MCPInstall

app-store-optimization

App Store Optimization (ASO) toolkit for researching keywords, analyzing competitor rankings, generating metadata suggestions, and improving app visibility on Apple App Store and Google Play Store. Use when the user asks about ASO, app store rankings, app metadata, app titles and descriptions, app store listings, app visibility, or mobile app marketing on iOS or Android. Supports keyword research and scoring, competitor keyword analysis, metadata optimization, A/B test planning, launch checklists, and tracking ranking changes.

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Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/alirezarezvani/claude-skills --skill app-store-optimization
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads app-store-optimization automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

App Store Optimization (ASO)


Keyword Research Workflow

Discover and evaluate keywords that drive app store visibility.

Workflow: Conduct Keyword Research

  1. Define target audience and core app functions:
    • Primary use case (what problem does the app solve)
    • Target user demographics
    • Competitive category
  2. Generate seed keywords from:
    • App features and benefits
    • User language (not developer terminology)
    • App store autocomplete suggestions
  3. Expand keyword list using:
    • Modifiers (free, best, simple)
    • Actions (create, track, organize)
    • Audiences (for students, for teams, for business)
  4. Evaluate each keyword:
    • Search volume (estimated monthly searches)
    • Competition (number and quality of ranking apps)
    • Relevance (alignment with app function)
  5. Score and prioritize keywords:
    • Primary: Title and keyword field (iOS)
    • Secondary: Subtitle and short description
    • Tertiary: Full description only
  6. Map keywords to metadata locations
  7. Document keyword strategy for tracking
  8. Validation: Keywords scored; placement mapped; no competitor brand names included; no plurals in iOS keyword field

Keyword Evaluation Criteria

FactorWeightHigh Score Indicators
Relevance35%Describes core app function
Volume25%10,000+ monthly searches
Competition25%Top 10 apps have <4.5 avg rating
Conversion15%Transactional intent ("best X app")

Keyword Placement Priority

LocationSearch Weight
App TitleHighest
Subtitle (iOS)High
Keyword Field (iOS)High
Short Description (Android)High
Full DescriptionMedium

See: references/keyword-research-guide.md


Metadata Optimization Workflow

Optimize app store listing elements for search ranking and conversion.

Workflow: Optimize App Metadata

  1. Audit current metadata against platform limits:
    • Title character count and keyword presence
    • Subtitle/short description usage
    • Keyword field efficiency (iOS)
    • Description keyword density
  2. Optimize title following formula:
    [Brand Name] - [Primary Keyword] [Secondary Keyword]
    
  3. Write subtitle (iOS) or short description (Android):
    • Focus on primary benefit
    • Include secondary keyword
    • Use action verbs
  4. Optimize keyword field (iOS only):
    • Remove duplicates from title
    • Remove plurals (Apple indexes both forms)
    • No spaces after commas
    • Prioritize by score
  5. Rewrite full description:
    • Hook paragraph with value proposition
    • Feature bullets with keywords
    • Social proof section
    • Call to action
  6. Validate character counts for each field
  7. Calculate keyword density (target 2-3% primary)
  8. Validation: All fields within character limits; primary keyword in title; no keyword stuffing (>5%); natural language preserved

Platform Character Limits

FieldApple App StoreGoogle Play Store
Title30 characters50 characters
Subtitle30 charactersN/A
Short DescriptionN/A80 characters
Keywords100 charactersN/A
Promotional Text170 charactersN/A
Full Description4,000 characters4,000 characters
What's New4,000 characters500 characters

Description Structure

PARAGRAPH 1: Hook (50-100 words)
├── Address user pain point
├── State main value proposition
└── Include primary keyword

PARAGRAPH 2-3: Features (100-150 words)
├── Top 5 features with benefits
├── Bullet points for scanability
└── Secondary keywords naturally integrated

PARAGRAPH 4: Social Proof (50-75 words)
├── Download count or rating
├── Press mentions or awards
└── Summary of user testimonials

PARAGRAPH 5: Call to Action (25-50 words)
├── Clear next step
└── Reassurance (free trial, no signup)

See: references/platform-requirements.md


Competitor Analysis Workflow

Analyze top competitors to identify keyword gaps and positioning opportunities.

Workflow: Analyze Competitor ASO Strategy

  1. Identify top 10 competitors:
    • Direct competitors (same core function)
    • Indirect competitors (overlapping audience)
    • Category leaders (top downloads)
  2. Extract competitor keywords from:
    • App titles and subtitles
    • First 100 words of descriptions
    • Visible metadata patterns
  3. Build competitor keyword matrix:
    • Map which keywords each competitor targets
    • Calculate coverage percentage per keyword
  4. Identify keyword gaps:
    • Keywords with <40% competitor coverage
    • High volume terms competitors miss
    • Long-tail opportunities
  5. Analyze competitor visual assets:
    • Icon design patterns
    • Screenshot messaging and style
    • Video presence and quality
  6. Compare ratings and review patterns:
    • Average rating by competitor
    • Common praise themes
    • Common complaint themes
  7. Document positioning opportunities
  8. Validation: 10+ competitors analyzed; keyword matrix complete; gaps identified with volume estimates; visual audit documented

