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Agent Skill · 研究检索

app-store-optimization(应用商店优化)

app-store-optimization 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。

app-store-optimization 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为需要联网;许可证为 MIT,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 22,041。

总安装 4,672周安装 189GitHub Stars 13,236下载 1,467核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT

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-optimization(应用商店优化)
来源仓库:https://github.com/alirezarezvani/claude-skills
仓库路径:skills/app-store-optimization
安装命令:
npx skills add https://github.com/alirezarezvani/claude-skills --skill app-store-optimization
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。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/alirezarezvani/claude-skills --skill app-store-optimization

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

专注应用商店关键词研究与优化,提升应用在商店中的可见性和下载转化率。适用于移动应用推广、ASO 策略制定和竞品分析场景。提供关键词种子扩展、搜索量评估、竞争度分析和本地化适配建议。安装需确认目标市场和核心功能定义,避免使用开发者术语影响用户语言匹配。不涉及应用内购买或评分引导,仅聚焦元数据优化和流量获取策略。

来源仓库内路径:skills/app-store-optimization

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/alirezarezvani/claude-skills --skill app-store…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

app-store-optimization 怎么安装?

在终端执行 npx skills add https://github.com/alirezarezvani/claude-skills --skill app-store-optimizat…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

app-store-optimization 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

app-store-optimization 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

app-store-optimization 是免费的吗?

来源标注的许可证是 MIT。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

app-store-optimization 用的人多吗?

公开数据是累计安装 4,672 次,近一周安装 189 次,下载 1,467 次,GitHub 13,236 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

app-store-optimization 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 3 个目录同时收录,说明这个 Skill 流传比较广。
  • 许可证明确为 MIT,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
app-store-optimization

应用商店优化 · 本页

4,67213,236需要联网MIT通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

App Store Optimization (ASO)


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

  1. Generate seed keywords from:

- App features and benefits - User language (not developer terminology) - App store autocomplete suggestions

  1. Expand keyword list using:

- Modifiers (free, best, simple) - Actions (create, track, organize) - Audiences (for students, for teams, for business)

  1. Evaluate each keyword:

- Search volume (estimated monthly searches) - Competition (number and quality of ranking apps) - Relevance (alignment with app function)

  1. Score and prioritize keywords:

- Primary: Title and keyword field (iOS) - Secondary: Subtitle and short description - Tertiary: Full description only

  1. Map keywords to metadata locations
  2. Document keyword strategy for tracking
  3. Validation: Keywords scored; placement mapped; no competitor brand names included; no plurals in iOS keyword field

Keyword Evaluation Criteria

FactorWeightHigh Score Indicators
Relevance35%Describes core app function
Volume25%10,000+ monthly searches
Competition25%Top 10 apps have <4.5 avg rating
Conversion15%Transactional intent ("best X app")

Keyword Placement Priority

LocationSearch Weight
App TitleHighest
Subtitle (iOS)High
Keyword Field (iOS)High
Short Description (Android)High
Full DescriptionMedium

See: 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

  1. Optimize title following formula: [Brand Name] - [Primary Keyword] [Secondary Keyword]
  2. Write subtitle (iOS) or short description (Android):

- Focus on primary benefit - Include secondary keyword - Use action verbs

  1. Optimize keyword field (iOS only):

- Remove duplicates from title - Remove plurals (Apple indexes both forms) - No spaces after commas - Prioritize by score

  1. Rewrite full description:

- Hook paragraph with value proposition - Feature bullets with keywords - Social proof section - Call to action

  1. Validate character counts for each field
  2. Calculate keyword density (target 2-3% primary)
  3. Validation: All fields within character limits; primary keyword in title; no keyword stuffing (>5%); natural language preserved

Platform Character Limits

FieldApple App StoreGoogle Play Store
Title30 characters50 characters
Subtitle30 charactersN/A
Short DescriptionN/A80 characters
Keywords100 charactersN/A
Promotional Text170 charactersN/A
Full Description4,000 characters4,000 characters
What's New4,000 characters500 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


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)

  1. Extract competitor keywords from:

- App titles and subtitles - First 100 words of descriptions - Visible metadata patterns

  1. Build competitor keyword matrix:

- Map which keywords each competitor targets - Calculate coverage percentage per keyword

  1. Identify keyword gaps:

- Keywords with <40% competitor coverage - High volume terms competitors miss - Long-tail opportunities

  1. Analyze competitor visual assets:

- Icon design patterns - Screenshot messaging and style - Video presence and quality

  1. Compare ratings and review patterns:

- Average rating by competitor - Common praise themes - Common complaint themes

  1. Document positioning opportunities
  2. Validation: 10+ competitors analyzed; keyword matrix complete; gaps identified with volume estimates; visual audit documented

Competitor Analysis Matrix

Analysis AreaData Points
KeywordsTitle keywords, description frequency
MetadataCharacter utilization, keyword density
VisualsIcon style, screenshot count/style
RatingsAverage rating, total count, velocity
ReviewsTop praise, top complaints

Gap Analysis Template

Opportunity TypeExampleAction
Keyword gap"habit tracker" (40% coverage)Add to keyword field
Feature gapCompetitor lacks widgetHighlight in screenshots
Visual gapNo videos in top 5Create app preview
Messaging gapNone 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

  1. Submit for review (2 weeks before):

- Complete all store requirements - Verify compliance with guidelines - Prepare launch communications

  1. Configure post-launch systems:

- Set up review monitoring - Prepare response templates - Configure rating prompt timing

