app-store-aso

Generate optimized Apple App Store metadata recommendations with ASO best practices. Use this skill when analyzing app listings, optimizing metadata (title, subtitle, description, keywords), performing competitive analysis, or validating App Store listing requirements. Triggers on queries about App Store optimization, metadata review, or screenshot strategy.

Safety Notice

This listing is imported from skills.sh public index metadata. Review upstream SKILL.md and repository scripts before running.

Copy this and send it to your AI assistant to learn

Install skill "app-store-aso" with this command: npx skills add timbroddin/app-store-aso-skill/timbroddin-app-store-aso-skill-app-store-aso

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.

Source Transparency

This detail page is rendered from real SKILL.md content. Trust labels are metadata-based hints, not a safety guarantee.

Related Skills

Related by shared tags or category signals.

Research

fall-detection-video-analysis

Detects whether anyone has fallen within a target area. Supports video stream analysis and is suitable for real-time safety monitoring of elderly people living alone. | 跌倒检测视频版技能,检测目标区域内是否有人跌倒,支持视频流检测,适用于独居老人居家安全监测

Archived SourceRecently Updated
Research

fall-detection-image-analysis

Detects whether anyone has fallen within a specified target area. Supports both image and short video analysis. Suitable for scenarios such as home care for elderly people living alone and safety monitoring in nursing homes. | 检测目标区域内是否有人跌倒,支持图片和短视频检测,适用于独居老人居家看护、养老院安全监测等场景

Archived SourceRecently Updated
Research

contactless-health-risk-detection-analysis

Combines frontal facial image capture with multimodal physiological feature analysis to provide early risk screening and alerts for chronic and acute conditions such as heart attack, stroke, hypertension, and hyperlipidemia. | 非接触式健康风险识别技能,通过正面人像采集结合多模态生理特征分析,提供心梗、脑梗、高血压、高血脂等慢病急症早期风险筛查预警

Archived SourceRecently Updated
Research

Cjl Autoresearch Cc

# cjl-autoresearch-cc

Archived SourceRecently Updated