Install Robustness Checker
Pick your agent. Every command is ready to paste.
npx skills add zhnnky329/MathModeling-skills --skill robustness-checkerThe open skills CLI detects your agents (Claude Code, Cursor, Codex and 70+ more) and installs to the right folder. Add -g to install globally.
01
Install it
Run the command above, or upload the folder in Claude.
02
Ask naturally
Describe the task. Your agent loads robustness-checker when the request matches its description.
03
Name it if needed
If it does not trigger, say “use the robustness-checker skill”. In Codex, type $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
- Identify load-bearing assumptions and claims.
- 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.
- State perturbation ranges and why they are meaningful before interpreting results.
- Run checks with fixed seeds where stochastic.
- Save compact metrics to:
robustness/Qx/qx_robustness_summary.json
- In
submission, also save:
robustness/Qx/qx_robustness_report.md
- 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, orFAIL; - 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.
Tips for Robustness Checker
- • Start a fresh session after installing so your agent picks up the new skill.
- • It is part of a pack with 29 skills. Install only the ones you need with --skill to keep triggers precise.
- • If two skills overlap, mention the one you want by name.
Questions about Robustness Checker
What does the Robustness Checker skill do?
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.
How do I install Robustness Checker?
Run "npx skills add zhnnky329/MathModeling-skills --skill robustness-checker" in your project. The skills CLI installs it for Claude Code, Cursor, Codex and 70+ other agents. In Claude on the web, upload the skill folder as a ZIP under Customize > Skills.
Does Robustness Checker work in Cursor and Codex?
Yes. Robustness Checker uses the open SKILL.md format, so it works in Claude Code, Claude, Cursor, OpenAI Codex, Gemini CLI, GitHub Copilot and other agents that support agent skills.
Is Robustness Checker free?
Robustness Checker is open source on GitHub under the MIT license. It is free to install; check the license before commercial use.
Who made Robustness Checker?
Robustness Checker is published by zhnnky329 in the zhnnky329/MathModeling-skills repository, together with 28 other skills.




