fisherman_RottenFleshToLeather

A vanilla-plus, lightweight data utility to smelt Rotten Flesh into Leather in standard Furnaces. Balanced and conflict-free.

Rotten Flesh to Leather (Universal Stable)

Tired of hoarding mountains of useless Rotten Flesh in your storage chests? Rotten Flesh to Leather is a vanilla-plus, high-performance, and lightweight data utility that bridges this survival immersion gap using pure, rigorous data-driven engineering.

No active scripts, no redundant tick counters, and absolutely no complex experimental toggles required.


⚡ Engineering & Mechanics

  • 🟢 Production-Layer Smelting: Registers a clean, incremental data recipe inside the standard vanilla furnace container. Input 1x minecraft:rotten_flesh to smoothly cook it into 1x minecraft:leather.
  • 🟢 Industrial Precision Balancing: Every piece of flesh takes exactly 10.0 seconds to process (matching vanilla standard smelting speed) and generously rewards the player with 0.1 experience points (equivalent to smelting a block of cobblestone).
  • 🟢 Conflict-Free Namespace: Uses a dedicated fisherman: unique identifier namespace. It loads incrementally during world initialization and will NEVER overwrite vanilla recipes or conflict with third-party behavior packs.

🧪 Compatibility Report

  • Vanilla Stable (No Experiments): Fully optimized for Minecraft Bedrock 1.21.20 and above. It binds strictly to the asset-driven data layer. You do NOT need to toggle on any "Beta APIs" or experimental switches for your world survival or realms.
  • Absolute Zero Overhead: Since it utilizes native C++ asset-driven rendering without any heavy background logical loops, the impact on your world's TPS and CPU memory is absolute zero. Perfect for low-end mobile hardware and dedicated BDS servers.

💬 Developed by a Computer Science graduate and passionate Java/Bedrock player. If you appreciate robust, clean data structures that respect your device's performance, feel free to check out my other scripted combat tools!

fisherman_RottenFleshToLeather Team

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