app-store-optimization – OpenClaw Skill

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name: app-store-optimization description: App Store Optimization toolkit for researching keywords, optimizing metadata, and tracking mobile app performance on Apple App Store and Google Play Store. triggers:

• ASO
• app store optimization
• app store ranking
• app keywords
• app metadata
• play store optimization
• app store listing
• improve app rankings
• app visibility
• app store SEO
• mobile app marketing
• app conversion rate

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App Store Optimization (ASO)

ASO tools for researching keywords, optimizing metadata, analyzing competitors, and improving app store visibility on Apple App Store and Google Play Store.

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Table of Contents

• Keyword Research Workflow
• Metadata Optimization Workflow
• Competitor Analysis Workflow
• App Launch Workflow
• A/B Testing Workflow
• Before/After Examples
• Tools and References

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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

Factor	Weight	High Score Indicators
Relevance	35%	Describes core app function
Volume	25%	10,000+ monthly searches
Competition	25%	Top 10 apps have <4.5 avg rating
Conversion	15%	Transactional intent ("best X app")

Keyword Placement Priority

Location	Search Weight	Character Limit
App Title	Highest	30 (iOS) / 50 (Android)
Subtitle (iOS)	High	30
Keyword Field (iOS)	High	100
Short Description (Android)	High	80
Full Description	Medium	4,000

See: references/keyword-research-guide.md

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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

Field	Apple App Store	Google Play Store
Title	30 characters	50 characters
Subtitle	30 characters	N/A
Short Description	N/A	80 characters
Keywords	100 characters	N/A
Promotional Text	170 characters	N/A
Full Description	4,000 characters	4,000 characters
What's New	4,000 characters	500 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

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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 Area	Data Points
Keywords	Title keywords, description frequency
Metadata	Character utilization, keyword density
Visuals	Icon style, screenshot count/style
Ratings	Average rating, total count, velocity
Reviews	Top praise, top complaints

Gap Analysis Template

Opportunity Type	Example	Action
Keyword gap	"habit tracker" (40% coverage)	Add to keyword field
Feature gap	Competitor lacks widget	Highlight in screenshots
Visual gap	No videos in top 5	Create app preview
Messaging gap	None mention "free"	Test free positioning

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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

Category	Items
Metadata	Title, subtitle, description, keywords
Visual Assets	Icon, screenshots (all sizes), video
Compliance	Age rating, privacy policy, content rights
Technical	App binary, signing certificates
Analytics	SDK integration, event tracking
Marketing	Press kit, social content, email ready

Launch Timing Considerations

Factor	Recommendation
Day of week	Tuesday-Wednesday (avoid weekends)
Time of day	Morning in target market timezone
Seasonal	Align with relevant category seasons
Competition	Avoid major competitor launch dates

See: references/aso-best-practices.md

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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

Element	Conversion Impact	Test Complexity
App Icon	10-25% lift possible	Medium (design needed)
Screenshot 1	15-35% lift possible	Medium
Title	5-15% lift possible	Low
Short Description	5-10% lift possible	Low
Video	10-20% lift possible	High

Sample Size Quick Reference

Baseline CVR	Impressions 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

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Before/After Examples

Title Optimization

Productivity App:

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

Fitness App:

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

Subtitle Optimization (iOS)

Version	Subtitle	Analysis
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

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

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Tools and References

Scripts

Script	Purpose	Usage
keyword_analyzer.py	Analyze keywords for volume and competition	python keyword_analyzer.py --keywords "todo,task,planner"
metadata_optimizer.py	Validate metadata character limits and density	python metadata_optimizer.py --platform ios --title "App Title"
competitor_analyzer.py	Extract and compare competitor keywords	python competitor_analyzer.py --competitors "App1,App2,App3"
aso_scorer.py	Calculate overall ASO health score	python aso_scorer.py --app-id com.example.app
ab_test_planner.py	Plan tests and calculate sample sizes	python ab_test_planner.py --cvr 0.05 --lift 0.10
review_analyzer.py	Analyze review sentiment and themes	python review_analyzer.py --app-id com.example.app
launch_checklist.py	Generate platform-specific launch checklists	python launch_checklist.py --platform ios
localization_helper.py	Manage multi-language metadata	python localization_helper.py --locales "en,es,de,ja"

References

Document	Content
platform-requirements.md	iOS and Android metadata specs, visual asset requirements
aso-best-practices.md	Optimization strategies, rating management, launch tactics
keyword-research-guide.md	Research methodology, evaluation framework, tracking

Assets

Template	Purpose
aso-audit-template.md	Structured audit checklist for app store listings

