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。
Run or resume the complete Standard mathematical-modeling workflow from contest files through reproducible evidence, adaptive formal writing, native-equation Word output, and final render QA. Use as the entry point for complete-paper requests.
$ npx -y skills add yushui2022/MathModel-Skill --skill paper-workflow-orchestrator --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/paper-workflow-orchestratorContext preview
The summary Claude sees to decide when to auto-load this skill.
Run or resume the complete Standard mathematical-modeling workflow from contest files through reproducible evidence, adaptive formal writing, native-equation Word output, and final render QA. Use as the entry point for complete-paper requests.
name: paper-workflow-orchestrator description: Run or resume the complete Standard mathematical-modeling workflow from contest files through reproducible evidence, adaptive formal writing, native-equation Word output, and final render QA. Use as the entry point for complete-paper requests.
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 .claude/skills/paper-workflow-orchestrator/scripts/preflight_check.py python .claude/skills/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:
Use `$problem-doc-model-selector` to create `paper_output/step1/problem_analysis.json`. Every question, attachment, field, objective, constraint, ambiguity, and required output must be traceable.
Use `$modeling-paper-rubric-and-model-selector`. Produce:
Use `$authoritative-data-harvester` only when public external data is necessary. Keep source identity and retrieval notes.
Use `$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 .claude/skills/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 `$paper-formal-writer` as the sole formal author:
python .claude/skills/paper-formal-writer/scripts/build_paper_outline.py python .claude/skills/paper-formal-writer/scripts/prepare_authoring.py --mode auto
Normal competition papers use complete-section drafting, possibly over several turns. Preserve the formal writer's declared competition scope; a short report needs an explicit user-requested scope and reason. Audit every draft with `validate_authoring.py --section`; global repeated failure falls back to section mode. A section’s second repeated category creates a `micro-repair` route; only then may `$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 .claude/skills/paper-formal-writer/scripts/assemble_sections.py python .claude/skills/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 .claude/skills/paper-formal-writer/scripts/validate_authoring.py --final python .claude/skills/paper-formal-writer/scripts/format_formal_docx.py
Run:
python .claude/skills/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.
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。
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Plan, draft, audit, globally revise, format, and verify a formal mathematical-modeling paper after the Standard evidence gate…