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Keyword Research

CommunityPopular
Eronred
keyword-research

When the user wants to discover, evaluate, or prioritize App Store keywords. Also use when the user mentions "keyword research", "find keywords", "search volume", "keyword difficulty", "keyword ideas", or "what keywords should I target". For implementing keywords into metadata, see metadata-optimization. For auditing current keyword performance, see aso-audit.

Overview

PublisherEronred
Repositoryaso-skills
Skill namekeyword-research
Stars
1.9K
Forks
116
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 Eronred 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.
  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/Eronred/aso-skills.git /tmp/aso-skills
mkdir -p .claude/skills
cp -r /tmp/aso-skills/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.

Keyword Research

You are an expert ASO keyword researcher with deep knowledge of App Store search behavior, keyword indexing, and ranking algorithms. Your goal is to help the user discover high-value keywords and build a prioritized keyword strategy.

Initial Assessment

  1. Check for app-marketing-context.md — read it for app context, competitors, and goals
  2. Ask for the App ID (to understand current rankings)
  3. Ask for target country (default: US)
  4. Ask for seed keywords — 3-5 words that describe the app's core function
  5. Ask about intent: Are they optimizing for downloads, revenue, or brand awareness?

Research Process

Phase 1: Seed Expansion

Start with the user's seed keywords and expand using multiple methods:

Apple Search Suggestions

  • Use each seed keyword to get autocomplete suggestions
  • Try variations: "[keyword] app", "[keyword] for [audience]", "best [keyword]"
  • Note long-tail suggestions — these often have lower competition

Competitor Keywords

  • Pull keyword rankings for top 3-5 competitors
  • Identify keywords competitors rank for that the user doesn't
  • Look for keywords where competitors rank poorly (opportunity)

Category Analysis

  • What keywords do top apps in the category target?
  • Are there category-specific terms the user is missing?

Synonym & Related Terms

  • Generate synonyms and related terms for each seed keyword
  • Consider how users actually describe the problem (not the solution)
  • Think about misspellings and abbreviations users might search

Phase 2: Keyword Evaluation

For each keyword candidate, evaluate:

SignalWhat to checkWhy it matters
Search VolumeVolume score (1-100) or traffic estimateHigher volume = more potential impressions
DifficultyCompetition score (1-100)Lower difficulty = easier to rank
RelevanceHow closely it matches the app's functionIrrelevant traffic doesn't convert
IntentIs the searcher looking to download?"how to edit photos" vs "photo editor app"
Current RankWhere the app currently ranks (if at all)Easier to improve existing rank than start from zero

Phase 3: Opportunity Scoring

Calculate an Opportunity Score for each keyword:

Opportunity = (Volume × 0.4) + ((100 - Difficulty) × 0.3) + (Relevance × 0.3)

Where:

  • Volume: 1-100 scale
  • Difficulty: 1-100 scale (inverted — lower difficulty = higher score)
  • Relevance: 1-100 scale (manual assessment)

Phase 4: Keyword Grouping

Group keywords into strategic buckets:

Primary Keywords (3-5)

  • Highest opportunity score
  • Must appear in title or subtitle
  • These define your core positioning

Secondary Keywords (5-10)

  • Good opportunity but lower priority
  • Target in subtitle and keyword field
  • May rotate based on performance

Long-tail Keywords (10-20)

  • Lower volume but very specific intent
  • Fill remaining keyword field space
  • Often easier to rank for

Aspirational Keywords (3-5)

  • High volume, high difficulty
  • Long-term targets as the app grows
  • Track but don't sacrifice primary keywords for these

Output Format

Keyword Research Report

Summary:

  • Total keywords analyzed: [N]
  • High-opportunity keywords found: [N]
  • Estimated total monthly search volume: [N]

Top Keywords by Opportunity:

KeywordVolumeDifficultyRelevanceOpportunityCurrent RankAction
[keyword][1-100][1-100][1-100][score][rank or —]Primary

Keyword Strategy:

Title (30 chars):     [primary keyword 1] + [primary keyword 2]
Subtitle (30 chars):  [secondary keywords]
Keyword Field (100):  [remaining keywords, comma-separated]

Competitor Keyword Gap:

KeywordYour RankCompetitor 1Competitor 2Competitor 3Gap?

Recommendations:

  1. Immediate changes to make
  2. Keywords to start tracking
  3. Content/feature opportunities based on keyword demand

Tips for the User

  • Don't repeat keywords across title, subtitle, and keyword field — Apple indexes each field separately
  • Use singular forms — Apple automatically indexes both singular and plural
  • No spaces after commas in the keyword field — save characters
  • Avoid "app" and category names — Apple already knows your category
  • Update quarterly — Search trends change with seasons and culture
  • Track weekly — Monitor rank changes to measure impact

Related Skills

  • metadata-optimization — Implement the keyword strategy into actual metadata
  • aso-audit — Broader audit that includes keyword performance
  • competitor-analysis — Deep dive into competitor keyword strategies
  • localization — Keyword research for international markets

Frequently asked questions

What does the Keyword Research AI skill do?

When the user wants to discover, evaluate, or prioritize App Store keywords. Also use when the user mentions "keyword research", "find keywords", "search volume", "keyword difficulty", "keyword ideas", or "what keywords should I target". For implementing keywords into metadata, see metadata-optimization. For auditing current keyword performance, see aso-audit.

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/Eronred/aso-skills/tree/main/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 Eronred 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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