MathModeling-skills is a collection of Claude Code and Codex skills for mathematical modeling competitions, guiding users through data analysis, method-specific checks, reproducible computations, figures, and paper audits without taking over modeling decisions. It is for contestants who want AI to handle mechanical checks while they retain responsibility for choosing methods and interpreting results. The catalogue entries form the project's skills, instructions, hook, setting, and plugin workflow.
Borrowing it
Nothing to install: this file belongs to zhnnky329/MathModeling-skills. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/zhnnky329/MathModeling-skills/main/.claude/skills/robustness-checker/SKILL.mdgit clone --depth 1 https://github.com/zhnnky329/MathModeling-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/zhnnky329/mathmodeling-skills/robustness-checker)<a href="https://agentmods.dev/skills/zhnnky329/mathmodeling-skills/robustness-checker"><img src="https://agentmods.dev/badge/skills/zhnnky329/mathmodeling-skills/robustness-checker/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/zhnnky329/mathmodeling-skills/robustness-checker"><img src="https://agentmods.dev/badge/skills/zhnnky329/mathmodeling-skills/robustness-checker.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00042 | $0.00490 |
| Opus 5.5 | $0.00017 | $0.00196 |
| Sonnet 5.5 | $0.00008 | $0.00098 |
| Haiku 4.5 | $0.00004 | $0.00049 |
Grade A, and why
robustness-checker scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2mo ago First seen · 68 lines · 42 tokens per session scan A 42052bcea072
robustness-checker is a skill published in the GitHub repository zhnnky329/MathModeling-skills (1,080 stars, last pushed 1mo ago), licensed MIT. It adds 42 tokens to every session and 490 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
robustness-checker compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| robustness-checkerzhnnky329/MathModeling-skills | Skill | 1.1k | 1mo ago | MIT | A | 42 | Claude Code |
| instrument-data-to-allotropeanthropics/knowledge-work-plugins | Skill | 35k | 2d ago | Apache-2.0 | A | 123 | Claude Code |
| exploratory-data-analysisK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 83 | Claude Code |
| matlabK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 44 | Claude Code |
| phylogeneticsK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 71 | Claude Code, Codex |
| research-engineerdavila7/claude-code-templates | Skill | 33k | yesterday | MIT | A | 43 | Claude Code, Codex |
| mapping-to-snomedmaziyarpanahi/openmed | Skill | 5.8k | yesterday | Apache-2.0 | A | 205 | Claude Code |