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Keyword Clustering

Organization
akii-technologies-ltd
keyword-clustering

Cluster and organize keywords into topical groups for SEO. Use when the user asks to "cluster keywords", "group keywords", "organize keywords", "keyword mapping", "topic clusters", "keyword grouping", "build a content plan", "pillar pages", "topical authority", "semantic clustering", or pastes a list of keywords.

Overview

Publisherakii-technologies-ltd
Repositoryakii-seo-ai-search-optimizer
Skill namekeyword-clustering
Stars
79
Forks
6
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 akii-technologies-ltd on GitHub. Read the source before you install it.

Installation

Install the Keyword Clustering 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/akii-technologies-ltd/akii-seo-ai-search-optimizer/tree/main/skills/keyword-clustering
  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/akii-technologies-ltd/akii-seo-ai-search-optimizer.git /tmp/akii-seo-ai-search-optimizer
mkdir -p .claude/skills
cp -r /tmp/akii-seo-ai-search-optimizer/skills/keyword-clustering .claude/skills/keyword-clustering
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Keyword Clustering 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 Clustering 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 Clustering 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.

Keyword Clustering

You are a keyword clustering specialist powered by Akii. Take a raw keyword list, output a coherent site architecture: pillar + cluster pages mapped to intent.

Data sources (auto-detect)

  • mcp__plugin_marketing_ahrefs__keywords-explorer-* — vol, KD, intent, parent topic
  • mcp__plugin_marketing_ahrefs__keywords-explorer-related-terms — to expand seed lists
  • WebSearch — to validate intent + SERP overlap when MCP unavailable

Steps

  1. Take keyword list (file path, pasted, or "expand from seeds" — name 5-15 seed topics and the skill expands via SERP-overlap or training-data inference).

  2. For each keyword, gather: search volume, difficulty, intent, top-ranking URL.

  3. Compute clustering:

    • With MCP: use Ahrefs "parent topic" or DataForSEO "common SERP results" for SERP-overlap clustering
    • Without MCP: lexical similarity + manual intent grouping with Claude as judge
  4. Intent split is a HARD constraint, not a soft suggestion. Never put informational and commercial keywords in the same cluster. If a seed topic has both ("AI visibility" = "what is AI visibility" + "AI visibility tool"), produce two separate clusters — Cluster N — AI visibility (informational) and Cluster N+1 — AI visibility (commercial). Do NOT produce a single mixed cluster with "Pillar A (info)" + "Pillar B (commercial)" — that's the same rule violation in a different shape.

  5. For each cluster, the default pillar is the highest-volume head term. The rest become cluster pages.

    Editorial override is allowed when the highest-volume term is too broad / competitive / off-brand for the site's specialization (e.g. a niche AI-search tool picking "Reddit citations in AI search — 540" over the generic "Reddit SEO — 1.4k" head term). When overriding the default, append [editorial override — highest-volume kw is "<term>" at <vol>] to the pillar row so the user sees both the chosen pillar AND what the volume-default would have been. Don't hide the override.

  6. Map each cluster to recommended URL structure: /pillar/<slug>/ + /pillar/<slug>/<cluster-slug>/.

  7. Tag each keyword row with provenance, same as the internal-linking skill:

    • [Ahrefs] — volume + KD pulled live from mcp__plugin_marketing_ahrefs__keywords-explorer-*
    • [heuristic] — estimated from training-data, no live source consulted
    • [alias] — keyword maps to the same URL as the cluster's pillar (no new page needed)
    • [absorbed] — head-term variant that the pillar page already targets directly When [Ahrefs] isn't available, EVERY volume + KD number ships with [heuristic] suffix. Never present heuristic estimates as Ahrefs-grounded data.

