authoritative-data-har…
自动定位并获取权威公开数据(优先API/官方批量下载),输出可复现抓取与清洗方案。Invoke when用户需要权威数据、官方统计、API下载或数据源爬取。
Plan, draft, audit, globally revise, format, and verify a formal mathematical-modeling paper after the Standard evidence gate passes. Use for the sole S7/S8 formal manuscript path, not for legacy micro-unit scaffolds.
$ npx -y skills add yushui2022/MathModel-Skill --skill paper-formal-writer --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/paper-formal-writerContext preview
The summary Claude sees to decide when to auto-load this skill.
Plan, draft, audit, globally revise, format, and verify a formal mathematical-modeling paper after the Standard evidence gate passes. Use for the sole S7/S8 formal manuscript path, not for legacy micro-unit scaffolds.
name: paper-formal-writer description: Plan, draft, audit, globally revise, format, and verify a formal mathematical-modeling paper after the Standard evidence gate passes. Use for the sole S7/S8 formal manuscript path, not for legacy micro-unit scaffolds.
This skill is the sole formal author and final manuscript producer in Standard. It turns a fresh S6 evidence chain into an audited Markdown source and native-equation Word document.
Run before formal work:
python .claude/skills/paper-workflow-orchestrator/scripts/workflow_guard.py --skill paper-formal-writer
S0-S6 must pass. If workflow state is uncertain, run `workflow_guard.py --status` and follow its current artifact report rather than memory.
S6 evidence PASS -> writing plan -> chapter drafts -> chapter audits -> queued local repair when required -> deterministic assembly -> Agent global revision -> final Markdown audit -> formal DOCX -> S8 format/render gate
Do not promote quickstart, legacy, or mechanically merged micro-unit output into a formal filename.
1. Build the evidence-aware outline:
python .claude/skills/paper-formal-writer/scripts/build_paper_outline.py
2. Prepare adaptive authoring:
python .claude/skills/paper-formal-writer/scripts/prepare_authoring.py --mode auto
Default delivery is `competition`: plan at least 14000 effective characters, with a main-paper floor of 8000 and at least 18 rendered pages before appendices. These are adjustable project defaults, not contest rules. Verify the actual contest's page cap and counting rules before writing. `auto` therefore selects `section`; a short report needs `--delivery short-report --scope-reason "user-requested scope"`. Custom floors use `--min-pages` / `--min-body-chars` with a reason; never lower them merely to turn a failed check green.
Read [references/competition-scope.md](references/competition-scope.md) when preparing scope or repairing a short manuscript. Several authoring turns may produce one coherent chapter; preserve evidence and notation between turns instead of compressing the paper into one response.
The authoritative S7 contracts are:
For numerical results, read [references/metric-claims.md](references/metric-claims.md). Formal preparation binds each required metric to its unit, sample/split, CSV row and successful experiment. Missing metadata blocks S7 instead of being guessed from prose. Maintain a sibling `.claims.json` for each draft and the final source; use an empty claims list for chapters without numbers. Only declared conversions are allowed. Never promote a feasible solution to an optimal one without recorded verification.
Write only to the path recorded for the current unit in `authoring_state.json`. Each chapter must be a complete, coherent section and must include its required removable marker:
<!-- mathmodel-evidence: evidence-id-1, evidence-id-2 -->
Audit every changed draft:
python .claude/skills/paper-formal-writer/scripts/validate_authoring.py --section <section-id>
Routing is deterministic:
Audits block short drafts, missing chapters/evidence/numbers, broken formulas, placeholders, duplicate prose, broken figure/table references, and internal workflow language.
After all active units pass:
python .claude/skills/paper-formal-writer/scripts/assemble_sections.py python .claude/skills/paper-formal-writer/scripts/validate_authoring.py --assembled
The assembler follows outline order, strips evidence comments, merges claim sidecars, and preserves existing numbering. It writes `paper_output/drafts/assembled_draft.md`.
Then read the entire assembled draft and evidence chain, and rewrite the paper globally into:
paper_output/final_paper_source.md
This pass must unify terminology, notation, transitions, argument order, citations, captions, and conclusions. Copying the assembled draft unchanged is rejected. Update `final_paper_source.claims.json` with the revised quotes and exact source hash; do not copy stale bindings. Sidecars remain outside the rendered manuscript.
Run the final source audit:
python .claude/skills/paper-formal-writer/scripts/validate_authoring.py --final
Any change to evidence, writing plan, approved section draft, assembly, or final source invalidates downstream PASS state.
Only after `authoring_state.status = PASS`:
python .claude/skills/paper-formal-writer/scripts/format_formal_docx.py python .claude/skills/paper-formal-writer/scripts/check_paper_format.py --render required
Formal mode requires fresh S6 and S7 hashes. `--allow-draft` may create `final_paper_draft.docx` for layout diagnosis, but never satisfies S7/S8 or overwrites the formal DOCX.
`format_formal_docx.py` converts LaTeX to editable Word OMML and blocks failed conversion. `check_paper_format.py` checks dynamic length, required hierarchy, citations, evidence-linked figures/tables, formulas, duplicate prose, DOCX structure, LibreOffice PDF rendering, page count, and extractable text.
Agent-native mathematical modeling workflow skills for Trae, Claude Code, and Codex, covering problem parsing, modeling, code generation, evidence checks, paper writing, and Word delivery.
自动定位并获取权威公开数据(优先API/官方批量下载),输出可复现抓取与清洗方案。Invoke when用户需要权威数据、官方统计、API下载或数据源爬取。
Manages persistent memory. Invoke to read active context or archive old tasks. Structure: Long-term Principles (Rules) +…
自动清洗赛题或爬取的数据(处理缺失/异常/格式),并生成可视化图表。Invoke when 用户需要处理原始数据、清洗数据或生成数据分析图表。
根据 model_route.json、数据计划和清洗数据,为数学建模论文生成结果证据契约和 q1/q2/q3 建模代码脚手架。Invoke when 需要把模型输出、评价指标、结构化结论、论文表格和当前赛题专用建模代码沉淀到…
按常见评分点生成建模论文结构与写作清单,并根据题目类型与数据条件给出模型选择与对照实验路线。Invoke when需要“论文格式/评分对齐/模型选型/路线不确定”。
Repair a repeatedly failing Standard paper section from repair_queue.json, or generate an explicitly requested legacy/quickstart…