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.
日本語の概要は準備中です。原文の説明を表示しています。
Generate and run minimal reproducible MATLAB or Beita Tianyuan compatible code for the human-approved main method and usable baseline, with compact experiment artifacts and a canonical run summary.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
code/matlab/Qx/qx_code_plan.md exists.Legacy artifacts may be read during migration but do not override the human decision.
.m files under code/matlab/Qx/.run_summary.json under results/Qx/experiments/roundN/.diary or another full log only for a failure or reproducibility warning.code-reviewer.code/matlab/Qx/
├── qx_code_plan.md
├── qx_baseline.m
├── qx_main.m
└── run_all.m % only when useful
Do not create scripts for unapproved candidates or a duplicate README.
readtable, readmatrix, writetable, writematrix, save, load, and fullfile.rng(2026) or the recorded seed.jsonencode when supported; otherwise write the required JSON fields deterministically.Follow the model-code-analyzer contract, including approved decision ID, roles, paths, metrics, output-degeneracy evidence, fallback state, timing, seed, environment, warnings, and errors.
code-reviewer.まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
Plan the smallest set of diagnostic, comparison, paper, and appendix figures or tables needed to support verified mathematical-modeling decisions and claims.
日本語の概要は準備中です。原文の説明を表示しています。