xujunjie-coder/math-modeling-workflow/robustness-checker

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Design and run risk-targeted robustness, sensitivity, error, and baseline checks for an approved mathematical model, emitting compact machine evidence in lean mode and a final report in submission mode.

Install

npx vskill@latest install xujunjie-coder/math-modeling-workflow/robustness-checker

SKILL.md

The same SKILL.md was published earlier as mrhaozzzz/skills/robustness-checker.

Purpose

Test the claims most likely to fail. Choose checks from the model's assumptions and decision risks rather than filling a generic checklist.

Preconditions

  • Approved main and usable baseline executed.
  • Run summary, method card, probe summary, and relevant outputs exist.
  • Claim or decision to be tested is known.

Workflow

  1. Identify load-bearing assumptions and claims.
  2. Select applicable checks:
  • parameter or weight perturbation;
  • alternate split or resampling;
  • seed stability;
  • outlier/missing-data treatment;
  • constraint/capacity perturbation;
  • baseline comparison;
  • output concentration/rank stability;
  • error and uncertainty analysis.
  1. State perturbation ranges and why they are meaningful before interpreting results.
  2. Run checks with fixed seeds where stochastic.
  3. Save compact metrics to:

robustness/Qx/qx_robustness_summary.json

  1. In submission, also save:

robustness/Qx/qx_robustness_report.md

  1. If the stability verdict affects method continuation or claim scope, invoke one choice card and log the human answer in qx_decisions.jsonl.

Summary Contract

Record:

  • tested claim/assumption;
  • input and result source paths;
  • perturbation;
  • metric and threshold if predeclared;
  • observed value;
  • status PASS, CONDITIONAL, or FAIL;
  • limitation;
  • fallback-trigger relevance.

Rules

  • Do not run irrelevant checks merely to reach a count.
  • Do not invent a threshold after seeing the result without labeling it exploratory.
  • Do not convert stability metrics into the human confidence verdict.
  • Do not create robustness-checker_modeler_decision.md.
  • A failed robustness check is evidence for adjust/fallback/claim downgrade, not permission for AI to decide.

Verification

  • Every major final claim has a supporting check or explicit limitation.
  • Perturbations are justified and reproducible.
  • Baseline and main comparisons remain metric-compatible.
  • Concentration/degeneracy risks are revisited when relevant.
  • Submission report sources its numbers from the summary and experiment artifacts.

Security

  • Security scanPassed, 90 of 100Pattern checks on the skill's files.
  • Model reviewPassed

Quality evaluation

  • Task evaluationNot runNo evaluation yet. Run one in Skill Studio.

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