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Platform Limitations

Data Constraints

Constraint	Impact
No official keyword volume data	Estimates based on third-party tools
Competitor data limited to public info	Cannot see internal metrics
Review access limited to public reviews	No access to private feedback
Historical data unavailable for new apps	Cannot compare to past performance

Platform Behavior

Platform	Behavior
iOS	Keyword changes require app submission
iOS	Promotional text editable without update
Android	Metadata changes index in 1-2 hours
Android	No separate keyword field (use description)
Both	Algorithm changes without notice

When Not to Use This Skill

Scenario	Alternative
Web apps	Use web SEO skills
Enterprise apps (not public)	Internal distribution tools
Beta/TestFlight only	Focus on feedback, not ASO
Paid advertising strategy	Use paid acquisition skills

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Related Skills

Skill	Integration Point
content-creator	App description copywriting
marketing-demand-acquisition	Launch promotion campaigns
marketing-strategy-pmm	Go-to-market planning

App Store Optimization (ASO) Skill

Version: 1.0.0 Last Updated: November 7, 2025 Author: Claude Skills Factory

Overview

A comprehensive App Store Optimization (ASO) skill that provides complete capabilities for researching, optimizing, and tracking mobile app performance on the Apple App Store and Google Play Store. This skill empowers app developers and marketers to maximize their app's visibility, downloads, and success in competitive app marketplaces.

What This Skill Does

This skill provides end-to-end ASO capabilities across seven key areas:

1. Research & Analysis: Keyword research, competitor analysis, market trends, review sentiment
2. Metadata Optimization: Title, description, keywords with platform-specific character limits
3. Conversion Optimization: A/B testing framework, visual asset optimization
4. Rating & Review Management: Sentiment analysis, response strategies, issue identification
5. Launch & Update Strategies: Pre-launch checklists, timing optimization, update planning
6. Analytics & Tracking: ASO scoring, keyword rankings, performance benchmarking
7. Localization: Multi-language strategy, translation management, ROI analysis

Key Features

Comprehensive Keyword Research

• Search volume and competition analysis
• Long-tail keyword discovery
• Competitor keyword extraction
• Keyword difficulty scoring
• Strategic prioritization

Platform-Specific Metadata Optimization

• Apple App Store:
  • Title (30 chars)
  • Subtitle (30 chars)
  • Promotional Text (170 chars)
  • Description (4000 chars)
  • Keywords field (100 chars)
• Google Play Store:
  • Title (50 chars)
  • Short Description (80 chars)
  • Full Description (4000 chars)
• Character limit validation
• Keyword density analysis
• Multiple optimization strategies

Competitor Intelligence

• Automated competitor discovery
• Metadata strategy analysis
• Visual asset assessment
• Gap identification
• Competitive positioning

ASO Health Scoring

• 0-100 overall score
• Four-category breakdown (Metadata, Ratings, Keywords, Conversion)
• Strengths and weaknesses identification
• Prioritized action recommendations
• Expected impact estimates

Scientific A/B Testing

• Test design and hypothesis formulation
• Sample size calculation
• Statistical significance analysis
• Duration estimation
• Implementation recommendations

Global Localization

• Market prioritization (Tier 1/2/3)
• Translation cost estimation
• Character limit adaptation by language
• Cultural keyword considerations
• ROI analysis

Review Intelligence

• Sentiment analysis
• Common theme extraction
• Bug and issue identification
• Feature request clustering
• Professional response templates

Launch Planning

• Platform-specific checklists
• Timeline generation
• Compliance validation
• Optimal timing recommendations
• Seasonal campaign planning

Python Modules

This skill includes 8 powerful Python modules:

1. keyword_analyzer.py

Purpose: Analyzes keywords for search volume, competition, and relevance

Key Functions:

• analyze_keyword(): Single keyword analysis
• compare_keywords(): Multi-keyword comparison and ranking
• find_long_tail_opportunities(): Generate long-tail variations
• calculate_keyword_density(): Analyze keyword usage in text
• extract_keywords_from_text(): Extract keywords from reviews/descriptions

2. metadata_optimizer.py

Purpose: Optimizes titles, descriptions, keywords with character limit validation

Key Functions:

• optimize_title(): Generate optimal title options
• optimize_description(): Create conversion-focused descriptions
• optimize_keyword_field(): Maximize Apple's 100-char keyword field
• validate_character_limits(): Ensure platform compliance
• calculate_keyword_density(): Analyze keyword integration

3. competitor_analyzer.py

Purpose: Analyzes competitor ASO strategies

Key Functions:

• analyze_competitor(): Single competitor deep-dive
• compare_competitors(): Multi-competitor analysis
• identify_gaps(): Find competitive opportunities
• _calculate_competitive_strength(): Score competitor ASO quality

4. aso_scorer.py

Purpose: Calculates comprehensive ASO health score

Key Functions:

