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.
npx skills add https://github.com/rshankras/claude-code-apple-skills --skill 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.
product/localization-strategyYour 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:
eagerly (within guidelines), localize the prompt moment, reply to every early review.
requestReview triggers by storefront maturity — a market with 12 ratings needs theprompt more than the home market with 5,000.
converts like an unknown app, because there it *is* one.
and seed them via TestFlight communities or launch outreach in that region.
ASC offers a reset when you release a new version. It is almost always a mistake:
a naked 5.0★ from 6, and the count never comes back except one rating at a time.
replying to outdated negative reviews (updated ratings replace the old score — see
review-response-writer) and by the What's New copy.
app with almost no ratings mass. Even then, run the math on count loss first.
months.
Two ASC toggles turn a bad build from a rating catastrophe into a contained incident:
crash dashboards, not whenever review finishes.
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.
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.
requestReview — never at launch, never mid-task. Aim themoment, cap the frequency, and localize what "success" means per market. Implementation:
generators/review-prompt.
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.
5-review storefront is one bad review away from 60% negative.
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.
generators/review-prompt, app-store/review-response-writer, product/localization-strategy, growth/store-growth-auditComplete development kit for Microsoft 365 Copilot declarative agents with three comprehensive workflows (basic, advanced, validation), TypeSpec support, and Microsoft 365 Agents Toolkit integration
Format and structurally validate local treatment-plan documentation after clinical decisions have already been supplied and verified by authorized licensed professionals. Use for source traceability, clinician-authored intervention records, goals and checkpoints, shared-decision records, reconciliation handoffs, and release gates—not for clinical decision-making.
> provider/change budget/修改卖家/修改预算/draft/草稿/我的任务/my tasks/what am I working on/关闭/取消任务/决策列表/decision list/指定服务商/browse (sender.role = COUNTERPARTY, not you); (3) literal "Read the okx-ai skill" (or legacy "Read the okx-agent-task skill") in the envelope.
Automate payer review of prior authorization (PA) requests. This skill should be used when users say "Review this PA request", "Process prior authorization for [procedure]", "Assess medical necessity", "Generate PA decision", or when processing clinical documentation for coverage policy validation and authorization decisions.
Expert in designing and building autonomous AI agents. Masters tool use, memory systems, planning strategies, and multi-agent orchestration.
Autonomous agents are AI systems that can independently decompose goals, plan actions, execute tools, and self-correct without constant human guidance. The challenge isn't making them capable - it's making them reliable. Every extra decision multiplies failure probability.
Orchestrates design workflows by routing work through brainstorming, multi-agent review, and execution readiness in the correct order.
Structured persuasion for tech leads, PMs, and founders—not activity logs. Five scenarios (kickoff, status update, wrap-up, investor pitch, solution selling) on one 5-part framework (Hook→Context→Proposal→Evidence→Ask). AI prompts for missing materials and audience context; pre-submit checklist. Claude Code plugin; Cursor, Codex, and chat via prompts.
Take rshankras/ratings-mechanics from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.