Falsify or support mathematical claims via Monte-Carlo parameter sweeps.
npx skills add https://github.com/flonat/flonat-research --skill numerical-check
Please help me install this Agent Skill. Skill: numerical-check Source: https://github.com/flonat/flonat-research/tree/main/skills/numerical-check Command: npx skills add https://github.com/flonat/flonat-research --skill numerical-check
This Skill provides a method to numerically verify or falsify mathematical claims, ensuring that your hypotheses are based on robust empirical testing.
Perform a numerical check on your conjecture "f(x) = x^2 + 1 is always positive" by providing the script and specifying the domain.
High-intent search queries and answers about installing and using this skill.
You can verify mathematical conjectures by running numerical stress tests and Monte-Carlo sweeps across the parameter space to empirically support or falsify the claim.
Yes, you can find counterexamples to invalidate mathematical claims by performing empirical testing and sweep analysis to identify parameter values where the function behavior fails.
Yes, you need numpy and scipy to script the empirical analysis and perform the numerical stress testing required to evaluate continuous or large-n limit statements.
The best way to numerically falsify self-authored math statements is to script a parameter space sweep that searches for specific counterexamples violating your continuous limit hypothesis.
You should avoid numerical stress testing when your mathematical claim requires strict formal proof rather than empirical support, or when your conjecture involves discrete logic outside continuous parameter spaces.
Yes, numerical-check works for large-n limit statements by utilizing Monte-Carlo methods and parameter sweeps to empirically test the behavior of your mathematical functions.
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