app-store-optimization

davila7/claude-code-templates · updated May 7, 2026

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$npx skills add https://github.com/davila7/claude-code-templates --skill app-store-optimization
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summary

This comprehensive skill provides complete ASO capabilities for successfully launching and optimizing mobile applications on the Apple App Store and Google Play Store.

skill.md

App Store Optimization (ASO) Skill

This comprehensive skill provides complete ASO capabilities for successfully launching and optimizing mobile applications on the Apple App Store and Google Play Store.

Capabilities

Research & Analysis

  • Keyword Research: Analyze keyword volume, competition, and relevance for app discovery
  • Competitor Analysis: Deep-dive into top-performing apps in your category
  • Market Trend Analysis: Identify emerging trends and opportunities in your app category
  • Review Sentiment Analysis: Extract insights from user reviews to identify strengths and issues
  • Category Analysis: Evaluate optimal category and subcategory placement strategies

Metadata Optimization

  • Title Optimization: Create compelling titles with optimal keyword placement (platform-specific character limits)
  • Description Optimization: Craft both short and full descriptions that convert and rank
  • Subtitle/Promotional Text: Optimize Apple-specific subtitle (30 chars) and promotional text (170 chars)
  • Keyword Field: Maximize Apple's 100-character keyword field with strategic selection
  • Category Selection: Data-driven recommendations for primary and secondary categories
  • Icon Best Practices: Guidelines for designing high-converting app icons
  • Screenshot Optimization: Strategies for creating screenshots that drive installs
  • Preview Video: Best practices for app preview videos
  • Localization: Multi-language optimization strategies for global reach

Conversion Optimization

  • A/B Testing Framework: Plan and track metadata experiments for continuous improvement
  • Visual Asset Testing: Test icons, screenshots, and videos for maximum conversion
  • Store Listing Optimization: Comprehensive page optimization for impression-to-install conversion
  • Call-to-Action: Optimize CTAs in descriptions and promotional materials

Rating & Review Management

  • Review Monitoring: Track and analyze user reviews for actionable insights
  • Response Strategies: Templates and best practices for responding to reviews
  • Rating Improvement: Tactical approaches to improve app ratings organically
  • Issue Identification: Surface common problems and feature requests from reviews

Launch & Update Strategies

  • Pre-Launch Checklist: Complete validation before submitting to stores
  • Launch Timing: Optimize release timing for maximum visibility and downloads
  • Update Cadence: Plan optimal update frequency and feature rollouts
  • Feature Announcements: Craft "What's New" sections that re-engage users
  • Seasonal Optimization: Leverage seasonal trends and events

Analytics & Tracking

  • ASO Score: Calculate overall ASO health score across multiple factors
  • Keyword Rankings: Track keyword position changes over time
  • Conversion Metrics: Monitor impression-to-install conversion rates
  • Download Velocity: Track download trends and momentum
  • Performance Benchmarking: Compare against category averages and competitors

Platform-Specific Requirements

  • Apple App Store:
    • Title: 30 characters
    • Subtitle: 30 characters
    • Promotional Text: 170 characters (editable without app update)
    • Description: 4,000 characters
    • Keywords: 100 characters (comma-separated, no spaces)
    • What's New: 4,000 characters
  • Google Play Store:
    • Title: 50 characters (formerly 30, increased in 2021)
    • Short Description: 80 characters
    • Full Description: 4,000 characters
    • No separate keyword field (keywords extracted from title and description)

Input Requirements

Keyword Research

{
  "app_name": "MyApp",
  "category": "Productivity",
  "target_keywords": ["task manager", "productivity", "todo list"],
  "competitors": ["Todoist", "Any.do", "Microsoft To Do"],
  "language": "en-US"
}

Metadata Optimization

{
  "platform": "apple" | "google",
  "app_info": {
    "name": "MyApp",
    "category": "Productivity",
    "target_audience": "Professionals aged 25-45",
    "key_features": ["Task management", "Team collaboration", "AI assistance"],
    "unique_value": "AI-powered task prioritization"
  },
  "current_metadata": {
    "title": "Current Title",
    "subtitle": "Current Subtitle",
    "description": "Current description..."
  },
  "target_keywords": ["productivity", "task manager", "todo"]
}

Review Analysis

{
  "app_id": "com.myapp.app",
  "platform": "apple" | "google",
  "date_range": "last_30_days" | "last_90_days" | "all_time",
  "rating_filter": [1, 2, 3, 4, 5],
  "language": "en"
}

ASO Score Calculation

{
  "metadata": {
    "title_quality": 0.8,
    "description_quality": 0.7,
    "keyword_density": 0.6
  },
  "ratings": {
    "average_rating": 4.5,
    "total_ratings": 15000
  },
  "conversion": {
    "impression_to_install": 0.05
  },
  "keyword_rankings": {
    "top_10": 5,
    "top_50": 12,
    "top_100": 18
  }
}

Output Formats

Keyword Research Report

  • List of recommended keywords with search volume estimates
  • Competition level analysis (low/medium/high)
  • Relevance scores for each keyword
  • Strategic recommendations for primary vs. secondary keywords
  • Long-tail keyword opportunities

Optimized Metadata Package

  • Platform-specific title (with character count validation)
  • Subtitle/promotional text (Apple)
  • Short description (Google)
  • Full description (both platforms)
  • Keyword field (Apple - 100 chars)
  • Character count validation for all fields
  • Keyword density analysis
  • Before/after comparison

Competitor Analysis Report

  • Top 10 competitors in category
  • Their metadata strategies
  • Keyword overlap analysis
  • Visual asset assessment
  • Rating and review volume comparison
  • Identified gaps and opportunities

ASO Health Score

  • Overall score (0-100)
  • Category breakdown:
    • Metadata Quality (0-25)
    • Ratings & Reviews (0-25)
    • Keyword Performance (0-25)
    • Conversion Metrics (0-25)
  • Specific improvement recommendations
  • Priority action items

A/B Test Plan

  • Hypothesis and test variables
  • Test duration recommendations
  • Success metrics definition
  • Sample size calculations
  • Statistical significance thresholds

Launch Checklist

  • Pre-submission validation (all required assets, metadata)
  • Store compliance verification
  • Testing checklist (devices, OS versions)
  • Marketing preparation items
  • Post-launch monitoring plan

How to Use

Keyword Research

Hey Claude—I just added the "app-store-optimization" skill. Can you research the best keywords for a productivity app targeting professionals? Focus on keywords with good search volume but lower competition.

Optimize App Store Listing

Hey Claude—I just added the "app-store-optimization" skill. Can you optimize my app's metadata for the Apple App Store? Here's my current listing: [provide current metadata]. I want to rank for "task management" and "productivity tools".

Analyze Competitor Strategy

Hey Claude—I just added the "app-store-optimization" skill. Can you analyze the ASO strategies of Todoist, Any.do, and Microsoft To Do? I want to understand what they're doing well and where there are opportunities.

Review Sentiment Analysis

Hey Claude—I just added the "app-store-optimization" skill. Can you analyze recent reviews for my app (com.myapp.ios) and identify the most common user complaints and feature requests?

Calculate ASO Score

Hey Claude—I just added the "app-store-optimization" skill. Can you calculate my app's overall ASO health score and provide specific recommendations for improvement?

Plan A/B Test

Hey Claude—I just added the "app-store-optimization" skill. I want to A/B test my app icon and first screenshot. Can you help me design the test and determine how long to run it?

Pre-Launch Checklist

Hey Claude—I just added the "app-store-optimization" skill. Can you generate a comprehensive pre-launch checklist for submitting my app to both Apple App Store and Google Play Store?

Scripts

keyword_analyzer.py

Analyzes keywords for search volume, competition, and relevance. Provides strategic recommendations for primary and secondary keywords.

Key Functions:

  • analyze_keyword(): Analyze single keyword metrics
  • compare_keywords(): Compare multiple keywords
  • find_long_tail(): Discover long-tail keyword opportunities
  • calculate_keyword_difficulty(): Assess competition level

metadata_optimizer.py

Optimizes titles, descriptions, and keyword fields with platform-specific character limit validation.

Key Functions:

  • optimize_title(): Create compelling, keyword-rich titles
  • optimize_description(): Generate conversion-focused descriptions
  • optimize_keyword_field(): Maximize Apple's 100-char keyword field
  • validate_character_limits(): Ensure compliance with platform limits
  • calculate_keyword_density(): Analyze keyword usage in metadata

competitor_analyzer.py

Analyzes top competitors' ASO strategies and identifies opportunities.

