Query 2021-2025 CUMCM school awards and 2026 forecasts, or assess a student's preparation distance from provincial and national awards using school history and prior modeling experience.
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。
Use the bundled workbook as the only source for school counts. Two modes: 学校画像 and 备赛进度评估.
Run python scripts/query_school.py --school "学校名称" --region "赛区".
ok: output 2021-2025 first/second prizes, totals, 2026 forecast, frequent advisor and appearances.ambiguous: show candidates and ask for confirmation.not_found: output: “非常遗憾，按当前榜单口径，您所在的高校过去5年没有获得国奖成绩。对于这类学校，本年度可能获得国奖的经验概率为6.81%。” State that 6.81% is an owner-supplied school-level heuristic, not an individual probability or official result.First query the school. Then ask only for missing information: 是否参加过其他数模竞赛、次数和名称、是否获奖及等级、完整模拟/论文次数、目标奖项. Output 学校基础、个人准备度、目标差距、下一步行动.
$bzd-review-paper when available to review the previous paper, then complete 1-2 simulations.Preserve workbook values. Historical advisor frequency is correlation, not quota control. Do not use “预定、分配、保证”. Data ends in 2025 and 2026 is forecast. End every assessment with: “这是一项基于学校历史与个人经历的经验评估，不是官方概率，也不能承诺具体奖项。”
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概要と使いどころ
Review a user-provided Chinese mathematical-modeling abstract without requiring the problem statement or full paper, identify concrete writing defects, and prioritize revisions. Use when the user asks to check, diagnose, or improve a modeling-paper abstract; do not claim coverage or factual consistency that cannot be verified without the problem or paper.
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 or review mathematical-modeling competition AI tool usage statements and the anonymous AI工具使用详情.pdf. Use when users provide real AI usage records, or when checking a paper, statement, details PDF, code, and AI records for completeness, anonymity, and consistency.
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。
Review a mathematical-modeling paper's model-assumption section against the complete problem statement. Diagnose whether each assumption is necessary, evidence-based, compatible with the task, appropriately simplified, and usable by the later model. Use for assumption review, not automatic full-paper review or unsupported assumption generation.
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。
Query the bundled BZD mathematical-modeling dictionary for a user-selected model, explain its assumptions, inputs, outputs, limitations and validation, then judge whether it fits the current competition problem and data. Use when a user asks whether a proposed model is appropriate, requests model feasibility review, or wants dictionary-based alternatives for a modeling task.
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。
Review the model establishment, numerical solution, result analysis, model validation, and sensitivity or robustness sections of a mathematical-modeling paper against the complete problem. Use for core-model-section diagnosis, not automatic full-paper scoring or unsupported recomputation.
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 a coherent, whole-paper mathematical modeling solution framework from a complete contest problem. Use after the user supplies a CUMCM or other modeling problem and asks for modeling ideas, an overall solution plan, question-by-question analysis, candidate-model comparison, model-selection reasons, innovations, validation, or cross-question linkage. For every question, explain the task and mathematical essence, compare multiple feasible models in tables, recommend a route with explicit reasons, and keep all questions connected through shared data, variables, parameters, constraints, and validation.
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。