Run Nash game-theory simulations via NASH CLI and output JSON results.
npx skills add https://github.com/chiangchenghsin-hash/n-nash --skill nash-run
Please help me install this Agent Skill. Skill: nash-run Source: https://github.com/chiangchenghsin-hash/n-nash/tree/main/nash-run Command: npx skills add https://github.com/chiangchenghsin-hash/n-nash --skill nash-run
This Skill helps you execute reproducible game-theory simulations and experiments so you can obtain machine-readable results instead of running ad-hoc, non-repeatable trials.
You can run a Hawk-Dove simulation across multiple seeds, sweep a key parameter range, or compare several environments (e.g., public_goods vs common_pool) and then produce validation outputs and charts.
Ask the AI agent to run a 200-round Hawk-Dove simulation with 100 agents, seed 42, and save the results to results.json, then validate and visualize the output.
High-intent search queries and answers about installing and using this skill.
To run reproducible game theory simulations, execute NASH CLI commands via uv run nash with specific random seeds and save the JSON outputs to structured files for consistent downstream analysis and validation.
Yes, you can perform parameter sweeps across multiple game environments by splitting configurations across parallel subagents, allowing side-by-side evaluation of models like public_goods and common_pool.
Nobel-style equilibrium validation checks simulation convergence across multiple parameter sweeps and seeds, generating JSON result artifacts that confirm stable strategic equilibria in preset game environments.
You generate report-ready charts by invoking the validate and viz follow-up workflow after your simulation runs, which processes the JSON outputs into visual artifacts for immediate analytical review.
No external dependencies are required to run reproducible game simulations, as the tool operates standalone by invoking the NASH CLI through uv run nash to generate JSON outputs directly.
The best way to compare multi-model performance is running parallel environments with controlled seeds and executing parameter sweeps, then applying post-run validation to produce standardized JSON artifacts.
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