Informazioni su questa demo

A fresh take on deckbuilding using Rune-based combat in this strategic turn-based roguelite.

Choose your path through mystical realms in your attempt to reach Ariah, using an innovative 2x3 rune grid system. Combine Action, Passive, and Catalyst runes to create devastating combos.

Key Features

🔮 Rune System

• 2x3 Grid Combat: Use runes strategically depending on their placement in the grid.

• Every rune type serves a purpose:

  • Action Runes: Your primary combat tools requiring manual activation

  • Passive Runes: Automatically trigger each turn for consistent benefits

  • Catalyst Runes: Enhance adjacent runes with multipliers and bonuses

⚔️ Deep Strategic Combat

🗺️ Roguelike Progression

👥 Unique Characters & Abilities

• EmberMonk: Master of flame and inner strength

• Character-Specific Abilities: Each character has unique abilities

• Customizable Builds: Choose from unlocked abilities to create your perfect playstyle

Combat That Rewards Strategy

Unlike traditional deckbuilding card games, Trial of Ariah's rune system gives you a fresh take on a beloved gaming system. The 2x3 grid creates spatial strategy where rune placement matters as much as rune selection. Catalyst runes transform adjacent abilities - a simple damage rune becomes devastating when buffed by a Triple Right catalyst.

Demo Features

Currently Available:

• Complete rune-based combat system

• Procedural map generation for endless replayability

• The first zone with an end boss

Coming Soon:

• Additional playable characters with unique abilities

• More areas and mechanics

• Expanded enemy roster and boss encounters

• Enhanced story elements and world-building

• Quality of life improvements based on community feedback

Embark on your trial. Master the runes. Discover what lies beyond. But can you reach Ariah?

Divulgazione dei contenuti generati dall'IA

Gli sviluppatori descrivono il modo in cui il loro gioco utilizza i contenuti generati dall'IA in questo modo:

Some assets were developed working with large language models