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[NEW] +1 Slash Bubble Wrap

[NEW] +1 Slash Bubble Wrap

A Roblox experience owned by Project Novice Builders.

SimulatorENclickerasmrpets

Description (written by the developer; not a statement by this register)

Welcome to +1 Slash Bubble Wrap! ⚔️ 💥 Every click gives you MORE Strength! 🧱 Use your Strength to Slash through satisfying ASMR Walls! 🗡️ Unlock STRONGER & CRAZIER Swords! 👑 Earn Wins and discover new Worlds with different wall types! 🏆 Climb to the top of the leaderboards! 🐾 Hatch PETS for huge boosts! ✨ Collect Auras for powerful multipliers! 📱 Supports ALL Devices! 🎁CODES: - Release 👍 Like & Favorite if you enjoy the game! ⭐

Last updated by the developer 27 Sept 2026, 14:31 UTC.

0
playing (measured 26 Oct 2026, 12:49 UTC)
14,533
lifetime visits
76%
liked (59 👍 / 19 👎)
94
favourites (6.5 per 1k visits)
17 August 2026
created (per Roblox)

Play on Roblox →

Live players over time

Growth

Growth history is still building. We hold one measurement for this entry. A trend needs at least two, taken days apart — so this will fill in on its own rather than being estimated. We do not publish numbers we have not measured.

Measurement log — every players count we have recorded
Measured (UTC)PlayersChange
26 Oct 2026, 12:490first reading

Concurrent players at each of our measurements (insert-on-change).

Monetization — game passes

Godly Dummy
1,499 R$
Exclusive Sword
999 R$
Admin Dummy
799 R$
Hacker Aura
404 R$
Exclusive Dummy
299 R$
x2 Wins
299 R$
👑 VIP 👑
249 R$
+1 Pet Equip
199 R$
+1 Pet Equip
149 R$
+1 Pet Equip
99 R$
+15 Speed
99 R$
OP Auto Clicker
79 R$
Multi Hatch
79 R$
Starter Pack
39 R$
More Pet Storage
29 R$

Game passes for sale and their prices, measured from the Roblox passes API. Developer products bought inside the game aren’t publicly listable.

Badge funnel

Welcome! · welcome10,656 · 0.00% win

How many players have ever earned each badge — a proxy for how far players get before they stop. Steep drop-offs mark where a game loses people. Win rate is the past-day share of players who earned it; categories are assigned by a language model from each badge’s own name and description.