• calculate_overall_score(): 0-100 ASO health score
• score_metadata_quality(): Evaluate metadata optimization
• score_ratings_reviews(): Assess rating quality and volume
• score_keyword_performance(): Analyze ranking positions
• score_conversion_metrics(): Evaluate conversion rates
• generate_recommendations(): Prioritized improvement actions

5. ab_test_planner.py

Purpose: Plans and tracks A/B tests for ASO elements

Key Functions:

• design_test(): Create test hypothesis and structure
• calculate_sample_size(): Determine required visitors
• calculate_significance(): Assess statistical validity
• track_test_results(): Monitor ongoing tests
• generate_test_report(): Create comprehensive test reports

6. localization_helper.py

Purpose: Manages multi-language ASO optimization

Key Functions:

• identify_target_markets(): Prioritize localization markets
• translate_metadata(): Adapt metadata for languages
• adapt_keywords(): Cultural keyword adaptation
• validate_translations(): Character limit validation
• calculate_localization_roi(): Estimate investment returns

7. review_analyzer.py

Purpose: Analyzes user reviews for actionable insights

Key Functions:

• analyze_sentiment(): Calculate sentiment distribution
• extract_common_themes(): Identify frequent topics
• identify_issues(): Surface bugs and problems
• find_feature_requests(): Extract desired features
• track_sentiment_trends(): Monitor changes over time
• generate_response_templates(): Create review responses

8. launch_checklist.py

Purpose: Generates comprehensive launch and update checklists

Key Functions:

• generate_prelaunch_checklist(): Complete submission validation
• validate_app_store_compliance(): Check guidelines compliance
• create_update_plan(): Plan update cadence
• optimize_launch_timing(): Recommend launch dates
• plan_seasonal_campaigns(): Identify seasonal opportunities

Installation

For Claude Code (Desktop/CLI)

Project-Level Installation

# Copy skill folder to project
cp -r app-store-optimization /path/to/your/project/.claude/skills/

# Claude will auto-load the skill when working in this project

User-Level Installation (Available in All Projects)

# Copy skill folder to user-level skills
cp -r app-store-optimization ~/.claude/skills/

# Claude will load this skill in all your projects

For Claude Apps (Browser)

1. Use the skill-creator skill to import the skill
2. Or manually import via Claude Apps interface

Verification

To verify installation:

# Check if skill folder exists
ls ~/.claude/skills/app-store-optimization/

# You should see:
# SKILL.md
# keyword_analyzer.py
# metadata_optimizer.py
# competitor_analyzer.py
# aso_scorer.py
# ab_test_planner.py
# localization_helper.py
# review_analyzer.py
# launch_checklist.py
# sample_input.json
# expected_output.json
# HOW_TO_USE.md
# README.md

Usage Examples

Example 1: Complete Keyword Research

Hey Claude—I just added the "app-store-optimization" skill. Can you research keywords for my fitness app? I'm targeting people who want home workouts, yoga, and meal planning. Analyze top competitors like Nike Training Club and Peloton.

What Claude will do:

• Use keyword_analyzer.py to research keywords
• Use competitor_analyzer.py to analyze Nike Training Club and Peloton
• Provide prioritized keyword list with search volumes, competition levels
• Identify gaps and long-tail opportunities
• Recommend primary keywords for title and secondary keywords for description

Example 2: Optimize App Store Metadata

Hey Claude—I just added the "app-store-optimization" skill. Optimize my app's metadata for both Apple App Store and Google Play Store:
- App: FitFlow
- Category: Health & Fitness
- Features: AI workout plans, nutrition tracking, progress photos
- Keywords: fitness app, workout planner, home fitness

What Claude will do:

• Use metadata_optimizer.py to create optimized titles (multiple options)
• Generate platform-specific descriptions (short and full)
• Optimize Apple's 100-character keyword field
• Validate all character limits
• Calculate keyword density
• Provide before/after comparison

Example 3: Calculate ASO Health Score

Hey Claude—I just added the "app-store-optimization" skill. Calculate my app's ASO score:
- Average rating: 4.3 stars (8,200 ratings)
- Keywords in top 10: 4
- Keywords in top 50: 15
- Conversion rate: 3.8%
- Title: "FitFlow - Home Workouts"
- Description: 1,500 characters with 3 keyword mentions

What Claude will do:

• Use aso_scorer.py to calculate overall score (0-100)
• Break down by category (Metadata: X/25, Ratings: X/25, Keywords: X/25, Conversion: X/25)
• Identify strengths and weaknesses
• Generate prioritized recommendations
• Estimate impact of improvements

Example 4: A/B Test Planning

Hey Claude—I just added the "app-store-optimization" skill. I want to A/B test my app icon. My current conversion rate is 4.2%. How many visitors do I need and how long should I run the test?