  1. Execute launch day:

- Verify app is live in both stores - Announce across all channels - Begin review response cycle

  1. Monitor initial performance (days 1-7):

- Track download velocity hourly - Monitor reviews and respond within 24 hours - Document any issues for quick fixes

  1. Conduct 7-day retrospective:

- Compare performance to projections - Identify quick optimization wins - Plan first metadata update

  1. Schedule first update (2 weeks post-launch)
  2. Validation: App live in stores; analytics tracking; review responses within 24h; download velocity documented; first update scheduled

Pre-Launch Checklist

CategoryItems
MetadataTitle, subtitle, description, keywords
Visual AssetsIcon, screenshots (all sizes), video
ComplianceAge rating, privacy policy, content rights
TechnicalApp binary, signing certificates
AnalyticsSDK integration, event tracking
MarketingPress kit, social content, email ready

Launch Timing Considerations

FactorRecommendation
Day of weekTuesday-Wednesday (avoid weekends)
Time of dayMorning in target market timezone
SeasonalAlign with relevant category seasons
CompetitionAvoid major competitor launch dates

See: 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)

  1. Form hypothesis: If we [change], then [metric] will [improve/increase] by [amount] because [rationale].
  2. Create variants:

- Control: Current version - Treatment: Single variable change

  1. Calculate required sample size:

- Baseline conversion rate - Minimum detectable effect (usually 5%) - Statistical significance (95%)

  1. Launch test:

- Apple: Use Product Page Optimization - Android: Use Store Listing Experiments

  1. Run test for minimum duration:

- At least 7 days - Until statistical significance reached

  1. Analyze results:

- Compare conversion rates - Check statistical significance - Document learnings

  1. Validation: Single variable tested; sample size sufficient; significance reached (95%); results documented; winner implemented

A/B Test Prioritization

ElementConversion ImpactTest Complexity
App Icon10-25% lift possibleMedium (design needed)
Screenshot 115-35% lift possibleMedium
Title5-15% lift possibleLow
Short Description5-10% lift possibleLow
Video10-20% lift possibleHigh

Sample Size Quick Reference

Baseline CVRImpressions 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:

VersionTitleAnalysis
Before"MyTasks"No keywords, brand only (8 chars)
After"MyTasks - Todo List & Planner"Primary + secondary keywords (29 chars)

Fitness App:

VersionTitleAnalysis
Before"FitTrack Pro"Generic modifier (12 chars)
After"FitTrack: Workout Log & Gym"Category keywords (27 chars)

Subtitle Optimization (iOS)

VersionSubtitleAnalysis
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

VersionCaptionIssue
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

ScriptPurposeUsage
keyword_analyzer.pyAnalyze keywords for volume and competitionpython keyword_analyzer.py --keywords "todo,task,planner"
metadata_optimizer.pyValidate metadata character limits and densitypython metadata_optimizer.py --platform ios --title "App Title"
competitor_analyzer.pyExtract and compare competitor keywordspython competitor_analyzer.py --competitors "App1,App2,App3"
aso_scorer.pyCalculate overall ASO health scorepython aso_scorer.py --app-id com.example.app
ab_test_planner.pyPlan tests and calculate sample sizespython ab_test_planner.py --cvr 0.05 --lift 0.10
review_analyzer.pyAnalyze review sentiment and themespython review_analyzer.py --app-id com.example.app
launch_checklist.pyGenerate platform-specific launch checklistspython launch_checklist.py --platform ios
localization_helper.pyManage multi-language metadatapython localization_helper.py --locales "en,es,de,ja"

References

DocumentContent
platform-requirements.mdiOS and Android metadata specs, visual asset requirements
aso-best-practices.mdOptimization strategies, rating management, launch tactics
keyword-research-guide.mdResearch methodology, evaluation framework, tracking

Assets

TemplatePurpose
aso-audit-template.mdStructured audit checklist for app store listings

Platform Notes

Platform / ConstraintBehavior / Impact
iOS keyword changesRequire app submission
iOS promotional textEditable without an app update
Android metadata changesIndex in 1-2 hours
Android keyword fieldNone — use description instead
Keyword volume dataEstimates only; no official source
Competitor dataPublic listings only

When not to use this skill: web apps (use web SEO), enterprise/internal apps, TestFlight-only betas, or paid advertising strategy.


Related Skills

SkillIntegration Point
content-creatorApp description copywriting
marketing-demand-acquisitionLaunch promotion campaigns
marketing-strategy-pmmGo-to-market planning

Proactive Triggers

  • No keyword optimization in title → App title is the #1 ranking factor. Include top keyword.
  • Screenshots don't show value → Screenshots should tell a story, not show UI.
  • No ratings strategy → Below 4.0 stars kills conversion. Implement in-app rating prompts.
  • Description keyword-stuffed → Natural language with keywords beats keyword stuffing.

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

Communication

All output passes quality verification:

  • Self-verify: source attribution, assumption audit, confidence scoring
  • Output format: Bottom Line → What (with confidence) → Why → How to Act
  • Results only. Every finding tagged: 🟢 verified, 🟡 medium, 🔴 assumed.

平台分布

Claude Code

28.94%
按下载量换算425

OpenCode

19%
按下载量换算279

Gemini CLI

17.98%
按下载量换算264

Antigravity

11.8%
按下载量换算173

Codex

6.93%
按下载量换算102

Cursor

3.57%
按下载量换算52

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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。

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