Translate natural-language game-state questions into RAM predicate evaluations.
npx skills add https://github.com/scawful/afs_scawful --skill semantic-query
Please help me install this Agent Skill. Skill: semantic-query Source: https://github.com/scawful/afs_scawful/tree/main/skills/semantic-query Command: npx skills add https://github.com/scawful/afs_scawful --skill semantic-query
Translates natural-language in-game questions into RAM predicates to quickly determine current conditions and state.
Use a direct instruction like: Ask semantic-query to evaluate is_overworld and return the result.
High-intent search queries and answers about installing and using this skill.
To query game state with natural language predicates, you translate human-readable questions into RAM predicate evaluations on the current memory map. This allows an AI agent to quickly assess conditions like is_overworld or in_cutscene to drive decisions.
RAM inspection for AI agent game conditions works by mapping natural-language questions to boolean or simple value predicates across RAM representations. It evaluates memory addresses to verify player status and inventory, returning simple values to drive agent behavior.
Yes, you can check if a player can_control or has an item by inspecting game memory. The system evaluates state queries such as has <item> and is_safe against the current RAM map to return boolean results for agent decision making.
The best way to translate natural-language game questions into boolean values is by using a unified interface that maps human queries directly to RAM predicate evaluations. This approach handles scene assessments and inventory checks to return simple values for dynamic environments.
Yes, you need a current memory map to evaluate AI agent predicates in a dynamic game environment. The system relies on RAM representations to perform predicate evaluations and return boolean results for questions like is_dungeon or is_safe.
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