Seo Research logo

Seo Research

Organization
hyperfx-ai
seo-research

Data-driven SEO research and analysis through the Hyper MCP — keyword research, competitor analysis, content planning, AI search visibility (ChatGPT, Claude, Perplexity, Google AI Overviews), backlink trends, search intent classification, page speed / Core Web Vitals, and full site audits. Use when the user asks for an SEO audit, keyword research, content strategy, competitor benchmarks, brand visibility in AI search, ranking history, or any organic search analysis.

Overview

Publisherhyperfx-ai
Repositorymarketing-skills
Skill nameseo-research
Stars
107
Forks
21
Bundled files
5
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.

  • 5 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by hyperfx-ai on GitHub. Read the source before you install it.

Installation

Install the Seo 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/hyperfx-ai/marketing-skills/tree/main/skills/seo-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/hyperfx-ai/marketing-skills.git /tmp/marketing-skills
mkdir -p .claude/skills
cp -r /tmp/marketing-skills/skills/seo-research .claude/skills/seo-research
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Seo 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 Seo 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 Seo 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.

SEO Research

Data-driven SEO research and analysis. Every recommendation must be backed by real data from the HyperSEO tools exposed by the Hyper MCP. Never guess metrics, never assume rankings, never recommend without evidence.

Out of scope — defer to other skills

RequestSend them to
Google Ads campaign creationgoogle-ads
Meta (Facebook / Instagram) adsmeta-ads
Competitor ad research from the Meta Ads Librarymeta-ads-library
Pinterest Ads campaignpinterest-ads
Write the blog post itselfblog-generation
Competitor monitoring, battle cards, non-SEO surfacescompetitor-intel
Organic Instagram / TikTok / LinkedIn publishinginstagram, tiktok, linkedin

Requirements

If hyperseo_search_volume_get is not found, the toolkit to enable is HyperSEO (hyperseo).

How to run the tools in this skill

Every tool in this skill is named by its canonical tool name. Run it with the call your surface gives you:

SurfaceFind a toolRun it
Hyper chatCall the tool by its name; search_tools(queries=["<provider> <action>"]) shows its parameters<name>(...)
MCP client (Claude, Cursor, Codex, ChatGPT)search("<what you want to do>"), then describe("<name>")call_read("<name>", {...}), call_write or call_destructive: the one that describe names in call_with
Hyper CLIhyperai search "<what you want to do>", then hyperai describe <name>hyperai call <name> --json '{...}'

If a tool is not found: in Hyper chat, find its toolkit id with discover_toolkits and pass it to enable_toolkit. On an MCP client or the CLI, tell the user which integration to connect in Hyper.

Tool surface

Tool groupTools
Keyword researchhyperseo_search_volume_get, hyperseo_keyword_difficulty_get, hyperseo_keyword_ideas_generate, hyperseo_site_keywords_search, hyperseo_intents_search
SERP & Google AI Modehyperseo_serp_results_get, hyperseo_ai_overviews_get
Competitor analysishyperseo_competitors_search, hyperseo_competitor_domains_search, hyperseo_domain_intersections_search, hyperseo_traffic_get_bulk
Domain & rankingshyperseo_domain_overview_get, hyperseo_domain_keywords_get, hyperseo_rank_history_get
Backlinkshyperseo_backlinks_history_get
AI search visibilityhyperseo_ai_search_volume_get, hyperseo_mentions_track
Technical healthhyperseo_pagespeed_get

Initial Assessment

Before running any tools, understand what the user actually needs:

  1. What is the goal? Are they researching keywords, auditing a site, planning content, analyzing competitors, or checking AI visibility?
  2. What is the domain / business? Get the actual domain. Don't infer it.
  3. What is the target market? Default to US (location code 2840) unless told otherwise.
  4. What is the current state? Are they starting from scratch or optimizing existing content?

Based on the answers, read the appropriate reference file:

User needReference
"Find keywords for…" / "What should I target?"references/keyword-research.md
"Who are my competitors?" / "Analyze [domain]"references/competitor-analysis.md
"What should I write about?" / "Content plan"references/content-seo-planning.md
"Am I visible in AI search?" / "Track my brand in ChatGPT"references/ai-search-optimization.md
"Audit my site" / "How is my domain doing?"references/site-audit.md

When the task spans multiple areas (e.g., "help me with SEO for my new site"), each area is its own step: run the site audit with references/site-audit.md first and give its findings. Read the file of the next area, keyword research and then content planning, only when the user goes on to it.

Core SEO Principles

Apply these throughout all analysis.

Search intent comes first. Every keyword has an intent. Use hyperseo_intents_search to classify at scale, then match content format to intent:

  • Informational ("what is X") → guides, explainers.
  • Commercial ("best X", "X vs Y") → comparison pages, reviews.
  • Transactional ("buy X", "X pricing") → product / landing pages.
  • Navigational ("X login") → not targetable by others.

