Keyword Research logo

Keyword Research

OrganizationPopular
every-app
keyword-research

Discover keyword opportunities, evaluate metrics and SERPs, and save/tag promising terms.

Overview

Publisherevery-app
Repositoryopen-seo
Skill namekeyword-research
Stars
22K
Forks
2.8K
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 every-app 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/every-app/open-seo/tree/main/.agents/skills/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/every-app/open-seo.git /tmp/open-seo
mkdir -p .claude/skills
cp -r /tmp/open-seo/.agents/skills/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.

OpenSEO Keyword Research

Goal

Turn seed topics into a prioritized keyword opportunity set using OpenSEO MCP data. The output should help the user decide what to target, what to save, and what to research next.

Required inputs

  • projectId
  • One or more seed topics, products, pages, competitors, or audience problems
  • Optional market/location/language

If projectId is missing, use list_projects first. If the target market/location/language is unclear and would materially affect keyword metrics, ask the user; otherwise use the MCP tool defaults.

Project context

The project-context tools are free and shared with the app and other agents.

  1. Call get_project_context first and ground the research in it — the business, the goal, the markets, and the competitors and key pages already saved.
  2. This skill needs business_overview and current_goal. If either is empty, run a minimal inline setup: ask the user, or infer from the site and confirm, just enough to fill them, write them back with update_project_context, then continue the research. Never front-load the full interview; suggest seo-project-setup at the end for the rest.
  3. Before spending credits, check the research log. If the same research ran within the last 30 days, reuse that result and say so instead of re-buying it.
  4. On finish, write back what is durable — a sharpened business_overview or current_goal, competitors that kept appearing in the SERPs via addCompetitors, pages the keywords should land on via addKeyPages — and append a research log entry: { appendResearchLog: { summary: "Keyword research: <seeds/market>. Verdict: <conclusion>" } }.

Deliver as a report

Deliver through the seo-report skill, saving with skill: "keyword-research". If that skill is not available, say so and stop before writing HTML.

OpenSEO MCP tools

  • research_keywords: primary discovery tool. Use 1-5 seeds per call and prefer 150 results unless the user asks for exhaustive research. For demand in one city, county, or region, call search_serp_locations and pass the returned locationName on the seed: volume, CPC, and competition become local; keyword ideas, KD, and intent stay national.
  • get_keyword_metrics: hydrate up to 700 known keywords with volume, keyword difficulty (KD), search intent, CPC, and monthly trends in one call. Use it to score candidate or known terms — including the Search Console striking-distance queries from step 1.
  • get_ranked_keywords: pull exact ranking keyword rows when a target domain or page is part of the research brief.
  • get_search_console_performance: when Search Console is connected, start from the project's real first-party demand — queries already earning impressions and near-ranking ("striking distance") terms. Pass minPosition: 5, maxPosition: 20, minImpressions: 50 so the server filters the striking-distance rows for you (Google sorts by clicks and can't filter by position itself). Then hydrate those striking-distance queries with get_keyword_metrics to attach difficulty and intent.
  • get_serp_results: inspect SERPs for the top candidate terms, especially when intent is ambiguous. Pass the same locationName for the SERP in one city, county, or region.
  • search_local_businesses, get_local_serp_results, and get_google_business_questions: use for local SEO topics when a business/location radius matters.
  • list_saved_keywords: avoid duplicating already-saved work or use existing tags as context.
  • save_keywords: save selected keywords only after explicit user confirmation.

Workflow

  1. Normalize seeds into a small set of distinct research angles. If Search Console is connected for the project, first pull get_search_console_performance with minPosition: 5, maxPosition: 20, minImpressions: 50 (default lookback), and hydrate those queries with get_keyword_metrics to attach KD and intent. That ranked, hydrated list is your fastest opportunity set — work it before broad discovery.
  2. If the request is local SEO, identify the business, location/coordinates or service area, and local categories. Research local volume for that city or county with research_keywords and locationName. Use search_local_businesses and get_local_serp_results for the most important location/keyword set instead of relying only on national keyword/SERP data.
  3. Call research_keywords for exploratory seeds. Use bulk calls when possible.
  4. Use get_keyword_metrics to hydrate a fixed keyword list — or the striking-distance queries from step 1 — with volume, KD, and intent before prioritizing.
  5. Use get_ranked_keywords when the user provides a domain/page and wants opportunities based on current rankings, near-misses, or competitor-owned terms.
  6. Remove irrelevant, duplicate, branded-only, and off-intent terms.
  7. Prioritize by practical opportunity, not volume alone:
    • Strong match to the user's product/page/topic
    • Clear search intent
    • Reasonable difficulty
    • Useful volume/CPC signal
    • SERP where the user can plausibly compete
    • For local SEO, local-pack/Maps visibility and proximity fit
  8. Use get_serp_results for high-potential or ambiguous keywords when SERP intent would change the recommendation; keep the default check small.
  9. Present a shortlist and a longer opportunity table.
  10. Ask before saving keywords. When saving, suggest concise tags such as topic:<topic>, intent:<intent>, or page:<slug>.

Output format

h1: the site or topic.

If a report template applies (see seo-report), its sections and tone replace this list.

Sections in this order:

  1. The opportunity — one or two opening sentences naming the best theme and why the site can win it now.
  2. Target these now — a table of keyword, intent, volume, KD, CPC, and the page to make. Add a bar chart comparing the volumes of the shortlist.
  3. Why these — one finding per keyword that needs justifying: the SERP or metric evidence, then the page to build.
  4. The longer opportunity list — a second table, same columns.
  5. Risks and caveats — notes: SERP intent that would change the recommendation, missing metrics written as unknown, close-variant volumes that are one bucket rather than several.
  6. What to do next — an ordered list, including whether to run keyword clustering, write a content brief, or save the chosen keywords.
  7. How this report was made — opens with the skill link line from seo-report, pointing at https://openseo.so/docs/skills/keyword-research ("OpenSEO Keyword Research skill"), then which tools returned what.

Guardrails

  • Do not invent metrics. If OpenSEO does not return a value, write unknown.
  • Do not call save_keywords without explicit confirmation.
  • Prefer business-fit and intent-fit over chasing the largest volume term.

Frequently asked questions

What does the Keyword Research AI skill do?

Discover keyword opportunities, evaluate metrics and SERPs, and save/tag promising terms.

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/every-app/open-seo/tree/main/.agents/skills/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 every-app 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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