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.

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

Keyword Research Workflow

Discover and evaluate keywords that drive app store visibility.

Workflow: Conduct Keyword Research

Define target audience and core app functions:
- Primary use case (what problem does the app solve)
- Target user demographics
- Competitive category
Generate seed keywords from:
- App features and benefits
- User language (not developer terminology)
- App store autocomplete suggestions
Expand keyword list using:
- Modifiers (free, best, simple)
- Actions (create, track, organize)
- Audiences (for students, for teams, for business)
Evaluate each keyword:
- Search volume (estimated monthly searches)
- Competition (number and quality of ranking apps)
- Relevance (alignment with app function)
Score and prioritize keywords:
- Primary: Title and keyword field (iOS)
- Secondary: Subtitle and short description
- Tertiary: Full description only
Map keywords to metadata locations
Document keyword strategy for tracking
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

Metadata Optimization Workflow

Optimize app store listing elements for search ranking and conversion.

Workflow: Optimize App Metadata

Audit current metadata against platform limits:
- Title character count and keyword presence
- Subtitle/short description usage
- Keyword field efficiency (iOS)
- Description keyword density

Optimize title following formula:

[Brand Name] - [Primary Keyword] [Secondary Keyword]

Write subtitle (iOS) or short description (Android):
- Focus on primary benefit
- Include secondary keyword
- Use action verbs
Optimize keyword field (iOS only):
- Remove duplicates from title
- Remove plurals (Apple indexes both forms)
- No spaces after commas
- Prioritize by score
Rewrite full description:
- Hook paragraph with value proposition
- Feature bullets with keywords
- Social proof section
- Call to action
Validate character counts for each field
Calculate keyword density (target 2-3% primary)
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

Competitor Analysis Workflow

Analyze top competitors to identify keyword gaps and positioning opportunities.

Workflow: Analyze Competitor ASO Strategy

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

App Launch Workflow

Execute a structured launch for maximum initial visibility.

Workflow: Launch App to Stores

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
Submit for review (2 weeks before):
- Complete all store requirements
- Verify compliance with guidelines
- Prepare launch communications
Configure post-launch systems:
- Set up review monitoring
- Prepare response templates
- Configure rating prompt timing
Execute launch day:
- Verify app is live in both stores
- Announce across all channels
- Begin review response cycle
Monitor initial performance (days 1-7):
- Track download velocity hourly
- Monitor reviews and respond within 24 hours
- Document any issues for quick fixes
Conduct 7-day retrospective:
- Compare performance to projections
- Identify quick optimization wins
- Plan first metadata update
Schedule first update (2 weeks post-launch)
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

A/B Testing Workflow

Test metadata and visual elements to improve conversion rates.

Workflow: Run A/B Test

Select test element (prioritize by impact):
- Icon (highest impact)
- Screenshot 1 (high impact)
- Title (high impact)
- Short description (medium impact)

Form hypothesis:

If we [change], then [metric] will [improve/increase] by [amount]
because [rationale].

Create variants:
- Control: Current version
- Treatment: Single variable change
Calculate required sample size:
- Baseline conversion rate
- Minimum detectable effect (usually 5%)
- Statistical significance (95%)
Launch test:
- Apple: Use Product Page Optimization
- Android: Use Store Listing Experiments
Run test for minimum duration:
- At least 7 days
- Until statistical significance reached
Analyze results:
- Compare conversion rates
- Check statistical significance
- Document learnings
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

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

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

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

Related Skills

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

Proactive Triggers

No keyword optimization in title -- App title is the #1 ranking factor. Include top keyword in title.
Screenshots don't show value -- Screenshots should tell a benefit story, not just show UI.
No ratings strategy -- Below 4.0 stars kills conversion. Implement in-app rating prompts at positive moments.
Description keyword-stuffed -- Natural language with keywords beats keyword stuffing. Target 2-3% density.

