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Keyword Research

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sanky369
keyword-research

Build a prioritized keyword cluster map using the 6 Circles Method — keywords at the intersection of positioning alignment, customer language, competitor blind spots, workable search volume, real expertise, and business intent. Use when the user asks 'what keywords should I target', 'what should I write about', 'find SEO opportunities', 'plan my content strategy', 'what does my audience search for', or before any SEO content is written. Produces a Keyword Map: scored keywords grouped into topic clusters, each cluster mapped to the article that should target it, ready to hand to content production.

Overview

Publishersanky369
Repositoryvibe-building-skills
Skill namekeyword-research
Stars
31
Forks
7
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 sanky369 on GitHub. Read the source before you install it.

Installation

Install the Keyword Research 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/sanky369/vibe-building-skills/tree/main/skills/marketing/keyword-research
  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/sanky369/vibe-building-skills.git /tmp/vibe-building-skills
mkdir -p .claude/skills
cp -r /tmp/vibe-building-skills/skills/marketing/keyword-research .claude/skills/keyword-research
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Keyword Research 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 Research 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 Research 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 Research

Produce a keyword map: a scored, clustered list of search terms worth targeting, each tied to the content that should win it. Governing principle: a keyword is only worth targeting if it survives all six circles — aligned with the positioning, phrased in the customer's language, under-served by competitors, actually searched, winnable with the user's real expertise, and connected to revenue. Traffic that fails circle 6 is vanity.

When to use / when not to

  • Use before writing SEO content, when planning a content calendar, or when the user wants to know what their audience searches for.
  • If no positioning statement exists, run skills/marketing/positioning-angles first (or infer a provisional one and label it) — circle 1 is unworkable without it.
  • If the user wants the full organic program — link building, technical SEO, programmatic pages, domain authority — use skills/marketing/seo-strategy; this skill is the keyword-selection layer only.
  • If keywords already exist and the user wants the article written, go straight to skills/marketing/seo-content.

Intake

One batch; reuse anything already produced by skills/marketing/orchestrator or skills/marketing/positioning-angles:

  1. Your positioning statement (or: what you sell, to whom, and what makes it different).
  2. Your 2–3 closest competitors' websites.
  3. Any sources of raw customer language — support tickets, sales calls, reviews, community threads — or paste examples of how customers describe the problem.
  4. Site status: domain age, roughly how much content published, any current rankings? (Determines how competitive a keyword you can win.)
  5. Access to a keyword tool (Ahrefs, Semrush, Google Keyword Planner)? If yes, the user runs volume lookups when asked; if no, proceed with estimates.

Don't stall: with a positioning statement and a recognizable market you can draft the full map from knowledge and web research — label all volumes and difficulty as estimates and list them for the user to verify in a tool.

Workflow — the 6 Circles Method

Run the circles as sequential filters: generate wide in circles 1–2, then cut in 3–6.

Circle 1 — Positioning alignment (seed)

Expand the positioning statement into 20–40 seed keywords. Take each element of the claim (audience, problem, mechanism, outcome) and list the searches that element implies. Rule: seeds come from the angle, not the category. "Project management tool for remote teams" seeds remote-team keywords (async standups, distributed team communication), not generic project management keywords the incumbents own.

Circle 2 — Customer language (rephrase)

Rewrite every seed in the words customers actually type, harvested from the intake sources. Kill jargon: if customers say "remote work tools", drop "distributed workforce management solutions". Add the question-form variants ("how do I…", "best X for Y", "X vs Y") — those carry the clearest intent.

Circle 3 — Competitor blind spots (cut or boost)

Check each surviving keyword against the competitors' content (crawl their blog/sitemap or search site:competitor.com <keyword>).

  • Competitors rank with strong, current content → cut, unless the user has a decisively better asset.
  • Competitors ignore it or cover it thinly/outdated → boost priority. These gaps are the map's best entries.