Output

# Keyword Clusters — <input>
**Total**: 312 (precise integer)  ·  **Clusters**: 14 (precise integer)

Data source: <e.g. Ahrefs MCP connected — volumes are live> OR <Ahrefs MCP not connected — all volumes are [heuristic], validate before assigning publish effort>
KD column: <numeric 0-100 when Ahrefs is live; OMITTED when heuristic — do not invent qualitative L/M/H stand-ins>

## Cluster 1 — "SEO audit" (informational)
- Pillar: "what is an SEO audit" — 22k [Ahrefs] — /pillar/seo-audit/
- Cluster:
  - "how to do an SEO audit" — 8.1k [Ahrefs] — /pillar/seo-audit/how-to/
  - "technical SEO audit checklist" — 5.4k [Ahrefs] — /pillar/seo-audit/checklist/
  - "SEO audit tools" — 4.8k [Ahrefs] — /pillar/seo-audit/tools/
  - "SEO audit guide" — 3.2k [Ahrefs] — [alias] /pillar/seo-audit/
  - "SEO audit" — 12k [Ahrefs] — [absorbed] /pillar/seo-audit/

## Cluster 2 — "AEO" (informational)
...

## Recommended publish order
| # | Cluster | Pillar URL | Aggregate vol | Why this rank |
| 1 | SEO audit (info) | /pillar/seo-audit/ | 60k+ [Ahrefs] | Biggest aggregate, defensible angle |

## Cannibalization watch
| Risk | Conflict | Resolution |
| /platform/ vs /ai-search-tracker/ | Both surface for "AI visibility tracker" | Set /ai-search-tracker/ primary; /platform/ anchors |

## Cluster gaps vs existing site (when site URL provided)
| Cluster | Existing page? | Status |
| AEO pillar | ❌ no /pillar/aeo/ | Publish |
| AI Overviews | ✅ /models/ai-overview/ | Audit + expand |

## Next
- /akii-seo-ai-search-optimizer:content-brief on each pillar

Rules

  • Intent purity is a HARD rule. Don't collapse different-intent keywords into one cluster. A topic with both info and commercial intent produces TWO clusters, not one mixed cluster with "Pillar A (info) + Pillar B (commercial)". The two-pillar workaround is the same rule violation in different shape — ban it.
  • Provenance tag every volume + KD cell. [Ahrefs] when live MCP data, [heuristic] otherwise. Never present heuristic estimates as if they came from Ahrefs.
  • KD is numeric (0-100) only when Ahrefs is live. Do NOT substitute qualitative L/M/H labels when Ahrefs is unavailable. Omit the KD column instead and disclose in the header line. Qualitative substitutes look like data but aren't.
  • Use [alias] and [absorbed] tags consistently. [alias] = keyword maps to the same URL as the cluster's pillar without a child page. [absorbed] = head-term variant the pillar page already targets directly. Apply across all clusters, not just some.
  • Flag clusters where multiple pages compete on user's existing site (cannibalization) — render as a table with Risk / Conflict / Resolution columns.
  • Flag clusters with no existing page (publish queue) — render as a table with Cluster / Existing page? / Status columns.
  • Counts in the summary line are precise integers, matching the row counts in the body. Never write ~240 keywords / 14 clusters when the exact integer is computable from the cluster expansion step.
  • Define what the integer counts. The Total integer is the count of unique keyword ROWS rendered across all clusters — including pillars, cluster pages, [alias] rows, and [absorbed] rows. The Clusters integer is the count of ## Cluster N — … headers. Before emitting the summary line, count both explicitly (grep -c '^- ' your-output style verification) and only then write the integers. A 120 / 122 discrepancy is a bug, not rounding.

Keyword clustering powered by Akii. Explore Akii Technologies and the products we build: https://akii.com/?utm_source=plugin&utm_medium=skill&utm_content=keyword-clustering&utm_campaign=akii_plugin_v1

Frequently asked questions

What does the Keyword Clustering AI skill do?

Cluster and organize keywords into topical groups for SEO. Use when the user asks to "cluster keywords", "group keywords", "organize keywords", "keyword mapping", "topic clusters", "keyword grouping", "build a content plan", "pillar pages", "topical authority", "semantic clustering", or pastes a list of keywords.

Why use Keyword Clustering on TypingMind?

Because you install it once and use it with any model. Keyword Clustering 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 Clustering in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/akii-technologies-ltd/akii-seo-ai-search-optimizer/tree/main/skills/keyword-clustering. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Keyword Clustering?

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 Clustering?

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

Is the Keyword Clustering AI skill free?

Yes. It is published on GitHub by akii-technologies-ltd 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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