--- name: Game Analysis type: research typical_query_count: 2-3 expected_output_length: 600-1000 words cascade_tier: local_ok description: > Evaluate a game for AI agent playability. Assesses API availability, observation/action spaces, and existing bot ecosystems. --- # Game Analysis: {game} ## Context Evaluate **{game}** to determine whether an AI agent can play it effectively. Focus on programmatic access, observation space, action space, and existing bot/AI ecosystems. ## Constraints - Platform: {platform} (PC, console, mobile, browser). - Agent type: {agent_type} (reinforcement learning, rule-based, LLM-driven, hybrid). - Budget for API/licenses: {budget}. ## Research Steps 1. Identify official APIs, modding support, or programmatic access methods for {game}. 2. Characterize the observation space (screen pixels, game state JSON, memory reading, etc.). 3. Characterize the action space (keyboard/mouse, API calls, controller inputs). 4. Survey existing bots, AI projects, or research papers for {game}. 5. Assess feasibility and difficulty for the target agent type. ## Output Format ### Game Profile | Property | Value | |-------------------|------------------------| | Game | {game} | | Genre | {genre} | | Platform | {platform} | | API Available | Yes / No / Partial | | Mod Support | Yes / No / Limited | | Existing AI Work | Extensive / Some / None| ### Observation Space Describe what data the agent can access and how (API, screen capture, memory hooks, etc.). ### Action Space Describe how the agent can interact with the game (input methods, timing constraints, etc.). ### Existing Ecosystem List known bots, frameworks, research papers, or communities working on AI for {game}. ### Feasibility Assessment - **Difficulty:** Easy / Medium / Hard / Impractical - **Best approach:** {recommended_agent_type} - **Key challenges:** Bullet list - **Estimated time to MVP:** {time_estimate} ### Recommendation One paragraph: should we proceed, and if so, what is the first step?