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。
Assemble a submission-ready writer package from final method, result, robustness, figure, and human-decision artifacts, then freeze approved numerical claims with provenance.
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。
Create the writer's single source package and immutable numerical snapshot. Do not manufacture missing judgments or freeze unapproved claims.
rigor_profile is submission.qx_decisions.jsonl.results/Qx/reports/qx_solution_package_for_writer.md.modeler-decision-logger as package_signoff.results/Qx/reports/frozen_numbers.json.Each entry contains:
{
"claim_id": "q1_main_rmse",
"value": 2.4,
"unit": "units",
"source_file": "results/Q1/experiments/final/metrics/main.json",
"source_locator": "$.rmse",
"frozen_at": "ISO-8601",
"frozen_by_skill": "solution-package-builder",
"decision_id": "q1_package_signoff"
}
Use a source line only for stable text files; use a JSON path, table key, or row/column identifier for structured data.
solution-package-builder_modeler_decision.md.frozen_numbers.json before human package sign-off.When a canonical source changes after freeze:
freeze_change_log.md;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。