跳到正文
FunCoding

robustness-checker

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.

Star
1,057
分类
其他
许可证
MIT
最近提交
2026年9月24日

plugins/mathmodeling-skills/skills/robustness-checker/SKILL.md

在 GitHub 上查看

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.
  3. State perturbation ranges and why they are meaningful before interpreting results.
  4. Run checks with fixed seeds where stochastic.
  5. 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.

相似的 Skill

claude-handoff

MAmattpocock/skills288k近 7 天新增 10,178 个 star

agent-payment-x402

AFaffaan-m/ECC278k近 7 天新增 4,046 个 star

agent-self-evaluation

AFaffaan-m/ECC278k近 7 天新增 4,046 个 star

bengali-nlp

AFaffaan-m/ECC278k近 7 天新增 4,046 个 star

ponytail-gain

DIDietrichGebert/ponytail162k近 7 天新增 5,405 个 star

精选

ponytail-debt

扫描 shortcut 注释,汇总技术债台账,标记无升级路径的债务。

DIDietrichGebert/ponytail162k近 7 天新增 5,405 个 star