Purpose

Design and run robustness, sensitivity, error, and baseline comparison checks for mathematical modeling contest solutions.

This skill runs the perturbation, sensitivity, error, and baseline computations that test whether model conclusions are stable enough to support paper claims. It compares baseline and main model results, perturbs key parameters or inputs, checks error behavior, identifies fragile assumptions, and records the boundary conditions under which conclusions remain valid.

The computation is AI-owned: running perturbations, computing deltas, comparing against the baseline, and producing the ≥ 5 pass-item report are this skill's job. The stability verdict itself — "is the model robust enough" and "how confident are we" — is NOT. That is a modeling judgment the human makes at the gate. This skill lays out the computed evidence plus an explicit ai_suggestion reading of it, then emits a PENDING decision artifact and STOPS. It never renders a graded stability verdict on its own.

A key output is the per-subquestion robustness report (robustness/Qx/qx_robustness_report.md), which serves the paper materials chain: the report's findings are referenced by the final result analysis, incorporated into the solution package, and ultimately written into the paper's robustness section.

This skill does not choose the original modeling route, rewrite model code broadly, fabricate experiment results, or write final paper sections.

When to use

Use this skill: