app-store-aso

timbroddin/app-store-aso-skill · updated May 18, 2026

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$npx skills add https://github.com/timbroddin/app-store-aso-skill --skill app-store-aso
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summary

This skill enables comprehensive Apple App Store Optimization (ASO) analysis and metadata generation. Analyze existing app listings, generate optimized metadata following Apple's guidelines and character limits, provide competitive insights, and recommend screenshot storyboard strategies.

skill.md

Apple App Store ASO Optimization

Overview

This skill enables comprehensive Apple App Store Optimization (ASO) analysis and metadata generation. Analyze existing app listings, generate optimized metadata following Apple's guidelines and character limits, provide competitive insights, and recommend screenshot storyboard strategies.

Core Workflow

When a user requests ASO optimization or metadata review:

  1. Analyze the App Context

    • Understand the app's purpose, features, and target audience
    • Identify unique value propositions and competitive differentiators
    • Note any changes or updates the user mentions
  2. Load ASO Knowledge Base

    • Reference references/aso_learnings.md for comprehensive ASO best practices
    • Apply competitive analysis strategies
    • Use proven optimization patterns
  3. Generate Optimized Metadata

    • Create optimized app name, subtitle, and promotional text
    • Write compelling description with keyword optimization
    • Generate keyword list with strategic placement
    • Ensure all metadata follows Apple's character limits
  4. Validate Character Counts

    • Use scripts/validate_metadata.py to verify all metadata meets Apple's requirements
    • Display validation results with character counts and limit compliance
    • Flag any violations with specific corrections needed
  5. Provide Screenshot Strategy

    • Recommend screenshot storyboard sequence
    • Suggest messaging hierarchy and visual focus areas
    • Align screenshot strategy with metadata messaging

Apple App Store Character Limits

Critical Limits to Validate:

  • App Name: 30 characters maximum
  • Subtitle: 30 characters maximum
  • Promotional Text: 170 characters maximum
  • Description: 4,000 characters maximum
  • Keywords: 100 characters maximum (comma-separated, no spaces)
  • What's New: 4,000 characters maximum

Metadata Validation Process

After generating recommendations, always validate using the validation script:

python scripts/validate_metadata.py

The script will:

  1. Prompt for each metadata field
  2. Calculate character counts
  3. Check against Apple's limits
  4. Display results with ✅ (pass) or ❌ (fail) indicators
  5. Show exact character counts and remaining characters

Integration Pattern:

  • Generate metadata recommendations
  • Run validation script with recommended content
  • Display validation results to user
  • Adjust any failing fields and re-validate

Output Format

Structure recommendations as:

📱 App Metadata Recommendations

App Name (X/30 characters) [optimized name]

Subtitle (X/30 characters) [optimized subtitle]

Promotional Text (X/170 characters) [promotional text]

Keywords (X/100 characters) [keyword,list,no,spaces]

Description (X/4000 characters) [full description]

🎯 Competitive Analysis

[Key insights and positioning recommendations]

📸 Screenshot Storyboard Strategy

[Ordered list of screenshot recommendations with messaging]

✅ Validation Results

[Output from validation script showing compliance]

Krankie: App Store Ranking Tracker

Krankie is an agent-first CLI tool for tracking App Store keyword rankings. Use it to monitor keyword performance, track ranking changes over time, and inform ASO optimization decisions with real data.

Installation

bun install -g krankie
# or run directly
bunx krankie

Key Commands

App Management:

# Search for apps
krankie app search "<query>" --platform ios

# Add an app to track
krankie app create <app_id> --platform ios

# List tracked apps
krankie app list

Keyword Tracking:

# Add keywords to track for an app
krankie keyword add <app_id> "<keyword>" --store us

# List tracked keywords
krankie keyword list

Ranking Checks:

# Run ranking checks for all tracked keywords
krankie check run

# View current rankings
krankie rankings

# See biggest movers (gains/losses)
krankie rankings movers

# View ranking history for a keyword
krankie rankings history <keyword_id>

# Check status of last run
krankie check status

Automation:

# Install daily cron job (default: 6 AM)
krankie cron install --hour 6

# Check cron status
krankie cron status

Agent Integration

All commands support --json flag for structured output:

krankie rankings --json
krankie app list --json

Get agent-friendly instructions:

krankie instructions --format json

Data Notes

  • Rankings track positions 1-200; null indicates outside this range
  • Data stored locally in ~/.krankie/krankie.db (SQLite)
  • Daily re-checks are rate-limited; use --force to override
  • Logs available at ~/.krankie/check.log

ASO Workflow Integration

  1. Before optimization: Use krankie rankings to establish baseline keyword positions
  2. Competitive analysis: Track competitor apps and their keyword rankings
  3. After metadata changes: Monitor krankie rankings movers to measure impact
  4. Trend analysis: Use krankie rankings history to identify patterns

Resources

scripts/validate_metadata.py

Python script that validates App Store metadata against Apple's character limits. Provides interactive validation with clear pass/fail indicators.

references/aso_learnings.md

Comprehensive ASO knowledge base containing optimization strategies, competitive analysis frameworks, keyword research techniques, and proven best practices. Load this file to inform all ASO recommendations.

how to use app-store-aso

How to use app-store-aso 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-aso
2

Execute installation command

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

$npx skills add https://github.com/timbroddin/app-store-aso-skill --skill app-store-aso

The skills CLI fetches app-store-aso from GitHub repository timbroddin/app-store-aso-skill 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-aso

Reload or restart Cursor to activate app-store-aso. Access the skill through slash commands (e.g., /app-store-aso) 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.

List & Monetize Your Skill

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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

4.556 reviews
  • Chaitanya Patil· Dec 28, 2024

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

  • Anaya Thompson· Dec 28, 2024

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

  • Layla Malhotra· Dec 20, 2024

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

  • Daniel Iyer· Dec 16, 2024

    Useful defaults in app-store-aso — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Omar Sanchez· Dec 4, 2024

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

  • Anaya Sanchez· Nov 27, 2024

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

  • Piyush G· Nov 19, 2024

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

  • Layla Perez· Nov 19, 2024

    Useful defaults in app-store-aso — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Layla Chawla· Nov 15, 2024

    app-store-aso has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Aanya Johnson· Nov 11, 2024

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

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