Competitor Analysis Matrix

Analysis AreaData Points
KeywordsTitle keywords, description frequency
MetadataCharacter utilization, keyword density
VisualsIcon style, screenshot count/style
RatingsAverage rating, total count, velocity
ReviewsTop praise, top complaints

Gap Analysis Template

Opportunity TypeExampleAction
Keyword gap"habit tracker" (40% coverage)Add to keyword field
Feature gapCompetitor lacks widgetHighlight in screenshots
Visual gapNo videos in top 5Create app preview
Messaging gapNone mention "free"Test free positioning

App Launch Workflow

Execute a structured launch for maximum initial visibility.

Workflow: Launch App to Stores

  1. Complete pre-launch preparation (4 weeks before):
    • Finalize keywords and metadata
    • Prepare all visual assets
    • Set up analytics (Firebase, Mixpanel)
    • Build press kit and media list
  2. Submit for review (2 weeks before):
    • Complete all store requirements
    • Verify compliance with guidelines
    • Prepare launch communications
  3. Configure post-launch systems:
    • Set up review monitoring
    • Prepare response templates
    • Configure rating prompt timing
  4. Execute launch day:
    • Verify app is live in both stores
    • Announce across all channels
    • Begin review response cycle
  5. Monitor initial performance (days 1-7):
    • Track download velocity hourly
    • Monitor reviews and respond within 24 hours
    • Document any issues for quick fixes
  6. Conduct 7-day retrospective:
    • Compare performance to projections
    • Identify quick optimization wins
    • Plan first metadata update
  7. Schedule first update (2 weeks post-launch)
  8. Validation: App live in stores; analytics tracking; review responses within 24h; download velocity documented; first update scheduled

Pre-Launch Checklist

CategoryItems
MetadataTitle, subtitle, description, keywords
Visual AssetsIcon, screenshots (all sizes), video
ComplianceAge rating, privacy policy, content rights
TechnicalApp binary, signing certificates
AnalyticsSDK integration, event tracking
MarketingPress kit, social content, email ready

Launch Timing Considerations

FactorRecommendation
Day of weekTuesday-Wednesday (avoid weekends)
Time of dayMorning in target market timezone
SeasonalAlign with relevant category seasons
CompetitionAvoid major competitor launch dates

See: references/aso-best-practices.md


A/B Testing Workflow

Test metadata and visual elements to improve conversion rates.

Workflow: Run A/B Test

  1. Select test element (prioritize by impact):
    • Icon (highest impact)
    • Screenshot 1 (high impact)
    • Title (high impact)
    • Short description (medium impact)
  2. Form hypothesis:
    If we [change], then [metric] will [improve/increase] by [amount]
    because [rationale].
    
  3. Create variants:
    • Control: Current version
    • Treatment: Single variable change
  4. Calculate required sample size:
    • Baseline conversion rate
    • Minimum detectable effect (usually 5%)
    • Statistical significance (95%)
  5. Launch test:
    • Apple: Use Product Page Optimization
    • Android: Use Store Listing Experiments
  6. Run test for minimum duration:
    • At least 7 days
    • Until statistical significance reached
  7. Analyze results:
    • Compare conversion rates
    • Check statistical significance
    • Document learnings
  8. Validation: Single variable tested; sample size sufficient; significance reached (95%); results documented; winner implemented

A/B Test Prioritization

ElementConversion ImpactTest Complexity
App Icon10-25% lift possibleMedium (design needed)
Screenshot 115-35% lift possibleMedium
Title5-15% lift possibleLow
Short Description5-10% lift possibleLow
Video10-20% lift possibleHigh

Sample Size Quick Reference

Baseline CVRImpressions Needed (per variant)
1%31,000
2%15,500
5%6,200
10%3,100

Test Documentation Template

TEST ID: ASO-2025-001
ELEMENT: App Icon
HYPOTHESIS: A bolder color icon will increase conversion by 10%
START DATE: [Date]
END DATE: [Date]

RESULTS:
├── Control CVR: 4.2%
├── Treatment CVR: 4.8%
├── Lift: +14.3%
├── Significance: 97%
└── Decision: Implement treatment

LEARNINGS:
- Bold colors outperform muted tones in this category
- Apply to screenshot backgrounds for next test

Before/After Examples

Title Optimization

Productivity App:

VersionTitleAnalysis
Before"MyTasks"No keywords, brand only (8 chars)
After"MyTasks - Todo List & Planner"Primary + secondary keywords (29 chars)

Fitness App:

VersionTitleAnalysis
Before"FitTrack Pro"Generic modifier (12 chars)
After"FitTrack: Workout Log & Gym"Category keywords (27 chars)

Subtitle Optimization (iOS)

VersionSubtitleAnalysis
Before"Get Things Done"Vague, no keywords
After"Daily Task Manager & Planner"Two keywords, benefit clear

Keyword Field Optimization (iOS)

Before (Inefficient - 89 chars, 8 keywords):

task manager, todo list, productivity app, daily planner, reminder app

After (Optimized - 97 chars, 14 keywords):

task,todo,checklist,reminder,organize,daily,planner,schedule,deadline,goals,habit,widget,sync,team

Improvements:

  • Removed spaces after commas (+8 chars)
  • Removed duplicates (task manager → task)
  • Removed plurals (reminders → reminder)
  • Removed words in title
  • Added more relevant keywords

Description Opening

Before:

MyTasks is a comprehensive task management solution designed
to help busy professionals organize their daily activities
and boost productivity.