Circle 4 — Search volume (screen)

Screen for workable volume. Heuristic (label it as such): target roughly 100–10,000 searches/month — below ~100 rarely repays an article; above ~10,000 is usually incumbent territory for a young domain. Decision rules: established domain with authority (from intake Q4) → the upper bound relaxes; brand-new domain → stay in the low hundreds-to-low-thousands band. If no tool access, estimate from autocomplete breadth, "People also ask" density, and ad presence — and flag every estimate.

Circle 5 — Expertise (cut)

Keep only keywords where the user can honestly out-write the current top results — first-hand experience, proprietary data, a named mechanism, or a sharper niche take. If the user can't add anything the top 3 results lack, cut the keyword regardless of volume.

Circle 6 — Business intent (score)

Score what remains by distance to revenue:

  • High: buying/comparison intent — "best X for Y", "X vs Y", "X pricing", "X alternative".
  • Medium: problem-aware how-tos the offer solves — "how to manage remote teams".
  • Low: informational adjacency — "remote work statistics". Keep low-intent keywords only as cluster support, never as the cluster head.

Cluster and prioritize

Group survivors into topic clusters (one head keyword + 3–8 supporting keywords that one strong article or hub can cover). Priority order: high intent + competitor blind spot first, then high intent + contested, then medium intent gaps. Propose the top 3–5 clusters and have the user confirm the order before content production starts.

Required output format

# Keyword Map — [Brand]

**Positioning it serves:** "[claim from skills/marketing/positioning-angles]"
**Volume/difficulty source:** [tool name | estimates — user to verify]

## Priority clusters
### Cluster 1: [name] — Priority: HIGH
Target article: "[working title]" — intent: [high/med] — feeds: [lead magnet / product page]
| Keyword | Role | Est. volume | Competition note | Intent |
|---|---|---|---|---|
| [head keyword] | Head | ~N/mo | [gap / thin / contested] | High |
| [supporting] | Support | ... | ... | ... |

### Cluster 2: ...
(3–5 clusters total)

## Cut list (why keywords were rejected)
- "[keyword]" — failed circle [n]: [one-line reason]
(representative sample, not exhaustive)

## Assumptions & verification queue
- [every estimated volume or inferred competitor read, listed for tool verification]

**Next step:** write Cluster 1's target article via skills/marketing/seo-content.

Quality bar (check before delivering)

  • Every cluster head passed all six circles; the failing circle is named for every cut shown.
  • No cluster head is a generic category term the incumbents own.
  • Every keyword uses customer phrasing — zero internal jargon survives.
  • All volumes labeled with their source; estimates explicitly flagged, never presented as tool data.
  • Every cluster names the article that targets it and the business asset it feeds.
  • 3–5 clusters delivered, priority-ordered and user-confirmed — not an undifferentiated keyword dump.

Integration

  • Upstream: skills/marketing/positioning-angles supplies the claim that seeds circle 1; skills/marketing/orchestrator routes here on awareness and scale paths.
  • Downstream: skills/marketing/seo-content consumes the cluster map one cluster at a time (head keyword + supports + working title cross the boundary); skills/marketing/seo-strategy consumes it as the keyword layer of a full program; skills/marketing/content-atomizer uses cluster themes to steer repurposing.

Frequently asked questions

What does the Keyword Research AI skill do?

Build a prioritized keyword cluster map using the 6 Circles Method — keywords at the intersection of positioning alignment, customer language, competitor blind spots, workable search volume, real expertise, and business intent. Use when the user asks 'what keywords should I target', 'what should I write about', 'find SEO opportunities', 'plan my content strategy', 'what does my audience search for', or before any SEO content is written. Produces a Keyword Map: scored keywords grouped into topic clusters, each cluster mapped to the article that should target it, ready to hand to content prod...

Why use Keyword Research on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/sanky369/vibe-building-skills/tree/main/skills/marketing/keyword-research. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Keyword Research?

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 Research?

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

Is the Keyword Research AI skill free?

Yes. It is published on GitHub by sanky369 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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