robustness-checker

Sensitivity analysis tests model stability by analyzing how outputs change with variations in parameters and inputs. This Skill automates that process for mathematical modeling solutions, generating per-subquestion robustness reports for human review.

Model robustness checking involves testing model stability under perturbations and variations. You need it after finalizing a model and reviewing its code to ensure conclusions are stable and reliable before proceeding to the next project stage.

Baseline comparison validates model improvements by comparing results against baseline models. This Skill automates the baseline comparison checks for mathematical modeling contest solutions, outputting decision artifacts for human review.

No, this checker requires validated candidate methods, reviewed code, and model outputs as prerequisites. It automates robustness, sensitivity, error, and baseline comparison checks based on these completed inputs.

The best way to automate error analysis is using a Skill that assesses model performance using error metrics and residual analysis. This tool automates error analysis alongside robustness and sensitivity checks for modeling contest solutions.

When checking model robustness for multiple subquestions, the Skill outputs per-subquestion reports and decision artifacts strictly for human review. It does not make final decisions and requires validated candidate methods and reviewed code to function.