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.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionrating-prompt-strategyExecute the skills CLI command in your project's root directory to begin installation:
Fetches rating-prompt-strategy from eronred/aso-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate rating-prompt-strategy. Access via /rating-prompt-strategy in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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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.
Only prompt users who have experienced value. Prompting too early produces low ratings. Prompting at a success moment produces 4–5 star ratings.
Apple's native prompt. Rules:
import StoreKit
// Call at the right moment
if let scene = UIApplication.shared.connectedScenes.first as? UIWindowScene {
SKStoreReviewController.requestReview(in: scene)
}
Google's native prompt. Rules:
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 */ }
}
}
Define 1–3 "success moments" in your app where users are most satisfied:
| App Type | Good Prompt Moments | Bad Prompt Moments |
|---|---|---|
| Fitness | After completing a workout | After skipping a session |
| Productivity | After completing a project/task | After a failed save or sync error |
| Games | After winning a level or beating a boss | After losing or failing |
| Finance | After first successful transaction | After a confusing error |
| Meditation | After completing a session | On cold open |
| Shopping | After a successful purchase/delivery | After a failed checkout |
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
Before triggering the native prompt, show a single in-app question:
"Are you enjoying [App Name]?"
[Yes, love it!] [Not really]
SKStoreReviewRequest / Play In-App ReviewThis 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.
iOS allows you to reset ratings per version in App Store Connect. Use this strategically:
review-management skill)After the fix is shipped:
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
| Platform | Maximum | Recommended |
|---|---|---|
| iOS | 3× per 365 days (Apple-enforced) | 1–2× per version |
| Android | No hard limit (Google throttles) | 1× per 30 days per user |
Never show the prompt twice in the same session.
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]
review-management — Respond to reviews to recover ratingonboarding-optimization — Fix activation issues that drive 1-star reviewsandroid-aso — Play In-App Review API contextretention-optimization — Engaged users give better ratingsMake data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
Solid pick for teams standardizing on skills: rating-prompt-strategy is focused, and the summary matches what you get after install.
We added rating-prompt-strategy from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Useful defaults in rating-prompt-strategy — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
I recommend rating-prompt-strategy for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Keeps context tight: rating-prompt-strategy is the kind of skill you can hand to a new teammate without a long onboarding doc.
rating-prompt-strategy is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
rating-prompt-strategy fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
rating-prompt-strategy reduced setup friction for our internal harness; good balance of opinion and flexibility.
Registry listing for rating-prompt-strategy matched our evaluation — installs cleanly and behaves as described in the markdown.
rating-prompt-strategy fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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