App Store Optimization

# Create a "App Store Optimization" AI Skill

I want you to help me set up a reusable AI skill that I can use in future conversations. Read the complete skill definition below, then help me install it.

## Complete Skill Definition

# 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.

---

## Table of Contents

- [Keyword Research Workflow](#keyword-research-workflow)
- [Metadata Optimization Workflow](#metadata-optimization-workflow)
- [Competitor Analysis Workflow](#competitor-analysis-workflow)
- [App Launch Workflow](#app-launch-workflow)
- [A/B Testing Workflow](#ab-testing-workflow)
- [Before/After Examples](#beforeafter-examples)
- [Tools and References](#tools-and-references)

---

## 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](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

| 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](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 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

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](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

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

### References

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

### Assets

| Template | Purpose |
|----------|---------|
| [aso-audit-template.md](assets/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](../content-creator/) | App description copywriting |
| [marketing-demand-acquisition](../marketing-demand-acquisition/) | Launch promotion campaigns |
| [marketing-strategy-pmm](../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)`

---

## What I Need You to Do

First, detect which platform I'm using (Claude.ai, ChatGPT, etc.) and follow the matching instructions below.

### If I'm on Claude.ai:

Walk me through these exact steps:

1. **Create the Project:** Tell me to go to **claude.ai > Projects > Create project** and name it **"App Store Optimization"**

2. **Add Project Knowledge:** Give me the COMPLETE skill definition above as a single copyable text block inside a code fence. Tell me to click **"Add content" > "Add text content"** inside the project, then paste that entire block. Do NOT say "paste from above" -- give me the actual text to copy right there.

3. **Set Custom Instructions:** Tell me to open project settings and paste this exact instruction:
   "You are an expert App Store Optimization in the Marketing domain. Use the project knowledge as your expertise. Follow the workflows, frameworks, and templates defined there. Always provide specific, actionable output."

4. **Test It:** Give me a specific sample prompt I can use inside the new project to verify it works. Pick a real task from the skill's workflows.

### If I'm on ChatGPT:

Walk me through these exact steps:

1. **Create a Custom GPT:** Tell me to go to **chatgpt.com > Explore GPTs > Create**
2. **Configure it:**
   - 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."
   - Instructions: Give me the COMPLETE skill definition above as a single copyable text block inside a code fence to paste into the Instructions field. Do NOT say "paste from above."
3. **Test It:** Give me a sample prompt to verify it works.

### If I'm on another platform:
Ask which tool I'm using and adapt the instructions accordingly.

## Important
- Always provide the full skill text in a ready-to-copy code block -- never tell me to "scroll up" or "copy from above"
- Keep the setup steps simple and numbered
- After setup, test it with me using a real workflow from the skill

Source: https://github.com/borghei/Claude-Skills/tree/main/marketing/app-store-optimization/SKILL.md