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
"Competitor analysis"	Competitive keyword matrix with gap opportunities

Communication

All output passes quality verification:

Self-verify: source attribution, assumption audit, confidence scoring
Output format: Bottom Line first, then What (with confidence), Why, How to Act
Every finding tagged with confidence level: verified, medium confidence, or assumed

Troubleshooting

Problem	Likely Cause	Solution
Keywords not indexing after metadata update	Apple requires app submission for keyword changes; Google indexes in 1-2 hours	For iOS, submit an app update or use promotional text (editable without submission). For Google Play, wait 24-48 hours and verify via Google Play Console search analytics
Conversion rate dropped after metadata change	Title or screenshot change reduced clarity or trust signals	Revert to previous version immediately, then A/B test the change using Apple Product Page Optimization or Google Store Listing Experiments before rolling out again
App not appearing in search results	Metadata lacks relevant keywords, or app has low engagement signals	Audit title and keyword field for target terms. Check that the app is not suppressed for guideline violations. Improve ratings above 4.0 to boost ranking signals
Ratings declining after update	New bugs introduced, or rating prompt timing is poor	Analyze recent negative reviews for patterns. Fix critical bugs in a hotfix release. Adjust in-app rating prompt to trigger after positive user actions (e.g., completing a task), not on app launch
Competitor outranking despite weaker metadata	Competitor has stronger engagement metrics (installs, retention, rating velocity)	Focus on improving post-install engagement and retention. Apple and Google now weight behavioral signals (session length, uninstall rate, rating velocity) alongside metadata relevance
Localized listing underperforming	Direct translation without cultural keyword adaptation	Use native speakers for keyword research in each market. Adapt keywords to local search behavior rather than translating English terms. German compound words and Japanese katakana/kanji mixing require specialized ASO
Apple Search Ads not delivering impressions	Bid too low, relevance score poor, or audience too narrow	Increase bid to match category benchmarks. Improve metadata relevance for target keywords (Apple will not show irrelevant ads regardless of bid). Broaden audience targeting or enable Search Match

Success Criteria

Conversion Rate (Impression-to-Install): Target 25-30% on iOS and 27-33% on Google Play (2026 cross-category median: 25% iOS, 27.3% Google Play). Productivity and utility apps should aim for 40%+ given category norms
Keyword Rankings: Maintain 10+ keywords in top-10 positions and 20+ keywords in top-50 positions within your primary market. Track ranking improvements week-over-week after each metadata update
Rating Quality: Sustain an average rating of 4.5+ stars with 100+ ratings per month. Apps below 4.0 stars experience measurable conversion drops. Aim for 99%+ crash-free rate to protect ratings
Metadata Utilization: Use 90%+ of available character space in title, subtitle (iOS), short description (Android), and keyword field (iOS). Under-utilized metadata is wasted ranking potential
A/B Test Velocity: Run at least one store listing experiment per month. Achieve statistical significance (95% confidence) before implementing winners. Target 5-15% conversion lift per successful test cycle
Localization Coverage: Localize metadata for at least 5 priority markets (US, China, Japan, Germany, UK) with native-speaker keyword research. Localized apps see 15-30% download increases in target markets
Apple Search Ads Efficiency: Maintain a tap-through rate (TTR) above 5% and cost-per-acquisition (CPA) below category median. In 2026, Apple is expanding search ad inventory with additional inline placements -- optimize for the new Maximize Conversions bidding option

Scope & Limitations

In Scope:

Keyword research, metadata optimization, and character limit validation for Apple App Store and Google Play Store
Competitor ASO analysis using publicly available metadata, ratings, and reviews
A/B test planning with sample size calculations and statistical significance testing
Launch checklist generation, seasonal campaign planning, and localization strategy
Review sentiment analysis and feature request extraction

Out of Scope:

Real-time app store data fetching (scripts analyze static data you provide)
Apple Search Ads or Google Ads campaign management (use platform dashboards)
Creative asset design (icon, screenshots, video production)
Cross-device attribution and install tracking (requires MMP integration such as AppsFlyer, Adjust, or Branch)
In-app analytics and retention optimization (use Firebase, Mixpanel, or Amplitude)
Revenue or subscription optimization (pricing strategy is a separate discipline)

Data Constraints:

No official search volume API exists for either app store; volume estimates rely on third-party tools or heuristic scoring
Competitor data is limited to publicly visible metadata, ratings, and reviews
Historical ranking data requires external ASO tools (AppTweak, Sensor Tower, data.ai)
Apple's June 2025 algorithm update now indexes screenshot text as a ranking factor -- this skill's scripts do not yet analyze visual text content

