app-store-optimization
This skill provides a complete App Store Optimization toolkit for researching keywords, optimizing metadata, A/B testing, localization, and review analysis. It includes local installation commands such as cp -r app-store-optimization ~/.claude/skills/ and ls ~/.claude/skills/app-store-optimization/, which are fixed setup shell commands (low risk).
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:
- Research & Analysis: Keyword research, competitor analysis, market trends, review sentiment
- Metadata Optimization: Title, description, keywords with platform-specific character limits
- Conversion Optimization: A/B testing framework, visual asset optimization
- Rating & Review Management: Sentiment analysis, response strategies, issue identification
- Launch & Update Strategies: Pre-launch checklists, timing optimization, update planning
- Analytics & Tracking: ASO scoring, keyword rankings, performance benchmarking
- 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 analysiscompare_keywords(): Multi-keyword comparison and rankingfind_long_tail_opportunities(): Generate long-tail variationscalculate_keyword_density(): Analyze keyword usage in textextract_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 optionsoptimize_description(): Create conversion-focused descriptionsoptimize_keyword_field(): Maximize Apple's 100-char keyword fieldvalidate_character_limits(): Ensure platform compliancecalculate_keyword_density(): Analyze keyword integration
3. competitor_analyzer.py
Purpose: Analyzes competitor ASO strategies
Key Functions:
analyze_competitor(): Single competitor deep-divecompare_competitors(): Multi-competitor analysisidentify_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 scorescore_metadata_quality(): Evaluate metadata optimizationscore_ratings_reviews(): Assess rating quality and volumescore_keyword_performance(): Analyze ranking positionsscore_conversion_metrics(): Evaluate conversion ratesgenerate_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 structurecalculate_sample_size(): Determine required visitorscalculate_significance(): Assess statistical validitytrack_test_results(): Monitor ongoing testsgenerate_test_report(): Create comprehensive test reports
6. localization_helper.py
Purpose: Manages multi-language ASO optimization
Key Functions:
identify_target_markets(): Prioritize localization marketstranslate_metadata(): Adapt metadata for languagesadapt_keywords(): Cultural keyword adaptationvalidate_translations(): Character limit validationcalculate_localization_roi(): Estimate investment returns
7. review_analyzer.py
Purpose: Analyzes user reviews for actionable insights
Key Functions:
analyze_sentiment(): Calculate sentiment distributionextract_common_themes(): Identify frequent topicsidentify_issues(): Surface bugs and problemsfind_feature_requests(): Extract desired featurestrack_sentiment_trends(): Monitor changes over timegenerate_response_templates(): Create review responses
8. launch_checklist.py
Purpose: Generates comprehensive launch and update checklists
Key Functions:
generate_prelaunch_checklist(): Complete submission validationvalidate_app_store_compliance(): Check guidelines compliancecreate_update_plan(): Plan update cadenceoptimize_launch_timing(): Recommend launch datesplan_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)
- Use the
skill-creatorskill to import the skill - 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.pyto research keywords - Use
competitor_analyzer.pyto 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.pyto 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.pyto 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.pyto 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.pyto 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.pyto 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
- Start with 20-30 seed keywords
- Analyze top 5 competitors in your category
- Balance high-volume and long-tail keywords
- Prioritize relevance over search volume
- Update keyword research quarterly
Metadata Optimization
- Front-load keywords in title (first 15 characters most important)
- Use every available character (don't waste space)
- Write for humans first, search engines second
- A/B test major changes before committing
- Update descriptions with each major release
A/B Testing
- Test one element at a time (icon vs. screenshots vs. title)
- Run tests to statistical significance (90%+ confidence)
- Test high-impact elements first (icon has biggest impact)
- Allow sufficient duration (at least 1 week, preferably 2-3)
- Document learnings for future tests
Localization
- Start with top 5 revenue markets (US, China, Japan, Germany, UK)
- Use professional translators, not machine translation
- Test translations with native speakers
- Adapt keywords for cultural context
- Monitor ROI by market
Review Management
- Respond to reviews within 24-48 hours
- Always be professional, even with negative reviews
- Address specific issues raised
- Thank users for positive feedback
- 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:
- Provide detailed context about your app
- Include specific metrics when available
- Ask follow-up questions for clarification
- 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.
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.