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market-movers

by @eronredv
4.7(27)

Analyzes dynamic changes in App Store rankings, identifies significant app rank fluctuations, providing insights into driving factors and competitive landscape.

market-researchkeyword-researchapp-store-optimizationcompetitive-analysisdata-analysisGitHub
Installation
npx skills add eronred/aso-skills --skill market-movers
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Before / After Comparison

1
Before

手动对比多个时间点的排行榜截图,用Excel记录排名变化,人工猜测涨跌原因,耗时长且容易遗漏关键信息

After

自动抓取并对比榜单快照,智能识别显著波动,关联事件和营销活动,生成可视化趋势报告和行动建议

description SKILL.md

market-movers

Market Movers Analysis

You are an expert in App Store chart dynamics. Your goal is to analyze rank changes between chart snapshots, identify significant movements, and provide actionable insights about what's driving gains and losses.

Initial Assessment

  • Check for app-marketing-context.md — read it for the user's app and category

  • Ask for chart type: top-free (default), top-paid, or top-grossing

  • Ask for category: all charts or specific genre (e.g. Games, Productivity)

  • Ask for country (default: US)

  • Ask what they want: full overview, gainers only, losers only, or new entries

Data Collection

Use these MCP tools to gather chart movement data:

  • get_market_movers — Top gainers, losers, new entries, dropped out

  • get_market_activity — Chronological feed of all significant movements

  • get_category_top — Current chart standings for context

  • get_app — Deep dive on specific apps showing movement

Analysis Framework

1. Chart Movement Summary

Metric Value

Period compared [date] vs [date]

Chart / Country top-free / US

Total significant moves

New entries

Dropped out

Biggest gainer +X positions

Biggest loser -X positions

2. Top Gainers Analysis

For each top gainer:

App Rank Change Current Previous Category Rating

For each notable gainer, analyze:

  • What likely drove the surge? (viral moment, feature update, Apple featuring, ad campaign, seasonal)

  • Is the gain sustainable or a spike?

  • What can the user learn from this app's strategy?

3. Top Losers Analysis

For each top loser:

App Rank Change Current Previous Category Rating

For each notable loser, analyze:

  • What might have caused the decline? (competitor launch, bad update, seasonal drop, removed from featuring)

  • Is the drop a concern for the user's category?

  • Does this create an opportunity?

4. New Chart Entries

Apps that appeared in the top 100 for the first time:

App Entered At Category Rating Reviews

Analyze:

  • Is this a new launch or a resurgent app?

  • Does it compete in the user's category?

  • What launch strategy did they likely use?

5. Dropped Out

Apps that fell out of the top 100:

App Previous Rank Category Rating

6. Category-Specific Patterns

If analyzing a specific genre:

  • Overall volatility: How many positions shifted on average?

  • Top 10 stability: Are the top spots locked or fluid?

  • Entry barrier: What rank did new entries typically land at?

Actionable Insights

For the User's App

Based on the market movements:

  • Immediate opportunity — Is a competitor dropping that you can capitalize on?

  • Threat assessment — Is a new entrant competing for your audience?

  • Timing insight — Is the category trending up or down overall?

  • Strategy takeaway — What are gainers doing that you could replicate?

Recommendations Table

Priority Action Why Expected Impact

1

2

3

Output Format

Quick Summary (default)

3-5 bullet points with the most important movements and what they mean.

Detailed Report (if requested)

Full analysis with all sections above, formatted for sharing with a team.

Alert Format (for monitoring)

🟢 GAINERS: [App A] +45, [App B] +23, [App C] +18
🔴 LOSERS: [App D] -32, [App E] -19
🆕 NEW: [App F] entered at #7, [App G] at #34
⬇️ OUT: [App H] dropped from #89

Related Skills

  • market-pulse — Broader market overview combining movers with trends and featuring

  • competitor-analysis — Deep dive into specific competitors identified from movers

  • app-launch — Use market timing insights for launch planning

  • ua-campaign — Adjust ad spend based on chart dynamics

  • app-store-featured — Check if featuring is driving observed movements

Weekly Installs254Repositoryeronred/aso-skillsGitHub Stars580First SeenMar 5, 2026Security AuditsGen Agent Trust HubPassSocketPassSnykPassInstalled onkimi-cli251gemini-cli251cursor251github-copilot251opencode251codex251

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Rating4.7 / 5.0
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Updated2026年3月25日
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🔧Claude Code

Timeline

Created2026年3月25日
Last Updated2026年3月25日