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Rating Prompt Strategy

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Eronred
rating-prompt-strategy

When the user wants to improve their app's star rating, increase ratings volume, optimize when and how they prompt users for a review, or recover from a bad rating period. Use when the user mentions "app rating", "star rating", "review prompt", "SKStoreReviewRequest", "In-App Review API", "ask for review", "low rating", "rating drop", "get more reviews", or "recover from 1-star". For responding to reviews, see review-management. For overall ASO health, see aso-audit.

Overview

PublisherEronred
Repositoryaso-skills
Skill namerating-prompt-strategy
Stars
1.9K
Forks
116
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by Eronred on GitHub. Read the source before you install it.

Installation

Install the Rating Prompt Strategy AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Eronred/aso-skills.git /tmp/aso-skills
mkdir -p .claude/skills
cp -r /tmp/aso-skills/skills/rating-prompt-strategy .claude/skills/rating-prompt-strategy
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Rating Prompt Strategy in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Rating Prompt Strategy on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Rating Prompt Strategy is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Rating Prompt Strategy

You optimize when, how, and to whom an app shows review prompts — maximizing high ratings while minimizing negative ones. Ratings are an App Store ranking signal and a conversion factor on the product page.

Why Ratings Matter for ASO

  • Search ranking — Apps with higher ratings rank better for competitive keywords
  • Conversion — Rating stars are visible in search results; a 4.8 beats 4.2 at a glance
  • iOS: Rating resets per version (you can request a reset in App Store Connect)
  • Android: Ratings are permanent and cumulative — one bad period is hard to recover

The Core Rule

Only prompt users who have experienced value. Prompting too early produces low ratings. Prompting at a success moment produces 4–5 star ratings.

iOS — SKStoreReviewRequest

Apple's native prompt. Rules:

  • Shows at most 3 times per year regardless of how many times you call it
  • Apple controls the display logic — calling it doesn't guarantee it shows
  • Never prompt after an error, crash, or frustrating moment
  • Cannot customize the prompt UI
swift
import StoreKit

// Call at the right moment
if let scene = UIApplication.shared.connectedScenes.first as? UIWindowScene {
    SKStoreReviewController.requestReview(in: scene)
}

Android — Play In-App Review API

Google's native prompt. Rules:

  • No hard limits, but Google throttles it if called too often
  • Show after a clear positive moment
  • Cannot determine if the user actually rated (privacy)
kotlin
val manager = ReviewManagerFactory.create(context)
val request = manager.requestReviewFlow()
request.addOnCompleteListener { task ->
    if (task.isSuccessful) {
        val reviewInfo = task.result
        val flow = manager.launchReviewFlow(activity, reviewInfo)
        flow.addOnCompleteListener { /* proceed */ }
    }
}

Timing Framework

The Success Moment Trigger

Define 1–3 "success moments" in your app where users are most satisfied:

App TypeGood Prompt MomentsBad Prompt Moments
FitnessAfter completing a workoutAfter skipping a session
ProductivityAfter completing a project/taskAfter a failed save or sync error
GamesAfter winning a level or beating a bossAfter losing or failing
FinanceAfter first successful transactionAfter a confusing error
MeditationAfter completing a sessionOn cold open
ShoppingAfter a successful purchase/deliveryAfter a failed checkout

Session-Based Rules

Only prompt users who meet all criteria:

Criteria to prompt:
✓ Sessions >= 3 (not a first-time user)
✓ Time since install >= 3 days
✓ Has completed [activation event] at least once
✓ No crash in last session
✓ No negative signal (error, cancellation) in current session
✓ Not already rated this version

Pre-Prompt Survey (Recommended)

Before triggering the native prompt, show a single in-app question:

"Are you enjoying [App Name]?"
  [Yes, love it!]   [Not really]
  • "Yes" → trigger SKStoreReviewRequest / Play In-App Review
  • "Not really" → show a feedback form (email or in-app), do not trigger the native prompt

This filters out dissatisfied users before they can rate you 1–2 stars.

Expected improvement: 0.3–0.8 stars on average with a pre-prompt filter.

Version-Gating (iOS)

iOS allows you to reset ratings per version in App Store Connect. Use this strategically:

  • Reset after a major improvement — If you fixed the top-complained issues
  • Do not reset after a controversial change that users disliked
  • After a reset, run an aggressive (but filtered) prompt campaign in the first 7 days
  • Target your most engaged users first (longest session history)

Recovering from a Rating Drop

Diagnosis

  1. Check which version caused the drop — correlate with release dates
  2. Read the 1-star reviews for that period — find the common complaint
  3. Fix the issue in the next release
  4. Reply to every 1–3 star review (see review-management skill)

Recovery Campaign

After the fix is shipped:

  1. Reply to negative reviews: "Fixed in version X.X — please update and let us know"
  2. Some users will update their rating after a reply
  3. Run a prompt campaign targeted at your most loyal users (highest session count)
  4. Do not prompt users who left a negative review

Timeline

Day 0:   Issue identified — hotfix or patch in progress
Day 1–3: Reply to every negative review acknowledging the issue
Day 7:   Fix shipped — reply to previous negative reviews "Fixed in X.X"
Day 8+:  Enable prompt for sessions >= 5, no crash last 7 days
Week 3:  Monitor rating trend — should recover 0.2–0.5 stars in 2–4 weeks

Prompt Frequency

PlatformMaximumRecommended
iOS3× per 365 days (Apple-enforced)1–2× per version
AndroidNo hard limit (Google throttles)1× per 30 days per user

Never show the prompt twice in the same session.

Output Format

Rating Strategy Plan

Current rating: [X.X] ★  ([N] ratings)
Platform: iOS / Android / Both

Success moments identified:
1. [Event name] — fires when [condition]
2. [Event name] — fires when [condition]

Pre-prompt survey: Yes / No
  If yes: "Are you enjoying [App Name]?" → Yes / Not really

Prompt trigger logic:
  Sessions >= [N]
  Days since install >= [N]
  No crash in last [N] sessions
  [Activation event] completed: yes
  Already rated this version: no

Expected outcome: +[X] stars over [N] weeks

Recovery plan (if rating < 4.0):
  1. [Fix] — ship by [date]
  2. [Reply strategy] — [N] reviews to address
  3. [Prompt campaign] — start [date], target [segment]

Related Skills

  • review-management — Respond to reviews to recover rating
  • onboarding-optimization — Fix activation issues that drive 1-star reviews
  • android-aso — Play In-App Review API context
  • retention-optimization — Engaged users give better ratings

Frequently asked questions

What does the Rating Prompt Strategy AI skill do?

When the user wants to improve their app's star rating, increase ratings volume, optimize when and how they prompt users for a review, or recover from a bad rating period. Use when the user mentions "app rating", "star rating", "review prompt", "SKStoreReviewRequest", "In-App Review API", "ask for review", "low rating", "rating drop", "get more reviews", or "recover from 1-star". For responding to reviews, see review-management. For overall ASO health, see aso-audit.

Why use Rating Prompt Strategy on TypingMind?

Because you install it once and use it with any model. Rating Prompt Strategy is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Rating Prompt Strategy in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Eronred/aso-skills/tree/main/skills/rating-prompt-strategy. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Rating Prompt Strategy?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Rating Prompt Strategy?

As many as you like. As long as a model supports skills, you can use Rating Prompt Strategy with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Rating Prompt Strategy AI skill free?

Yes. It is published on GitHub by Eronred under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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