Integration Points

Integration	Purpose	How to Connect
Apple App Store Connect	Metadata submission, Product Page Optimization A/B tests, analytics	Upload optimized metadata from this skill's output directly into App Store Connect. Use Product Page Optimization for A/B tests planned by `ab_test_planner.py`
Google Play Console	Metadata submission, Store Listing Experiments, performance reports	Apply metadata recommendations in Play Console. Use Store Listing Experiments for A/B tests. Export conversion data for `aso_scorer.py` input
Apple Search Ads	Paid keyword discovery, Search Match insights	Use keyword data from `keyword_analyzer.py` to build Search Ads campaigns. Import Search Ads search term reports back into keyword research workflow. In 2026, leverage new inline ad placements and Maximize Conversions bidding
ASO Tools (AppTweak, Sensor Tower, data.ai)	Search volume data, ranking tracking, competitor intelligence	Export keyword volume and competitor data from ASO tools as input for `keyword_analyzer.py` and `competitor_analyzer.py`. Feed ranking history into `aso_scorer.py`
Firebase / Mixpanel / Amplitude	Post-install analytics, retention metrics	Use retention and engagement data to inform ASO scoring (engagement signals affect store rankings). Feed conversion funnel data into `aso_scorer.py` conversion metrics
campaign-analytics skill	Attribution modeling for app install campaigns	Combine ASO organic data with paid campaign attribution from `attribution_analyzer.py` to understand full acquisition picture
content-creator skill	App description copywriting and SEO optimization	Use `seo_optimizer.py` principles for app description writing. Apply brand voice consistency from `brand_voice_analyzer.py` across store listings

Tool Reference

keyword_analyzer.py

Type: Python library (imported, not CLI)

Classes:

KeywordAnalyzer -- Core analysis class

Key Methods:

Method	Parameters	Returns
`analyze_keyword()`	`keyword: str`, `search_volume: int = 0`, `competing_apps: int = 0`, `relevance_score: float = 0.0`	Dict with keyword analysis (potential score 0-100, difficulty score 0-100, recommendation)
`compare_keywords()`	`keywords_data: List[Dict]` (each dict: keyword, search_volume, competing_apps, relevance_score)	Ranked keywords with primary/secondary/long-tail categorization
`find_long_tail_opportunities()`	`base_keyword: str`, `modifiers: List[str]`	Long-tail keyword variations with competition estimates
`extract_keywords_from_text()`	`text: str`, `min_word_length: int = 3`	Top 50 keywords/phrases by frequency
`calculate_keyword_density()`	`text: str`, `target_keywords: List[str]`	Dict of keyword: density percentage

Convenience Function: analyze_keyword_set(keywords_data) -- Analyzes and ranks a full keyword set in one call.

metadata_optimizer.py

Type: Python library (imported, not CLI)

Classes:

MetadataOptimizer(platform: str = 'apple') -- Platform must be 'apple' or 'google'

Key Methods:

Method	Parameters	Returns
`optimize_title()`	`app_name: str`, `target_keywords: List[str]`, `include_brand: bool = True`	Title options with length, keywords included, pros/cons, recommendation
`optimize_description()`	`app_info: Dict` (name, key_features, unique_value, target_audience), `target_keywords: List[str]`, `description_type: str = 'full'`	Optimized description with keyword density analysis. Types: `'full'`, `'short'` (Google), `'subtitle'` (Apple)
`optimize_keyword_field()`	`target_keywords: List[str]`, `app_title: str = ""`, `app_description: str = ""`	Apple-only. Optimized 100-char keyword field (no spaces, no plurals, no title duplicates)
`validate_character_limits()`	`metadata: Dict[str, str]`	Validation report with errors, warnings, usage percentages
`calculate_keyword_density()`	`text: str`, `target_keywords: List[str]`	Per-keyword density with status (too_low / optimal / too_high)

Convenience Function: optimize_app_metadata(platform, app_info, target_keywords) -- Optimizes title, description, and keyword field in one call.

