Detect whether approved modeling code is Python or MATLAB/Beita Tianyuan and route it to the matching reviewer using the compact named-check review contract.
中文简介正在准备中。当前显示原文说明。
Audit whether the semantic evidence required by the active lean or submission profile exists and is current, without requiring one verbose artifact per skill or an arbitrary number of pass bullets.
中文简介正在准备中。当前显示原文说明。
查看安装方法安装前可以查看交给 AI 代理的指令内容。
Check deliverable evidence, not whether every invoked skill left a ceremonial report.
Per active Qx, require as applicable:
Lean completeness does not require final reports, freeze, paper, or G6 artifacts.
Per Qx require:
Globally require symbol table, assumptions, references, consistency audit, completeness audit, and QA report.
rigor_profile.paper/audits/completeness_audit.md only in submission/final mode.PRESENT: required evidence exists and is current.MISSING: required evidence absent.INSUFFICIENT: artifact exists but required semantic fields/checks are missing.STALE: cited material evidence changed after approval/review.NOT_APPLICABLE: justified by task/profile.≥5 prose pass items.PASSED only when all submission requirements are satisfied.还没有评价。欢迎分享你的使用感受。
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Detect whether approved modeling code is Python or MATLAB/Beita Tianyuan and route it to the matching reviewer using the compact named-check review contract.
中文简介正在准备中。当前显示原文说明。
Run scoped or final cross-media consistency checks for mathematical-modeling artifacts, comparing canonical numbers, symbols, parameters, decisions, files, and paper claims without performing full-workspace audits for low-risk changes.
中文简介正在准备中。当前显示原文说明。
Map contest attachments to subquestions, audit and clean raw data, and emit one reusable data profile with quality, coverage, imbalance, concentration, and method-readiness evidence for downstream risk screening.
中文简介正在准备中。当前显示原文说明。
Build one compact choice card at a genuine mathematical-modeling judgment point. Use before method screening, after a meaningful experiment, or before final claim/freeze approval so the human chooses the trade-off while AI handles mechanical consequences.
中文简介正在准备中。当前显示原文说明。
Plan the smallest set of diagnostic, comparison, paper, and appendix figures or tables needed to support verified mathematical-modeling decisions and claims.
中文简介正在准备中。当前显示原文说明。
Build the authoritative final method explanation for a submission-ready subquestion from the method card, human decision ledger, code plan, final results, and robustness evidence.
中文简介正在准备中。当前显示原文说明。