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nash-analyze

Validate NASH simulation results against Nobel equilibrium benchmarks and generate statistical interpretations.

1|Updated May 29, 2026
Search Tags:#nash equilibrium#game theory#validate#analyze#visualize#memory persistence#statistical testing
One-click install
npx skills add https://github.com/chiangchenghsin-hash/n-nash --skill nash-analyze
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: nash-analyze
Source: https://github.com/chiangchenghsin-hash/n-nash/tree/main/nash-analyze
Command: npx skills add https://github.com/chiangchenghsin-hash/n-nash --skill nash-analyze

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It helps users interpret Nash simulation results by converting raw output into human-readable conclusions, Nobel benchmark validation, statistical significance checks, and meaningful visualizations.

Core Features & Use Cases

  • Nobel benchmark validation: Verifies whether each environment converges to the equilibrium predicted by Nobel-winning game theory models.
  • Statistical validation and interpretation: Assesses convergence quality and what the results imply, including confidence and actionable next steps.
  • Visualization and reporting: Generates charts (and can guide Mermaid/HTML-ready structured summaries) so users can quickly understand trends and anomalies.
  • Multi-perspective synthesis: Produces a consensus explanation using multiple analysis angles (theory, statistics, practical implications, and a devil’s-advocate review).
  • Memory persistence: Stores conclusions and links them to source experiments for cross-session continuity.

Quick Start

Use the nash-analyze skill to validate and visualize the file results.json by interpreting it with Nobel verification and generating chart-ready output.

Frequently Asked Questions about nash-analyze

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I validate Nash equilibrium convergence in simulation results?▼

Nash equilibrium validation checks whether your simulation environments converge to the equilibria predicted by Nobel-winning game theory models, using statistical significance tests to assess convergence quality and confidence.

What is the best way to interpret Nash simulation output JSON?▼

Interpreting Nash simulation output involves parsing result schema keys like environment and history, running validate and visualize workflows, and summarizing statistical findings into plain language decision-grade conclusions.

Can I visualize Nash simulation metrics across different environments and time-series?▼

Visualizing Nash simulation metrics generates charts and structured Mermaid or HTML-ready summaries, allowing you to quickly understand trends and anomalies across multiple environments and time-series data.

Does analyzing game theory simulation results require statistical testing?▼

Statistical testing is required to assess convergence quality and interpret what game theory simulation results imply, providing confidence levels and actionable next steps for decision-grade conclusions.

How do I persist Nash simulation conclusions for cross-session continuity?▼

Persisting Nash simulation conclusions stores validated insights and links them to source experiments in memory, enabling cross-session continuity for ongoing hypothesis verification and analysis.

What are the limitations of using Nash equilibrium benchmarks for simulation validation?▼

Nobel benchmark validation relies on parsing specific result schema keys like environment and history, meaning incomplete JSON structures or missing simulation data can prevent accurate equilibrium convergence verification.

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