keyword-research▌
eronred/aso-skills · updated Apr 8, 2026
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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.
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
- Check for
app-marketing-context.md— read it for app context, competitors, and goals - Ask for the App ID (to understand current rankings)
- Ask for target country (default: US)
- Ask for seed keywords — 3-5 words that describe the app's core function
- 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:
- Immediate changes to make
- Keywords to start tracking
- 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 metadataaso-audit— Broader audit that includes keyword performancecompetitor-analysis— Deep dive into competitor keyword strategieslocalization— Keyword research for international markets
How to use keyword-research on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add keyword-research
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches keyword-research from GitHub repository eronred/aso-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate keyword-research. Access the skill through slash commands (e.g., /keyword-research) or your agent's skill management interface.
Security & Verification 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 development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
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
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share 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▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.4★★★★★49 reviews- ★★★★★Yusuf Mehta· Dec 20, 2024
Registry listing for keyword-research matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Benjamin Srinivasan· Dec 16, 2024
Keeps context tight: keyword-research is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Anaya Sharma· Dec 8, 2024
I recommend keyword-research for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Shikha Mishra· Dec 4, 2024
Useful defaults in keyword-research — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Maya Diallo· Nov 15, 2024
Useful defaults in keyword-research — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Benjamin Singh· Nov 11, 2024
keyword-research fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Yusuf Iyer· Nov 7, 2024
keyword-research has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Layla Martin· Oct 26, 2024
Solid pick for teams standardizing on skills: keyword-research is focused, and the summary matches what you get after install.
- ★★★★★Nia Brown· Oct 26, 2024
Useful defaults in keyword-research — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Zara Ghosh· Oct 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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