Keyword Cluster Architect logo

Keyword Cluster Architect

Organization
Affitor
keyword-cluster-architect

Map 50-200+ keywords into topical clusters for SEO domination. Build content roadmaps for topical authority. Triggers on: "keyword research", "keyword clustering", "topical authority", "keyword map", "keyword strategy", "content roadmap for SEO", "keyword grouping", "topic clusters", "SEO keyword plan", "map my keywords", "keyword cluster", "hub and spoke content", "build topical authority", "SEO content plan", "keyword universe".

Overview

PublisherAffitor
Repositoryaffiliate-skills
Skill namekeyword-cluster-architect
Stars
705
Forks
209
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 Affitor on GitHub. Read the source before you install it.

Installation

Install the Keyword Cluster Architect 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/Affitor/affiliate-skills/tree/main/skills/blog/keyword-cluster-architect
  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/Affitor/affiliate-skills.git /tmp/affiliate-skills
mkdir -p .claude/skills
cp -r /tmp/affiliate-skills/skills/blog/keyword-cluster-architect .claude/skills/keyword-cluster-architect
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Keyword Cluster Architect 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 Architect 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 Architect 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 Cluster Architect

Map 50-200+ keywords into topical clusters grouped by search intent. Build a content roadmap for dominating a topic with hub-and-spoke architecture. Google rewards topical authority — this skill builds the strategic map that tells you exactly what content to create and in what order.

Stage

S3: Blog & SEO — This is the strategic planning layer FOR blog content. Before writing individual posts, you need a map of the entire keyword landscape organized into clusters.

When to Use

  • User wants to plan SEO content strategy for a niche
  • User asks about keyword research, clustering, or topical authority
  • User says "keyword", "SEO plan", "content roadmap", "topic cluster", "hub and spoke"
  • Before running affiliate-blog-builder — to know WHICH articles to write
  • After monopoly-niche-finder — to map the keyword universe for the winning niche

Input Schema

yaml
niche: string                 # REQUIRED — the topic to cluster
                              # e.g., "AI video tools", "email marketing for SaaS"

seed_keywords: string[]       # OPTIONAL — starting keywords to expand from
                              # Default: auto-generated from niche

depth: string                 # OPTIONAL — "quick" (50 keywords) | "standard" (100) | "deep" (200+)
                              # Default: "standard"

affiliate_products: string[]  # OPTIONAL — products you promote (to prioritize commercial keywords)
                              # Default: none

Chaining from S1 monopoly-niche-finder: Use monopoly_niche.intersection as the niche input.

Workflow

Step 1: Generate Seed Keywords

If not provided, generate 5-10 seed keywords from the niche:

  • Product-focused: "[product] review", "best [category]"
  • Problem-focused: "how to [solve problem]", "[problem] solution"
  • Comparison: "[product A] vs [product B]", "alternatives to [product]"
  • Tutorial: "how to use [product]", "[product] tutorial"

Step 2: Expand Keywords

For each seed, use web_search to discover related keywords:

  1. Search: "[seed keyword]" — note related searches, People Also Ask
  2. Search: "[seed keyword] guide" OR "[seed keyword] tutorial" — informational variants
  3. Search: "best [seed keyword]" OR "[seed keyword] review" — commercial variants

Collect 50-200+ unique keywords depending on depth.

Step 3: Classify by Intent

Read shared/references/seo-strategy.md for clustering methodology.

Classify each keyword:

  • Informational (I): Learning, how-to, what-is → blog posts, tutorials
  • Commercial (C): Comparing, evaluating, reviewing → comparison posts, reviews
  • Transactional (T): Ready to buy, pricing, discount → landing pages, deal pages
  • Navigational (N): Brand-specific, login → skip (not your traffic to capture)

Step 4: Cluster by Topic

Group keywords that share the same search intent (would be answered by the same page):

Cluster: "[Main Topic]"
  Type: [I/C/T]
  Hub keyword: [highest volume keyword]
  Supporting keywords:
    - [keyword 1] — [est. volume]
    - [keyword 2] — [est. volume]
  Content type: [blog post / comparison / review / tutorial / landing page]
  Priority: [1-5 based on volume × intent × competition]

Step 5: Build Content Roadmap

Organize clusters into a hub-and-spoke map:

  1. Identify the hub page (broadest, highest-volume cluster)
  2. Connect spoke pages (specific clusters that link back to hub)
  3. Prioritize by: commercial intent first (revenue), then informational (traffic)
  4. Estimate effort: number of articles needed, suggested publishing cadence

Step 6: Self-Validation

  • Clusters are based on actual search data, not guesses
  • Each cluster has a clear search intent (I, C, or T)
  • Hub-and-spoke structure is logical (hub is broad, spokes are specific)
  • Priority ordering makes business sense (revenue-driving content first)
  • Total content pieces are realistic for user's capacity