competitor_analyzer.py

Type: Python library (imported, not CLI)

Classes:

CompetitorAnalyzer(category: str, platform: str = 'apple')

Key Methods:

Method	Parameters	Returns
`analyze_competitor()`	`app_data: Dict` (app_name, title, description, rating, ratings_count, keywords)	Title analysis, description analysis, keyword strategy, competitive strength score (0-100)
`compare_competitors()`	`competitors_data: List[Dict]`	Ranked competitors, common keywords, keyword gaps, best practices, opportunities
`identify_gaps()`	`your_app_data: Dict`, `competitors_data: List[Dict]`	Keyword gaps, rating gaps, content gaps, competitive positioning assessment

Convenience Function: analyze_competitor_set(category, competitors_data, platform='apple') -- Full competitive analysis in one call.

aso_scorer.py

Type: Python library (imported, not CLI)

Classes:

ASOScorer -- Calculates weighted ASO health score (0-100)

Key Methods:

Method	Parameters	Returns
`calculate_overall_score()`	`metadata: Dict`, `ratings: Dict`, `keyword_performance: Dict`, `conversion: Dict`	Overall score, health status, breakdown by component, prioritized recommendations, strengths, weaknesses
`score_metadata_quality()`	`metadata: Dict` (title_keyword_count, title_length, description_length, description_quality, keyword_density)	Score 0-100
`score_ratings_reviews()`	`ratings: Dict` (average_rating, total_ratings, recent_ratings_30d)	Score 0-100
`score_keyword_performance()`	`keyword_performance: Dict` (top_10, top_50, top_100, improving_keywords)	Score 0-100
`score_conversion_metrics()`	`conversion: Dict` (impression_to_install, downloads_last_30_days, downloads_trend)	Score 0-100

Weights: metadata_quality 25%, ratings_reviews 25%, keyword_performance 25%, conversion_metrics 25%.

Convenience Function: calculate_aso_score(metadata, ratings, keyword_performance, conversion)

ab_test_planner.py

Type: Python library (imported, not CLI)

Classes:

ABTestPlanner

Key Methods:

Method	Parameters	Returns
`design_test()`	`test_type: str` (`'icon'`, `'screenshot'`, `'title'`, `'description'`), `variant_a: Dict`, `variant_b: Dict`, `hypothesis: str`, `success_metric: str = 'conversion_rate'`	Test design with ID, variants, secondary metrics, best practices
`calculate_sample_size()`	`baseline_conversion: float`, `minimum_detectable_effect: float`, `confidence_level: str = 'standard'` (`'high'`/`'standard'`/`'exploratory'`), `power: float = 0.80`	Sample size per variant, duration estimates for low/medium/high traffic
`calculate_significance()`	`variant_a_conversions: int`, `variant_a_visitors: int`, `variant_b_conversions: int`, `variant_b_visitors: int`	Z-score, p-value, significance at 90%/95%, decision recommendation
`track_test_results()`	`test_id: str`, `results_data: Dict`	Progress tracking, current significance, next steps
`generate_test_report()`	`test_id: str`, `final_results: Dict`	Complete report with insights, implementation plan, learnings

Convenience Function: plan_ab_test(test_type, variant_a, variant_b, hypothesis, baseline_conversion)

review_analyzer.py

Type: Python library (imported, not CLI)

Classes:

ReviewAnalyzer(app_name: str)

Key Methods:

Method	Parameters	Returns
`analyze_sentiment()`	`reviews: List[Dict]` (each: text, rating, date)	Sentiment distribution (positive/neutral/negative %), average rating, detailed sentiments
`extract_common_themes()`	`reviews: List[Dict]`, `min_mentions: int = 3`	Common words, phrases, categorized themes (features, performance, usability, support, pricing)
`identify_issues()`	`reviews: List[Dict]`, `rating_threshold: int = 3`	Categorized issues (crashes, bugs, performance, compatibility) with severity scores and priority
`find_feature_requests()`	`reviews: List[Dict]`	Clustered and prioritized feature requests
`track_sentiment_trends()`	`reviews_by_period: Dict[str, List[Dict]]`	Trend direction (improving/declining/stable), period-over-period comparison
`generate_response_templates()`	`issue_category: str` (`'crash'`, `'bug'`, `'feature_request'`, `'positive'`, `'negative_general'`)	Response templates for review management

