supports skills, you can use Keyword Cluster with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Keyword Cluster AI skill free?

Yes. It is published on GitHub by indranilbanerjee under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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