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app-store-aso应用商店阿索

Agent Skill

app-store-aso 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

6,789

周安装

272

GitHub Stars

34

下载量

2,198
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:app-store-aso(应用商店阿索)
来源仓库:https://github.com/timbroddin/app-store-aso-skill
仓库路径:skills/app-store-aso
安装命令:
npx skills add https://github.com/timbroddin/app-store-aso-skill --skill app-store-aso
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

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

简介

提供全面的 Apple App Store 优化分析与元数据生成能力。

  • 支持竞品分析、关键词研究和截图故事板推荐。
  • 需先了解应用定位、更新计划和目标受众再输出建议。
  • 建议参考 Apple 官方指南并遵守字符限制要求。
  • app-store-aso 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

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

  1. Load ASO Knowledge Base

- Reference references/aso_learnings.md for comprehensive ASO best practices - Apply competitive analysis strategies - Use proven optimization patterns

  1. 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

  1. 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

  1. 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.

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

34.41%
按下载量换算756

Claude

31.72%
按下载量换算697

Cursor

18.3%
按下载量换算402

Gemini CLI

10.84%
按下载量换算238

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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