Verify mathematical derivations by clustering independent re-derivations by equivalence.
npx skills add https://github.com/fkguo/nullius --skill derivation-verify
Please help me install this Agent Skill. Skill: derivation-verify Source: https://github.com/fkguo/nullius/tree/main/skills/derivation-verify Command: npx skills add https://github.com/fkguo/nullius --skill derivation-verify
This Skill verifies the correctness of derived mathematical results by independently re-deriving them multiple times and ensuring agreement among the results.
Use the derivation-verify skill to verify the correctness of the derived formula 'A(q)=arctan(q/2m)/2q' by providing the context and the formula to derive.
High-intent search queries and answers about installing and using this skill.
Verify a mathematical derivation by independently re-deriving the result multiple times and clustering outcomes by mathematical equivalence. This process requires agreement among at least two independent derivations to ensure correctness.
Independent mathematical verification performs multiple independent re-derivations of a symbolic result and clusters them by mathematical equivalence. An adversarial comparator enforces convergence by requiring agreement among at least two independent derivations.
Yes, derivation verification applies to any field requiring verification of symbolic results. You can verify derived formulas like arctan(q/2m)/2q by providing the context and the formula to re-derive independently.
Yes, independent verification requires Claude, Codex, Gemini, Kimi, or OpenCode CLI multi-backend executors. These enforce cross-model agreement to ensure convergence among at least two independent derivations.
Check mathematical equivalence by clustering multiple independent re-derivations of a symbolic result rather than comparing string equality. An adversarial comparator ensures convergence by requiring agreement among at least two independent derivations.
Independent derivation verification requires cross-model agreement to enforce convergence among at least two independent derivations. This adversarial comparison ensures the correctness of derived mathematical results by eliminating single-model bias.
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