game-monetization
Skill12 starsChanged 23 days agoMITView on GitHub
Use when a game or game-like product introduces paid offers, ads, passes, subscriptions, trials, premium unlocks, paywalls, or store copy and the boundary between pricing, entitlement, gameplay, and evidence is unclear.
1 other project uses a version of this.
Adopted by
Most recently taken up by
- poorvith-mp/skills-gamedevtheir own version, first seen Aug 19, 2026
Other versions1 project uses a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 1
- poorvith-mp/skills-gamedev★ 4same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository194 other files it gives its agents.
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- The first 60 shown.
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 34
Every offer identifies what is gated, duration/renewal if applicable, price source/region when known, trial/expiry behavior, and …
≈ “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
Game Monetization
This skill owns monetization model selection, offer/gate semantics and evidence boundaries. It does not verify store transactions, grant ownership, set combat timing, or guarantee business outcomes.
Modes
| Mode | Offer model |
| cosmetic-iap | paid cosmetic/presentation goods |
| battle-pass | seasonal track tied to season-track |
| ad-reward | authored optional reward/continue/ad flow |
| premium-unlock | one-time content/feature gate |
| live-sub | recurring service/content entitlement |
| fair-f2p | project policy emphasizing non-paid gameplay parity |
These are product modes, not moral or revenue claims. A project can define additional models explicitly.
Ownership boundaries
- SKU/offer copy, gate and price intent live here/
iap-offers. - Verified purchase ownership and idempotent delivery live in
entitlement-grant. - Restore behavior lives in
restore-purchase. - Wallet accounting lives in
currency-dual. - Gameplay timing/hitboxes/cancels remain in
action-feeland their real owners; this skill does not secretly author +frames, cooldowns, or collision. - Seasonal tier/claim state lives in
season-track.
Offer evidence
Every offer identifies what is gated, duration/renewal if applicable, price source/region when known, trial/expiry behavior, and restore/cancel path where the platform/product requires it. Unverified price or competitor data stays marked unverified.
Claims such as conversion, retention, ARPU, uplift or revenue require actual analytics with population/window/experiment context. A configured SKU, debug entitlement, test purchase or design hypothesis does not prove revenue impact.
Store/platform compliance is version- and region-specific; verify current platform requirements before shipping. Calling this skill never authorizes a production price change by itself.
Acceptance
A reviewer can trace offer → verified entitlement/restore owner → granted product state without monetization code owning combat mechanics. Test buy/skip/restore/expiry paths appropriate to the selected model, and keep business performance claims separate from implementation correctness. No sentence promises revenue lift without measured evidence.
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.