# keyword-research

Discovers and prioritizes App Store keywords, scores ranking opportunities, and maps search terms into app metadata fields.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 3700 installs · ★ 2219
- Author: appeeky
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/appeeky/aso-skills/tree/HEAD/skills/keyword-research/SKILL.md
- Updated: 2026-10-06
- HTML: https://affpapa.org/en/nevibecode/skills/appeeky-keyword-research

## What it does

This skill collects and prioritizes App Store search queries. First, the agent reads `app-marketing-context.md`, then requests the App ID, 3 to 5 seed keywords, target country (defaulting to US), and campaign intent: downloads, revenue, or brand awareness. It expands seeds through Apple Search autocomplete suggestions, top 3-5 competitor rankings, and category terms.

Candidate keywords are scored from 1 to 100 across volume, difficulty, and relevance. The skill computes an Opportunity Score using the exact formula `(Volume × 0.4) + ((100 - Difficulty) × 0.3) + (Relevance × 0.3)`. The resulting terms are divided into primary keywords for the 30-character title, secondary terms for the 30-character subtitle, and long-tail terms for the 100-character keyword field without spaces after commas.

Relevance scoring requires manual assessment, because keyword viability depends strictly on your app's actual feature set. The final report gives a competitor keyword gap table and tactical recommendations.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add appeeky/aso-skills --skill keyword-research -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/keyword-research/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/keyword-research/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add appeeky/aso-skills --skill keyword-research -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/keyword-research/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add appeeky/aso-skills --skill keyword-research -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/keyword-research/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install appeeky/aso-skills keyword-research
```

2. **Manually: put the files in place**
- File: `.github/skills/keyword-research/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add appeeky/aso-skills --skill keyword-research -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/appeeky/aso-skills --path skills/keyword-research
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/keyword-research/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add appeeky/aso-skills --skill keyword-research -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/appeeky/aso-skills/tree/HEAD/skills/keyword-research/SKILL.md

## When to use it
- expand seed keywords using Apple autocomplete and competitor rankings
- calculate an opportunity score for candidate search terms
- allocate keywords across title, subtitle, and keyword field limits
- identify keyword ranking gaps against 3-5 competitor apps

## Example prompts
- Run keyword research for my app, here is the App ID and 3 seed keywords
- Find keywords for the US App Store and calculate the opportunity score
- Identify competitor keyword gaps and group terms into title and subtitle

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 69976e078d758b77.

BEGIN THIRD-PARTY FILE 69976e078d758b77
```text
---
name: keyword-research
description: 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.
metadata:
  version: 1.0.0
---

# 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:

| Signal | What to check | Why it matters |
|--------|--------------|----------------|
| **Search Volume** | Volume score (1-100) or traffic estimate | Higher volume = more potential impressions |
| **Difficulty** | Competition score (1-100) | Lower difficulty = easier to rank |
| **Relevance** | How closely it matches the app's function | Irrelevant traffic doesn't convert |
| **Intent** | Is the searcher looking to download? | "how to edit photos" vs "photo editor app" |
| **Current Rank** | Where the app currently ranks (if at all) | Easier to improve existing rank than start from zero |

### Phase 3: Opportunity Scoring
```
END THIRD-PARTY FILE 69976e078d758b77

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First 2694 of 5394 bytes. © appeeky · MIT · https://github.com/appeeky/aso-skills/tree/HEAD/skills/keyword-research/SKILL.md

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---
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