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.

eronred/aso-skillsUpdated Apr 8, 2026

Works with

Claude CodeCursorClineWindsurfCodexGooseGitHub CopilotZed

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Install Skill

Run in your terminal

$npx skills add https://github.com/eronred/aso-skills --skill keyword-research

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Installation Guide

How to use keyword-research on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your machine
  • Node.js 16+ with npm — verify with node --version
  • Active project directory where you want to add keyword-research
2

Run the install command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/eronred/aso-skills --skill keyword-research

Fetches keyword-research from eronred/aso-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI shows a list of agents. Use arrow keys and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ────────────────
│ · Cline · Codex · Goose · Windsurf
│ ●Cursor(selected)
│ · Cursor · Aider · Continue
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/keyword-research

Restart Cursor to activate keyword-research. Access via /keyword-research in your agent's command palette.

Security Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.

Documentation

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

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:

Keyword Volume Difficulty Relevance Opportunity Current Rank Action
[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:

Keyword Your Rank Competitor 1 Competitor 2 Competitor 3 Gap?

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

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Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Steps

  1. 1Install product management skill
  2. 2Start with user story generation for known feature
  3. 3Progress to competitive analysis: research 2-3 competitors
  4. 4Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5Draft stakeholder communications and refine based on feedback
  6. 6Build template library for recurring PM tasks
  7. 7Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use when

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid when

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Related Skills

Reviews

4.449 reviews
  • Y
    Yusuf MehtaDec 20, 2024

    Registry listing for keyword-research matched our evaluation — installs cleanly and behaves as described in the markdown.

  • B
    Benjamin SrinivasanDec 16, 2024

    Keeps context tight: keyword-research is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • A
    Anaya SharmaDec 8, 2024

    I recommend keyword-research for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • S
    Shikha MishraDec 4, 2024

    Useful defaults in keyword-research — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • M
    Maya DialloNov 15, 2024

    Useful defaults in keyword-research — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • B
    Benjamin SinghNov 11, 2024

    keyword-research fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Y
    Yusuf IyerNov 7, 2024

    keyword-research has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • L
    Layla MartinOct 26, 2024

    Solid pick for teams standardizing on skills: keyword-research is focused, and the summary matches what you get after install.

  • N
    Nia BrownOct 26, 2024

    Useful defaults in keyword-research — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Z
    Zara GhoshOct 6, 2024

    I recommend keyword-research for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

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