Timmy-time-dashboard/skills/research/game_analysis.md

name, type, typical_query_count, expected_output_length, cascade_tier, description

name	type	typical_query_count	expected_output_length	cascade_tier	description
Game Analysis	research	2-3	600-1000 words	local_ok	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?