Key Functions:

  • get_top_competitors(): Identify category leaders
  • analyze_competitor_metadata(): Extract and analyze competitor keywords
  • compare_visual_assets(): Evaluate icons and screenshots
  • identify_gaps(): Find competitive opportunities

aso_scorer.py

Calculates comprehensive ASO health score across multiple dimensions.

Key Functions:

  • calculate_overall_score(): Compute 0-100 ASO score
  • score_metadata_quality(): Evaluate title, description, keywords
  • score_ratings_reviews(): Assess rating quality and volume
  • score_keyword_performance(): Analyze ranking positions
  • score_conversion_metrics(): Evaluate impression-to-install rates
  • generate_recommendations(): Provide prioritized action items

ab_test_planner.py

Plans and tracks A/B tests for metadata and visual assets.

Key Functions:

  • design_test(): Create test hypothesis and variables
  • calculate_sample_size(): Determine required test duration
  • calculate_significance(): Assess statistical significance
  • track_results(): Monitor test performance
  • generate_report(): Summarize test outcomes

localization_helper.py

Manages multi-language ASO optimization strategies.

Key Functions:

  • identify_target_markets(): Recommend localization priorities
  • translate_metadata(): Generate localized metadata
  • adapt_keywords(): Research locale-specific keywords
  • validate_translations(): Check character limits per language
  • calculate_localization_roi(): Estimate impact of localization

review_analyzer.py

Analyzes user reviews for sentiment, issues, and feature requests.

Key Functions:

  • analyze_sentiment(): Calculate positive/negative/neutral ratios
  • extract_common_themes(): Identify frequently mentioned topics
  • identify_issues(): Surface bugs and user complaints
  • find_feature_requests(): Extract desired features
  • track_sentiment_trends(): Monitor sentiment over time
  • generate_response_templates(): Create review response drafts

launch_checklist.py

Generates comprehensive pre-launch and update checklists.

Key Functions:

  • generate_prelaunch_checklist(): Complete submission validation
  • validate_app_store_compliance(): Check Apple guidelines
  • validate_play_store_compliance(): Check Google policies
  • create_update_plan(): Plan update cadence and features
  • optimize_launch_timing(): Recommend release dates
  • plan_seasonal_campaigns(): Identify seasonal opportunities

Best Practices

Keyword Research

  1. Volume vs. Competition: Balance high-volume keywords with achievable rankings
  2. Relevance First: Only target keywords genuinely relevant to your app
  3. Long-Tail Strategy: Include 3-4 word phrases with lower competition
  4. Continuous Research: Keyword trends change—research quarterly
  5. Competitor Keywords: Don't copy blindly; ensure relevance to your features

Metadata Optimization

  1. Front-Load Keywords: Place most important keywords early in title/description
  2. Natural Language: Write for humans first, SEO second
  3. Feature Benefits: Focus on user
how to use app-store-optimization

How to use app-store-optimization on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add app-store-optimization
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/davila7/claude-code-templates --skill app-store-optimization

The skills CLI fetches app-store-optimization from GitHub repository davila7/claude-code-templates and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/app-store-optimization

Reload or restart Cursor to activate app-store-optimization. Access the skill through slash commands (e.g., /app-store-optimization) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

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Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.735 reviews
  • William Kapoor· Dec 12, 2024

    app-store-optimization fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Aditi Reddy· Dec 12, 2024

    I recommend app-store-optimization for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Yash Thakker· Nov 15, 2024

    app-store-optimization reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Henry Bansal· Nov 3, 2024

    We added app-store-optimization from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Aarav Chawla· Nov 3, 2024

    Keeps context tight: app-store-optimization is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Hana White· Oct 22, 2024

    Solid pick for teams standardizing on skills: app-store-optimization is focused, and the summary matches what you get after install.

  • Charlotte Iyer· Oct 22, 2024

    Registry listing for app-store-optimization matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Dhruvi Jain· Oct 6, 2024

    I recommend app-store-optimization for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Rahul Santra· Sep 25, 2024

    Solid pick for teams standardizing on skills: app-store-optimization is focused, and the summary matches what you get after install.

  • Mia Iyer· Sep 5, 2024

    I recommend app-store-optimization for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

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