E-E-A-T matters. Experience, Expertise, Authoritativeness, Trustworthiness. Google evaluates content quality. Recommend content strategies that demonstrate genuine expertise, not keyword-stuffed filler.

Topic authority over individual keywords. Ranking for one keyword in isolation is hard. Recommend topic clusters: a pillar page supported by related content that builds topical authority.

AI search is a separate channel. Traditional Google search volume and AI-chatbot query volume are different metrics measuring different behavior. Always check both when relevant. A keyword with low Google volume may have high AI query volume, and vice versa.

Prioritize by ROI. Low difficulty + decent volume = do first. High difficulty + high volume = long-term investment. Low volume + high difficulty = skip unless strategically important.

Tool Quick Reference

TaskToolKey output
Check search volume and CPChyperseo_search_volume_getMonthly volume, CPC, competition.
Assess ranking difficultyhyperseo_keyword_difficulty_getDifficulty score 0–100.
Generate keyword ideashyperseo_keyword_ideas_generateRelated keywords with metrics.
Find keywords for a sitehyperseo_site_keywords_searchKeywords a domain could target.
See who ranks on Googlehyperseo_serp_results_getTop positions, URLs, domains.
See Google's AI Mode answerhyperseo_ai_overviews_getThe AI Mode answer and the sources it cites. It does not show whether the normal results page has an AI Overview.
See what a domain ranks forhyperseo_domain_keywords_getCurrent rankings and traffic.
Find competing domains (by keywords)hyperseo_competitors_searchDomains ranking for similar keywords.
Find competing domains (by domain)hyperseo_competitor_domains_searchCompetitors based on shared organic keywords.
Get domain traffic metricshyperseo_domain_overview_getOrganic keywords, estimated organic traffic (ETV), traffic cost. For backlinks and rank, use hyperseo_backlinks_history_get.
Track backlink trendshyperseo_backlinks_history_getNew / lost backlinks over time.
Check AI chatbot query volumehyperseo_ai_search_volume_getMonthly queries to ChatGPT, Claude, etc.
Track brand mentions in LLMshyperseo_mentions_trackBrand citations across AI models.
Compare traffic across domainshyperseo_traffic_get_bulkOrganic / paid ETV and keyword counts per domain.
Track ranking history over timehyperseo_rank_history_getMonthly organic traffic and keyword trends.
Classify keyword search intenthyperseo_intents_searchIntent type with confidence scores.
Find keyword overlaps between domainshyperseo_domain_intersections_searchShared SERP keywords with positions.
Check page speed / Core Web Vitalshyperseo_pagespeed_getPerformance score, FCP, LCP, TBT, CLS.

Location codes

Default is US (2840). Common alternatives: UK 2826, Canada 2124, Australia 2036, Germany 2276.

Output Standards

Always present keyword data as tables:

| Keyword | Volume | Difficulty | CPC | Priority |
|---------|--------|-----------|-----|----------|

Always include the raw numbers alongside interpretation. Don't say "high volume" without showing the number.

Sort recommendations by priority. Quick wins first, long-term bets last.

When comparing domains, use side-by-side tables showing organic keywords and traffic (hyperseo_domain_overview_get), and backlinks, referring domains and domain rank (the latest row of hyperseo_backlinks_history_get).

For content plans, include: target keyword, search volume, difficulty, recommended content format, and priority tier.

Critical Rules

  1. Never guess metrics. If you don't have data, run the tool first.
  2. Research before recommending. Don't suggest keywords without checking volume and difficulty.
  3. Ask for the domain. Don't infer URLs from business names.
  4. Check both channels. When relevant, compare Google search volume with AI search volume.
  5. Be honest about difficulty. If a keyword is realistically unwinnable for the user's domain authority, say so and suggest alternatives.
  6. Think in clusters, not isolated keywords. Group related keywords into topic clusters.
  7. Validate against SERPs. Before recommending a content format, check what actually ranks with hyperseo_serp_results_get.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Seo Research AI skill do?

Data-driven SEO research and analysis through the Hyper MCP — keyword research, competitor analysis, content planning, AI search visibility (ChatGPT, Claude, Perplexity, Google AI Overviews), backlink trends, search intent classification, page speed / Core Web Vitals, and full site audits. Use when the user asks for an SEO audit, keyword research, content strategy, competitor benchmarks, brand visibility in AI search, ranking history, or any organic search analysis.

Why use Seo Research on TypingMind?

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

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

Which AI models can use Seo 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 Seo Research?

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

Is the Seo Research AI skill free?

Yes. It is published on GitHub by hyperfx-ai 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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