After:

Forget missed deadlines. MyTasks keeps every task, reminder,
and project in one place—so you focus on doing, not remembering.
Trusted by 500,000+ professionals.

Improvements:

  • Leads with user pain point
  • Specific benefit (not generic "boost productivity")
  • Social proof included
  • Keywords natural, not stuffed

Screenshot Caption Evolution

VersionCaptionIssue
Before"Task List Feature"Feature-focused, passive
Better"Create Task Lists"Action verb, but still feature
Best"Never Miss a Deadline"Benefit-focused, emotional

Tools and References

Scripts

ScriptPurposeUsage
keyword_analyzer.pyAnalyze keywords for volume and competitionpython keyword_analyzer.py --keywords "todo,task,planner"
metadata_optimizer.pyValidate metadata character limits and densitypython metadata_optimizer.py --platform ios --title "App Title"
competitor_analyzer.pyExtract and compare competitor keywordspython competitor_analyzer.py --competitors "App1,App2,App3"
aso_scorer.pyCalculate overall ASO health scorepython aso_scorer.py --app-id com.example.app
ab_test_planner.pyPlan tests and calculate sample sizespython ab_test_planner.py --cvr 0.05 --lift 0.10
review_analyzer.pyAnalyze review sentiment and themespython review_analyzer.py --app-id com.example.app
launch_checklist.pyGenerate platform-specific launch checklistspython launch_checklist.py --platform ios
localization_helper.pyManage multi-language metadatapython localization_helper.py --locales "en,es,de,ja"

References

DocumentContent
platform-requirements.mdiOS and Android metadata specs, visual asset requirements
aso-best-practices.mdOptimization strategies, rating management, launch tactics
keyword-research-guide.mdResearch methodology, evaluation framework, tracking

Assets

TemplatePurpose
aso-audit-template.mdStructured audit checklist for app store listings

Platform Notes

Platform / ConstraintBehavior / Impact
iOS keyword changesRequire app submission
iOS promotional textEditable without an app update
Android metadata changesIndex in 1-2 hours
Android keyword fieldNone — use description instead
Keyword volume dataEstimates only; no official source
Competitor dataPublic listings only

When not to use this skill: web apps (use web SEO), enterprise/internal apps, TestFlight-only betas, or paid advertising strategy.


Related Skills

SkillIntegration Point
content-creatorApp description copywriting
marketing-demand-acquisitionLaunch promotion campaigns
marketing-strategy-pmmGo-to-market planning

Proactive Triggers

  • No keyword optimization in title → App title is the #1 ranking factor. Include top keyword.
  • Screenshots don't show value → Screenshots should tell a story, not show UI.
  • No ratings strategy → Below 4.0 stars kills conversion. Implement in-app rating prompts.
  • Description keyword-stuffed → Natural language with keywords beats keyword stuffing.

Output Artifacts

When you ask for...You get...
"ASO audit"Full app store listing audit with prioritized fixes
"Keyword research"Keyword list with search volume and difficulty scores
"Optimize my listing"Rewritten title, subtitle, description, keyword field

Communication

All output passes quality verification:

  • Self-verify: source attribution, assumption audit, confidence scoring
  • Output format: Bottom Line → What (with confidence) → Why → How to Act
  • Results only. Every finding tagged: 🟢 verified, 🟡 medium, 🔴 assumed.

FAQ

What does the app-store-optimization skill do?

App Store Optimization (ASO) toolkit for researching keywords, analyzing competitor rankings, generating metadata suggestions, and improving app visibility on Apple App Store and Google Play Store. Use when the user asks about ASO, app store rankings, app metadata, app titles and descriptions, app store listings, app visibility, or mobile app marketing on iOS or Android. Supports keyword research and scoring, competitor keyword analysis, metadata optimization, A/B test planning, launch checklists, and tracking ranking changes.

How do I install app-store-optimization?

Run npx skills add https://github.com/alirezarezvani/claude-skills --skill app-store-optimization in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/app-store-optimization/ (personal) or .claude/skills/app-store-optimization/ (project).

Which AI agents can use app-store-optimization?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is app-store-optimization safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains app-store-optimization?

It is published by alirezarezvani in alirezarezvani/claude-skills. It has 872 recorded installs.

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