Output Schema

yaml
output_schema_version: "1.0.0"
keyword_clusters:
  niche: string
  total_keywords: number
  total_clusters: number

  hub:
    keyword: string
    cluster_name: string
    content_type: string
    priority: number

  clusters:
    - name: string
      intent: string          # "informational" | "commercial" | "transactional"
      hub_keyword: string
      keywords: string[]
      content_type: string    # "blog" | "comparison" | "review" | "tutorial" | "landing"
      priority: number        # 1-5
      estimated_volume: string

  content_roadmap:
    total_articles: number
    publishing_cadence: string
    priority_order: string[]  # Cluster names in order to write

  target_keywords: string[]   # Flat list of all keywords for chaining

chain_metadata:
  skill_slug: "keyword-cluster-architect"
  stage: "blog"
  timestamp: string
  suggested_next:
    - "affiliate-blog-builder"
    - "content-moat-calculator"
    - "comparison-post-writer"
    - "landing-page-creator"

Output Format

## Keyword Cluster Map: [Niche]

### Overview
- **Total keywords:** XXX
- **Clusters:** XX
- **Hub topic:** [main hub]
- **Content pieces needed:** XX articles

### Hub & Spoke Map
       [HUB: Main Topic]
      /    |    |    \
 [Spoke] [Spoke] [Spoke] [Spoke]
   |       |       |       |
 [Sub]   [Sub]   [Sub]   [Sub]

### Clusters by Priority

#### Priority 1: [Cluster Name] (Commercial Intent)
- **Hub keyword:** [keyword] — [volume]
- **Content type:** [comparison / review]
- **Keywords:** [list]
- **Article idea:** [specific title]

#### Priority 2: [Cluster Name] (Informational Intent)
[same structure]

[Continue for all clusters]

### Content Roadmap
| Week | Cluster | Article | Intent | Priority |
|---|---|---|---|---|
| 1 | [cluster] | [title] | C | 1 |
| 2 | [cluster] | [title] | C | 1 |
| 3 | [cluster] | [title] | I | 2 |

### Next Steps
- Run `content-moat-calculator` to estimate effort for topical authority
- Run `affiliate-blog-builder` for Priority 1 articles
- Run `comparison-post-writer` for commercial clusters

Error Handling

  • Niche too broad: "This niche is very broad. Let me narrow to a sub-niche for more actionable clusters. Or run monopoly-niche-finder first."
  • No search volume: "This niche may be too narrow for significant search traffic. Consider broadening slightly."
  • Too many keywords: Group aggressively into fewer clusters. Quality of clustering > quantity of keywords.
  • No commercial intent keywords: Flag as concern — hard to monetize through affiliate without commercial intent. Suggest adjacent niches.

Examples

Example 1: "Map keywords for AI video tools" → Seeds: "best AI video tools", "AI video generator", "HeyGen review". Expand to 100+ keywords. Cluster: "AI video reviews" (C), "how to make AI videos" (I), "AI video pricing" (T), "AI video vs traditional" (C). Hub: "Best AI Video Tools 2025".

Example 2: "Keyword strategy for my affiliate blog about email marketing" → Deep keyword research. Clusters: "email marketing platforms" (C), "email automation tutorials" (I), "email marketing pricing comparison" (T), "email deliverability guides" (I).

Example 3: "Plan my content roadmap" (after monopoly-niche-finder) → Pick up niche from chain. Map 100+ keywords in that intersection niche. Prioritize clusters by revenue potential.

Flywheel Connections

Feeds Into

  • affiliate-blog-builder (S3) — which articles to write and target keywords
  • comparison-post-writer (S3) — commercial clusters become comparison articles
  • content-moat-calculator (S3) — keyword count informs moat estimation
  • landing-page-creator (S4) — transactional clusters become landing pages
  • internal-linking-optimizer (S6) — cluster structure defines link architecture

Fed By

  • monopoly-niche-finder (S1) — niche to cluster keywords for
  • content-pillar-atomizer (S2) — content pillars suggest keyword areas
  • seo-audit (S6) — current ranking data reveals keyword gaps

Feedback Loop

  • seo-audit (S6) reveals ranking gaps in existing clusters → add keywords and new content to fill gaps

Quality Gate

Before delivering output, verify:

  1. Would I share this on MY personal social?
  2. Contains specific, surprising detail? (not generic)
  3. Respects reader's intelligence?
  4. Remarkable enough to share? (Purple Cow test)
  5. Irresistible offer framing? (roadmap feels actionable)

Any NO → rewrite before delivering.

References

  • shared/references/seo-strategy.md — Topical authority, clustering methodology, hub-and-spoke
  • shared/references/affiliate-glossary.md — Terminology
  • shared/references/flywheel-connections.md — Master connection map

Frequently asked questions

What does the Keyword Cluster Architect AI skill do?

Map 50-200+ keywords into topical clusters for SEO domination. Build content roadmaps for topical authority. Triggers on: "keyword research", "keyword clustering", "topical authority", "keyword map", "keyword strategy", "content roadmap for SEO", "keyword grouping", "topic clusters", "SEO keyword plan", "map my keywords", "keyword cluster", "hub and spoke content", "build topical authority", "SEO content plan", "keyword universe".

Why use Keyword Cluster Architect on TypingMind?

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

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

Which AI models can use Keyword Cluster Architect?

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 Architect?

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

Is the Keyword Cluster Architect AI skill free?

Yes. It is published on GitHub by Affitor 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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