Classify real-world scenarios into Nobel game-theory environment models for simulation.
npx skills add https://github.com/chiangchenghsin-hash/n-nash --skill nash-env
Please help me install this Agent Skill. Skill: nash-env Source: https://github.com/chiangchenghsin-hash/n-nash/tree/main/nash-env Command: npx skills add https://github.com/chiangchenghsin-hash/n-nash --skill nash-env
Help users identify which existing Nobel game-theory environment best matches a described real-world situation, so they can run the correct simulations and equilibrium checks.
Ask the AI: "Given this scenario [describe participants, incentives, information, and whether interactions repeat], which Nobel game-theory model fits best, and what assumptions do you need from me?"
High-intent search queries and answers about installing and using this skill.
To map a real-world scenario to a game theory model, classify the participants, incentives, and information structure to identify the matching Nobel game-theory environment for equilibrium simulation. This process confirms key assumptions to recommend the best fit.
The best way to choose between game-theory environments for a cross-domain problem is using agent-team parallel reasoning. This coordinates multiple subagents to compare candidate models and generate a scored comparison table for accurate model discovery.
To identify the fitting Nobel game-theory environment for a multi-agent reasoning problem, describe your scenario's participants, goals, information, and time horizon. A decision tree confirms assumptions to classify the correct model, such as Hawk-Dove or Public Goods.
Yes, you can inspect how payoffs and equilibria are computed through source-code guided environment understanding. This feature explains environment mechanics and points to the relevant environment implementation files for direct review.
Yes, the model classification process uses memory-guided routing to persist your final game-theory selection. This memory saves the chosen environment and seamlessly routes the workflow to the simulation execution skill.
You should not use automated environment mapping if your real-world scenario lacks clearly defined participants, goals, or information structures. Accurate equilibrium simulation requires confirming these key assumptions through the decision tree before proceeding.
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