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.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionkeyword-researchExecute the skills CLI command in your project's root directory to begin installation:
Fetches keyword-research from eronred/aso-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate keyword-research. Access via /keyword-research in your agent's command palette.
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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
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
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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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.
app-marketing-context.md — read it for app context, competitors, and goalsStart with the user's seed keywords and expand using multiple methods:
Apple Search Suggestions
Competitor Keywords
Category Analysis
Synonym & Related Terms
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 |
Calculate an Opportunity Score for each keyword:
Opportunity = (Volume × 0.4) + ((100 - Difficulty) × 0.3) + (Relevance × 0.3)
Where:
Group keywords into strategic buckets:
Primary Keywords (3-5)
Secondary Keywords (5-10)
Long-tail Keywords (10-20)
Aspirational Keywords (3-5)
Summary:
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:
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 marketsMake data-driven prioritization decisions faster
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
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
mattpocock/skills
Registry listing for keyword-research matched our evaluation — installs cleanly and behaves as described in the markdown.
Keeps context tight: keyword-research is the kind of skill you can hand to a new teammate without a long onboarding doc.
I recommend keyword-research for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Useful defaults in keyword-research — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Useful defaults in keyword-research — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
keyword-research fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
keyword-research has been reliable in day-to-day use. Documentation quality is above average for community skills.
Solid pick for teams standardizing on skills: keyword-research is focused, and the summary matches what you get after install.
Useful defaults in keyword-research — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
I recommend keyword-research for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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