Analyzes and summarizes observation layers and agent inventory encoding for the Tribal Village environment.
The tv-observation skill is designed to help developers and AI agents navigate the complex state representations of the Tribal Village simulation. By automatically extracting and summarizing observation layers, tinting types, and inventory update logic from Nim source files, it provides a clear picture of how the environment communicates data to agents. This skill is particularly useful for those building Reinforcement Learning models or debugging game mechanics, as it eliminates the need to manually grep through type definitions and environment logic to understand state encoding.
주요 기능
01Summarizes inventory handling and update logic
02Identifies observation layer and tint definitions in Nim code
03Extracts specific state-related code blocks for quick review
04Streamlines debugging of environment-to-agent state transitions
05Maps observation space encodings for agent input design
062 GitHub stars
사용 사례
01Debugging agent inventory encoding within the Tribal Village simulation
02Documenting environment observations for new project contributors
03Mapping state representations for training Reinforcement Learning agents
