The Loot Pool Trap

I spent a few weeks pulling apart loot systems from games I actually play. Base rate. Hard pity. Soft pity ramp. Expected value.

Nobody publishes P50 vs P90. At 0.6% base rate and two new legendary items added to the pool each month, the expected pulls to land one specific early-release cosmetic goes from ~1,200 at launch to ~5,200 by month 12. 4.4x harder. The battle pass "500% value" claim is rarely verified against a mid-game player who's already sitting on maxed filler currency.

Korea fined publishers for misleading lootbox odds. Apple required probability disclosure in 2017. Most of this math has never been independently audited.

So I built 7 AI skills to do it. Paste your loot table config. Get a structured audit.

1. Gacha Pity Auditor: EV, P50, P90, worst-case, soft pity model check
2. Sparking vs Hard Pity Simulator: both systems side-by-side, carry-over value quantified
3. Pool Dilution Calculator: 24-month probability timeline, auto-flags when expected pulls cross 1,000
4. Shard Economy Analyzer: binary vs variable drop math handled separately
5. Lootbox Compliance Generator: disclosure tables for Apple, Google, Japan METI, China MoC, Belgium, Netherlands, UK
6. Kompu Gacha Risk Detector: 3-condition legal check, CLT worst-case cost
7. Battle Pass ROI Calculator: segments value by new player, mid-game, and whale; checks your marketing claim against actual mid-game ROI

Works on Claude, ChatGPT, and Gemini. MIT licence. No signup.

Comment GACHA and I'll DM you the GitHub link.

Which of these seven would your game fail?

Hashtags

#mobilegaming #liveops #gameproduction #gacha #lootbox #gamedev #indiegame #aitools

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