Ratings Mechanics logo

Ratings Mechanics

Community
rshankras
ratings-mechanics

How App Store ratings actually behave — per-storefront isolation (your US stars show nowhere else), the never-reset rule, phased release + manual release as rating protection, and where prompting/replying fit. Use when planning ratings strategy for new markets, considering a ratings reset, setting release options, or diagnosing "why is my rating missing in country X."

Overview

Publisherrshankras
Repositoryclaude-code-apple-skills
Skill nameratings-mechanics
Stars
744
Forks
70
Bundled files
Instructions only
LicenseMIT
Links
Skill sourceRepository
  • 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 rshankras on GitHub. Read the source before you install it.

Installation

Install the Ratings Mechanics 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/rshankras/claude-code-apple-skills.git /tmp/claude-code-apple-skills
mkdir -p .claude/skills
cp -r /tmp/claude-code-apple-skills/skills/app-store/ratings-mechanics .claude/skills/ratings-mechanics
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ratings Mechanics 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 Ratings Mechanics 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 Ratings Mechanics 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.

Ratings Mechanics

The rating is an asset with mechanics most developers learn the hard way. This skill covers the four rules that aren't obvious from the ASC UI. Prompting code lives in generators/review-prompt; reply writing lives in app-store/review-response-writer — this skill is the strategy layer that tells you when each matters.

When This Skill Activates

  • Localizing or expanding into new storefronts ("why does my app show no rating in Japan?")
  • Considering the "reset ratings summary" option on a version release
  • Choosing release options before submitting (phased vs immediate, auto vs manual)
  • Planning a per-market ratings strategy alongside product/localization-strategy
  • A bad build or review-bomb is threatening the rating

Rule 1: Ratings are per-storefront — they do not travel

Your 4.8★ from 2,000 US ratings renders as no rating at all on the Japanese storefront until Japanese users rate the app there. Every storefront starts from zero.

Consequences:

  • ✅ Entering a new market = re-running the early-days ratings playbook in that market: prompt eagerly (within guidelines), localize the prompt moment, reply to every early review.
  • ✅ Weight requestReview triggers by storefront maturity — a market with 12 ratings needs the prompt more than the home market with 5,000.
  • ❌ Assuming social proof transfers with the binary. A localized listing with zero local ratings converts like an unknown app, because there it is one.
  • The written-review pool is also per-storefront: expect empty review sections in fresh markets and seed them via TestFlight communities or launch outreach in that region.

Rule 2: Never reset the ratings summary

ASC offers a reset when you release a new version. It is almost always a mistake:

  • Reset discards the count as well as the average — 4.2★ from 3,000 ratings converts better than a naked 5.0★ from 6, and the count never comes back except one rating at a time.
  • The instinct to reset ("v2 is a big rewrite, old reviews don't apply") is better served by replying to outdated negative reviews (updated ratings replace the old score — see review-response-writer) and by the What's New copy.
  • ✅ Legitimate near-exception: a catastrophic launch (sub-3★, low count, fixed root cause) on an app with almost no ratings mass. Even then, run the math on count loss first.
  • ❌ Resetting an established app to chase a higher average. You'll rank and convert worse for months.

Rule 3: Phased release + manual release are rating armor

Two ASC toggles turn a bad build from a rating catastrophe into a contained incident:

  • Manual version release — approval ≠ release. Release when you're awake and watching crash dashboards, not whenever review finishes.
  • Phased release — 7-day staged rollout (1% → 2% → 5% → 10% → 20% → 50% → 100%) to users with automatic updates. A crashing build caught on day 1–2 has burned ~3% of your users; pause the rollout, fix, resubmit. Without it, 100% of users get the bad build and the 1-star flood arrives before the hotfix does.
  • ✅ Default for every release: phased ON + manual release + monitor day-1 crash rate.
  • ❌ Halting a phased release as a rollback. Pausing stops new deliveries only — users who got the build keep it, and manual App Store downloads always get the new version. The armor is damage limitation; the fix still has to ship.

Rule 4: The prompt-and-reply loop is the only rating input you control

  • Prompt at success moments with requestReview — never at launch, never mid-task. Aim the moment, cap the frequency, and localize what "success" means per market. Implementation: generators/review-prompt.
  • Reply to negative reviews — when a user updates their review after a reply, the new score replaces the old one in the average. Replies are the only mechanism that converts existing 1-stars into 4-stars. Templates: app-store/review-response-writer.
  • Per-storefront corollary: track "unanswered negative reviews" per market, not globally — a 5-review storefront is one bad review away from 60% negative.

Output Format

When auditing an app's ratings posture, report per storefront:

Storefront | Rating (count) | Unanswered 1–3★ (90d) | Prompt localized? | Phased+manual habit?

flagging: fresh storefronts with no prompting plan, any reset consideration (🔴 stop), and releases going out unphased.

References

Frequently asked questions

What does the Ratings Mechanics AI skill do?

How App Store ratings actually behave — per-storefront isolation (your US stars show nowhere else), the never-reset rule, phased release + manual release as rating protection, and where prompting/replying fit. Use when planning ratings strategy for new markets, considering a ratings reset, setting release options, or diagnosing "why is my rating missing in country X."

Why use Ratings Mechanics on TypingMind?

Because you install it once and use it with any model. Ratings Mechanics 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 Ratings Mechanics in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/rshankras/claude-code-apple-skills/tree/main/skills/app-store/ratings-mechanics. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Ratings Mechanics?

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 Ratings Mechanics?

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

Is the Ratings Mechanics AI skill free?

Yes. It is published on GitHub by rshankras 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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