定位并获取权威公开数据,优先使用官方 API 与批量下载,产出可复现的抓取代码、来源与许可说明、校验记录。当赛题未提供附件、需要用人口或经济或气象或交通等官方统计补齐数据,或用户要求从权威来源取数时使用。
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编排完整的数学建模工作流,从赛题与附件出发依次完成输入预检、题意分析、模型路线、数据与图表、代码与真实运行、证据门禁、正式写作与 Word/PDF 检查。当用户要做完整篇数学建模论文、需要从零跑通或从中断处续跑整个流程时使用。
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
Resolve <SKILL_ROOT> to the parent directory of this loaded skill folder, using its actual absolute path. Run commands from the contest project root, never from the installed skill directory. The placeholder is not a shell environment variable.
Use this skill as the only entry router for a complete competition-paper task. Standard targets strong models that can maintain a long evidence chain and execute tools reliably while keeping cost controlled. It does not use Pro multi-agent tournaments or approval checkpoints.
From the contest project root, run:
python "<SKILL_ROOT>/paper-workflow-orchestrator/scripts/preflight_check.py"
python "<SKILL_ROOT>/paper-workflow-orchestrator/scripts/workflow_guard.py" --status
Read paper_output/qa/workflow_guard_report.json and follow recommended_skill plus next_action. The current files and hashes override conversational memory.
Do not run downstream skills before their guard requirement passes. After a child skill finishes, return here and evaluate status again.
preflight_check.py inventories problem_files/, hashes every input, checks runtime dependencies, prepares paper_output/, and rejects mixed MathModel editions. Required outputs:
paper_output/preflight_report.jsonpaper_output/input_manifest.jsonpaper_output/OUTPUT_LAYOUT.mdUse problem-doc-model-selector to create paper_output/step1/problem_analysis.json. Every question, attachment, field, objective, constraint, ambiguity, and required output must be traceable.
Use modeling-paper-rubric-and-model-selector. Produce:
paper_output/plan/model_route.jsonpaper_output/plan/rubric_alignment.jsonpaper_output/plan/scoring_strategy.mdUse authoritative-data-harvester only when public external data is necessary. Keep source identity and retrieval notes.
Use data-cleaning-and-visualization. Read only files classified in the input manifest and produce a fresh load report, data plan, visualization plan, figure index, and cleaned data. Contest-specific code belongs under paper_output/code/, never inside installed skills.
Use model-code-and-result-generator to write question-specific code under paper_output/code/modeling/, including run_modeling.py and per-question modules. Code must emit machine-readable result contracts.
Run the modeling code. Preserve script, input, output, exit-code, size, and SHA-256 records in paper_output/results/run_manifest.json. Required evidence includes model results, finite metrics, conclusions, tables, and usable figures. Draft placeholders do not count.
Use quality-assurance-auditor and run official evidence validation:
python "<SKILL_ROOT>/quality-assurance-auditor/scripts/evidence_gate.py" --mode official
Do not enter formal writing until paper_output/qa/evidence_gate_report.json is PASS and all recorded inputs are still fresh.
Use paper-formal-writer as the sole formal author:
python "<SKILL_ROOT>/paper-formal-writer/scripts/build_paper_outline.py"
python "<SKILL_ROOT>/paper-formal-writer/scripts/prepare_authoring.py" --mode auto
Normal competition papers use complete-section drafting, possibly over several turns. Preserve the formal writer's declared competition scope; a short report needs an explicit user-requested scope and reason. Audit every draft with validate_authoring.py --section; global repeated failure falls back to section mode. A section’s second repeated category creates a micro-repair route; only then may paper-micro-unit-generator repair the queued location. The third repeated category blocks S7 and suggests Lite without switching automatically.
After every active unit passes:
python "<SKILL_ROOT>/paper-formal-writer/scripts/assemble_sections.py"
python "<SKILL_ROOT>/paper-formal-writer/scripts/validate_authoring.py" --assembled
The Agent must then globally revise the full assembly into paper_output/final_paper_source.md; a copy-only promotion is rejected. Finish with:
python "<SKILL_ROOT>/paper-formal-writer/scripts/validate_authoring.py" --final
python "<SKILL_ROOT>/paper-formal-writer/scripts/format_formal_docx.py"
Run:
python "<SKILL_ROOT>/paper-formal-writer/scripts/check_paper_format.py" --render required
Delivery requires a fresh PASS in paper_output/format_check_report.json. Fix the reported source, formula, citation, figure/table, DOCX, pagination, or PDF issue and rerun; never edit the report to force PASS.
paper_output/plan/writing_plan.json
paper_output/context/authoring_state.json
paper_output/qa/draft_audit.json
paper_output/qa/repair_queue.json
paper_output/drafts/sections/*.md
paper_output/drafts/assembled_draft.md
paper_output/final_paper_source.md
paper_output/final_paper.docx
paper_output/format_check_report.json
Legacy micro-unit and quickstart outputs remain under paper_output/drafts/legacy/ and paper_output/quickstart/. They can never satisfy S7.
For an interrupted or long task:
python "<SKILL_ROOT>/paper-workflow-orchestrator/scripts/workflow_guard.py" --status
python "<SKILL_ROOT>/context-memory-keeper/scripts/update_workflow_memory.py"
Read the guard report, current stage contracts, and paper_output/context/workflow_memory.json; continue from the first failing stage instead of replaying completed work.
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定位并获取权威公开数据,优先使用官方 API 与批量下载,产出可复现的抓取代码、来源与许可说明、校验记录。当赛题未提供附件、需要用人口或经济或气象或交通等官方统计补齐数据,或用户要求从权威来源取数时使用。
中文简介正在准备中。当前显示原文说明。
维护跨会话的项目记忆,记录长期规则与当前任务清单,供后续阶段恢复上下文。当长对话需要压缩、已确认的决策与约束需要落盘、或用户要求记住或恢复之前进展时使用。
中文简介正在准备中。当前显示原文说明。
清洗原始数据并生成论文级图表,处理缺失值、异常值、单位与格式不一致,产出加载报告、数据计划、图表索引、可复现的图文件与图注。当用户提供 csv 或 xlsx 原始附件需要清洗、需要为某一问画图或补图注、或数据与可视化计划待执行时使用。
中文简介正在准备中。当前显示原文说明。
数学建模全流程技能包:赛题与附件预检、题意与模型路线、数据与图表计划、真实运行建模代码、证据门禁、分章节正式写作与 Word/PDF 渲染检查,产出可复现的竞赛论文和全套证据。当用户要做完整篇数学建模竞赛论文时使用。
中文简介正在准备中。当前显示原文说明。
按已确定的模型路线为每一问生成可运行代码,并沉淀结果证据契约、评价指标、结构化结论与论文表格。当模型路线已定、需要编写并真实运行各问求解代码、或运行结果需要可核验的契约记录时使用。
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按评分点组织论文结构并给出模型路线,产出论文大纲、评分点对齐表与候选模型对照实验方案。当需要在多个候选模型间取舍、需要把评分要求映射成章节结构、或模型路线尚未确定时使用。赛题原文解析由 problem-doc-model-selector 负责。
中文简介正在准备中。当前显示原文说明。