Explore strategic decisions in Wardley Maps using MCTS and LLM scoring.
npx skills add https://github.com/owulveryck/alphazego --skill wardley-explore
Please help me install this Agent Skill. Skill: wardley-explore Source: https://github.com/owulveryck/alphazego/tree/main/exp/wardley Command: npx skills add https://github.com/owulveryck/alphazego --skill wardley-explore
This Skill helps you explore strategic Wardley Maps by using Monte Carlo Tree Search (MCTS) and a Language Model (LLM) to find optimal strategic sequences.
To explore a Wardley Map, provide the skill with a .wtg2 file and specify the desired depth and iterations.
High-intent search queries and answers about installing and using this skill.
To explore strategic decisions in a Wardley Map using MCTS, you provide a .wtg2 file and specify the desired depth and iterations. The Skill applies Monte Carlo Tree Search and LLM scoring to find optimal strategic sequences.
Calculating policy and value scores for Wardley Map states is handled by applying Monte Carlo Tree Search combined with a Language Model. The LLM evaluates strategic states while MCTS computes policy scores for potential moves.
Yes, you can use a Language Model to score strategic states in complex environments. This Skill leverages an LLM to evaluate states and guide the exploration of strategic moves within your Wardley Map.
Yes, you need a .wtg2 file format to start strategic planning with Monte Carlo Tree Search. You must provide this file and specify the exploration depth and iterations to begin the interactive analysis.
Interactive Wardley Map exploration works by parsing your .wtg2 file and applying MCTS to compute potential moves. It allows step-by-step exploration by evaluating strategic states with an LLM at each specified depth and iteration.
You should not use MCTS for decision-making in strategic planning if your environment lacks complex, branching strategic states. The Skill requires parsing and evaluating Wardley Maps, making it unsuitable for simple, linear strategic decisions.
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