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math-verifier

Audits mathematical derivations, statistical methods, and transformer circuit mathematics for AIKAGRYA research rigor.

1|Updated Feb 14, 2026
Search Tags:#verification#mathematics#statistics#causal inference#research rigor#transformer circuits
One-click install
npx skills add https://github.com/AmitabhainArunachala/clawd --skill math-verifier
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: math-verifier
Source: https://github.com/AmitabhainArunachala/clawd/tree/main/skills/math-verifier
Command: npx skills add https://github.com/AmitabhainArunachala/clawd --skill math-verifier

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill guarantees absolute mathematical rigor in AIKAGRYA research, ensuring all derivations, statistical methods, causal claims, and transformer circuit mathematics are sound before publication or dissemination.

Core Features & Use Cases

  • Mathematical Derivation Audit: Verifies R_V metric calculations and geometric interpretations.
  • Statistical Method Verification: Audits Cohen's d, p-values, and confidence intervals.
  • Causal Inference Audit: Checks for correlation vs. causation fallacies and validates activation patching.
  • Transformer Circuit Mathematics: Validates attention mechanisms, QK/OV circuits, and residual stream algebra.
  • Formal Verification Reports: Generates detailed reports on the soundness of mathematical claims.
  • Use Case: Before submitting a paper on a new AIKAGRYA model, use this agent to formally verify that the reported R_V metric is correctly calculated and that the statistical significance of the results is robust.

Quick Start

Use the math-verifier skill to audit the R_V derivations in the ~/mech-interp-latent-lab-phase1 directory.

Frequently Asked Questions about math-verifier

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

FAQPage Schema
How do I verify mathematical derivations and statistical methods in AI research?▼

Causal inference claims are audited by checking for correlation versus causation fallacies and validating causal claims like activation patching to ensure research conclusions are mathematically robust and sound.

What is the best way to validate transformer circuit mathematics for attention mechanisms?▼

Validating transformer circuit mathematics involves auditing attention mechanisms, QK/OV circuits, and residual stream algebra to ensure the derivations are correct and meet high publication standards.

How do I audit R_V metric calculations and geometric interpretations in a research directory?▼

You can audit R_V metric calculations by running a verification script against your research directory to formally verify correctness and generate a detailed report on mathematical soundness.

Does this statistical method verification tool check for correlation vs causation fallacies?▼

Yes, statistical method verification includes a causal inference audit that explicitly checks for correlation vs causation fallacies and validates activation patching claims for research rigor.

When do I need formal verification reports for mathematical claims?▼

You need formal verification reports when preparing AI research for publication, ensuring reported metrics are correctly calculated and statistical significance is robust before dissemination.

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