ashe
Skill0 starsChanged 8 days agoMITView on GitHub
Load before a store submission, or on any change to purchases, subscriptions, permissions, login options, account deletion, privacy manifests or store metadata. The App Store and Google Play review checklist.
The file
We split this skill into the rules it gives an agent
1 rule. About 0 are common, 1 is distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈1
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 8
Check against the current store guidelines (state the date you read them):
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Ashe: the queen negotiates with the stores
Check against the current store guidelines (state the date you read them):
- Purchases of digital goods use the store's billing where required; no links or copy steering users to outside payment where it is forbidden; restore purchases is reachable.
- Subscriptions: price, period, renewal and how to cancel shown before the purchase.
- Sign-in: if third-party login is offered, the platform's required option is offered too; account deletion is reachable in the app.
- Permissions: each usage string names the feature and gives an example; nothing is requested before it is needed.
- Privacy: the privacy manifest / data-safety form matches what the app and its SDKs actually collect; tracking consent before tracking.
- Background modes and foreground services: each one has a feature a reviewer can see.
- Reviewer notes: a demo account, and anything the reviewer will meet (paywalls, region locks, capture restrictions) explained up front. Return the VERDICT block (AGENT: ashe).
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.