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 | $0.00021 | $0.00245 |
| Sonnet 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 12d 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.
- 12d 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 (882 stars, last pushed 18d ago), licensed MIT. It adds 42 tokens to every session and 490 once invoked, about $0.0002 per session on Opus 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.
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