What Claude will do:

• Use ab_test_planner.py to design test
• Calculate required sample size (based on minimum detectable effect)
• Estimate test duration for low/medium/high traffic scenarios
• Provide test structure and success metrics
• Explain how to analyze results

Example 5: Review Sentiment Analysis

Hey Claude—I just added the "app-store-optimization" skill. Analyze my last 500 reviews and tell me:
- Overall sentiment
- Most common complaints
- Top feature requests
- Bugs needing immediate fixes

What Claude will do:

• Use review_analyzer.py to process reviews
• Calculate sentiment distribution
• Extract common themes
• Identify and prioritize issues
• Cluster feature requests
• Generate response templates

Example 6: Pre-Launch Checklist

Hey Claude—I just added the "app-store-optimization" skill. Generate a complete pre-launch checklist for both app stores. My launch date is March 15, 2026.

What Claude will do:

• Use launch_checklist.py to generate checklists
• Create Apple App Store checklist (metadata, assets, technical, legal)
• Create Google Play Store checklist (metadata, assets, technical, legal)
• Add universal checklist (marketing, QA, support)
• Generate timeline with milestones
• Calculate completion percentage

Best Practices

Keyword Research

1. Start with 20-30 seed keywords
2. Analyze top 5 competitors in your category
3. Balance high-volume and long-tail keywords
4. Prioritize relevance over search volume
5. Update keyword research quarterly

Metadata Optimization

1. Front-load keywords in title (first 15 characters most important)
2. Use every available character (don't waste space)
3. Write for humans first, search engines second
4. A/B test major changes before committing
5. Update descriptions with each major release

A/B Testing

1. Test one element at a time (icon vs. screenshots vs. title)
2. Run tests to statistical significance (90%+ confidence)
3. Test high-impact elements first (icon has biggest impact)
4. Allow sufficient duration (at least 1 week, preferably 2-3)
5. Document learnings for future tests

Localization

1. Start with top 5 revenue markets (US, China, Japan, Germany, UK)
2. Use professional translators, not machine translation
3. Test translations with native speakers
4. Adapt keywords for cultural context
5. Monitor ROI by market

Review Management

1. Respond to reviews within 24-48 hours
2. Always be professional, even with negative reviews
3. Address specific issues raised
4. Thank users for positive feedback
5. Use insights to prioritize product improvements

Technical Requirements

• Python: 3.7+ (for Python modules)
• Platform Support: Apple App Store, Google Play Store
• Data Formats: JSON input/output
• Dependencies: Standard library only (no external packages required)

Limitations

Data Dependencies

• Keyword search volumes are estimates (no official Apple/Google data)
• Competitor data limited to publicly available information
• Review analysis requires access to public reviews
• Historical data may not be available for new apps

Platform Constraints

• Apple: Metadata changes require app submission (except Promotional Text)
• Google: Metadata changes take 1-2 hours to index
• A/B testing requires significant traffic for statistical significance
• Store algorithms are proprietary and change without notice

Scope

• Does not include paid user acquisition (Apple Search Ads, Google Ads)
• Does not cover in-app analytics implementation
• Does not handle technical app development
• Focuses on organic discovery and conversion optimization

Troubleshooting

Issue: Python modules not found

Solution: Ensure all .py files are in the same directory as SKILL.md

Issue: Character limit validation failing

Solution: Check that you're using the correct platform ('apple' or 'google')

Issue: Keyword research returning limited results

Solution: Provide more context about your app, features, and target audience

Issue: ASO score seems inaccurate

Solution: Ensure you're providing accurate metrics (ratings, keyword rankings, conversion rate)

Version History

Version 1.0.0 (November 7, 2025)

• Initial release
• 8 Python modules with comprehensive ASO capabilities
• Support for both Apple App Store and Google Play Store
• Keyword research, metadata optimization, competitor analysis
• ASO scoring, A/B testing, localization, review analysis
• Launch planning and seasonal campaign tools

Support & Feedback

This skill is designed to help app developers and marketers succeed in competitive app marketplaces. For the best results:

1. Provide detailed context about your app
2. Include specific metrics when available
3. Ask follow-up questions for clarification
4. Iterate based on results

Credits

Developed by Claude Skills Factory Based on industry-standard ASO best practices Platform requirements current as of November 2025

License

This skill is provided as-is for use with Claude Code and Claude Apps. Customize and extend as needed for your specific use cases.

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Ready to optimize your app? Start with keyword research, then move to metadata optimization, and finally implement A/B testing for continuous improvement. The skill handles everything from pre-launch planning to ongoing optimization.

For detailed usage examples, see HOW_TO_USE.md.