Convenience Function: analyze_reviews(app_name, reviews) -- Runs sentiment, themes, issues, and feature requests in one call.

launch_checklist.py

Type: Python library (imported, not CLI)

Classes:

LaunchChecklistGenerator(platform: str = 'both') -- Platform: 'apple', 'google', or 'both'

Key Methods:

Method	Parameters	Returns
`generate_prelaunch_checklist()`	`app_info: Dict` (name, category, target_audience), `launch_date: Optional[str]` (YYYY-MM-DD)	Platform-specific checklists, universal checklist, timeline with milestones, completion summary
`validate_app_store_compliance()`	`app_data: Dict`, `platform: str = 'apple'`	Compliance validation with errors, warnings, recommendations
`create_update_plan()`	`current_version: str`, `planned_features: List[str]`, `update_frequency: str = 'monthly'`	Version schedule, feature distribution, What's New templates
`optimize_launch_timing()`	`app_category: str`, `target_audience: str`, `current_date: Optional[str]`	Optimal dates, day-of-week recommendation, seasonal considerations
`plan_seasonal_campaigns()`	`app_category: str`, `current_month: int = None`	Seasonal opportunities, campaign ideas, implementation timeline

Convenience Function: generate_launch_checklist(platform, app_info, launch_date)

localization_helper.py

Type: Python library (imported, not CLI)

Classes:

LocalizationHelper(app_category: str = 'general')

Key Methods:

Method	Parameters	Returns
`identify_target_markets()`	`current_market: str = 'en-US'`, `budget_level: str = 'medium'` (`'low'`/`'medium'`/`'high'`), `target_market_count: int = 5`	Prioritized markets (tier 1/2/3), estimated costs, phased implementation plan
`translate_metadata()`	`source_metadata: Dict[str, str]`, `source_language: str`, `target_language: str`, `platform: str = 'apple'`	Character limit validation per field with language-specific multipliers, translation notes
`adapt_keywords()`	`source_keywords: List[str]`, `source_language: str`, `target_language: str`, `target_market: str`	Adaptation strategy per keyword (full_localization / adapt_and_translate / direct_translation), cultural considerations
`validate_translations()`	`translated_metadata: Dict[str, str]`, `target_language: str`, `platform: str = 'apple'`	Character limit validation, quality checks (placeholders, excessive punctuation)
`calculate_localization_roi()`	`target_markets: List[str]`, `current_monthly_downloads: int`, `localization_cost: float`, `expected_lift_percentage: float = 0.15`	Market breakdown, expected monthly lift, payback period, annual ROI

Convenience Function: plan_localization_strategy(current_market, budget_level, monthly_downloads)

app-store-optimization

App Store Optimization (ASO)

Table of Contents

Keyword Research Workflow

Workflow: Conduct Keyword Research

Keyword Evaluation Criteria

Keyword Placement Priority

Metadata Optimization Workflow

Workflow: Optimize App Metadata

Platform Character Limits

Description Structure

Competitor Analysis Workflow

Workflow: Analyze Competitor ASO Strategy

Competitor Analysis Matrix

Gap Analysis Template

App Launch Workflow

Workflow: Launch App to Stores

Pre-Launch Checklist

Launch Timing Considerations

A/B Testing Workflow

Workflow: Run A/B Test

A/B Test Prioritization

Sample Size Quick Reference

Test Documentation Template

Before/After Examples

Title Optimization

Subtitle Optimization (iOS)

Keyword Field Optimization (iOS)

Description Opening

Screenshot Caption Evolution

Tools and References

Scripts

References

Assets

Platform Limitations

Data Constraints

Platform Behavior

When Not to Use This Skill

Related Skills

Proactive Triggers

Output Artifacts

Communication

Troubleshooting

Success Criteria

Scope & Limitations

Integration Points

Tool Reference

keyword_analyzer.py

metadata_optimizer.py

competitor_analyzer.py

aso_scorer.py

ab_test_planner.py

review_analyzer.py

launch_checklist.py

localization_helper.py