Audit mathematical derivations and statistical methods in interpretability research.
npx skills add https://github.com/AmitabhainArunachala/clawd --skill math-auditor
Please help me install this Agent Skill. Skill: Math Auditor Source: https://github.com/AmitabhainArunachala/clawd/tree/main/skills/math-auditor Command: npx skills add https://github.com/AmitabhainArunachala/clawd --skill math-auditor
This Skill provides expert-level mathematical and statistical verification for mechanistic interpretability research, ensuring the rigor and validity of claims, especially concerning metrics like R_V and consciousness geometry.
Use the Math Auditor skill to verify the R_V metric calculation in the file src/metrics/rv.py.
High-intent search queries and answers about installing and using this skill.
To verify mathematical derivations in mechanistic interpretability research, you can audit linear algebra, calculus, and optimization formulas to ensure research claims are mathematically sound and correctly aligned with code implementations.
The best way to audit statistical methodology in deep learning research is to systematically check statistical tests, effect sizes, and sample size justifications, ensuring your statistical methodologies are valid and free of common red flags.
You validate code-theory alignment for metrics like R_V and consciousness geometry by checking that the implementation in code precisely matches the mathematical definitions, ensuring the calculated results are theoretically accurate.
Yes, you can use numpy and scipy for math verification of deep learning research claims, as the statistical and mathematical auditing process relies on these dependencies alongside torch to validate linear algebra and statistical properties.
You need statistical auditing for mechanistic interpretability metrics before publishing research on a new metric, ensuring the metric's definition, statistical properties, and codebase implementation are mathematically sound and statistically valid.
Your interpretability metric calculation may not match its mathematical definition due to code-theory misalignment, where the implementation in the codebase deviates from the formal mathematical definitions or contains common statistical red flags.
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AmitabhainArunachalaAudit mechanistic interpretability claims against statistical rigor and validation tiers.
