Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/yushui2022/mathmodel-skill/model-code-and-result-generatornpx skills add yushui2022/MathModel-Skill --skill model-code-and-result-generatorgit clone --depth 1 https://github.com/yushui2022/MathModel-SkillWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/yushui2022/mathmodel-skill/model-code-and-result-generator)<a href="https://agentmods.dev/skills/yushui2022/mathmodel-skill/model-code-and-result-generator"><img src="https://agentmods.dev/badge/skills/yushui2022/mathmodel-skill/model-code-and-result-generator.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00099 | $0.02269 |
| Opus 5 | $0.00049 | $0.01135 |
| Sonnet 5 | $0.00020 | $0.00454 |
| Haiku 4.5 | $0.00010 | $0.00227 |
Grade A, and why
model-code-and-result-generator scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 122 lines — stays where its author put it; the contents beside it link to each section on GitHub.
建模代码与结果证据生成器
全局流程协作约束（长对话防漂移）
- 本 skill 不得作为孤立入口。用户要求完整论文、生成 Word、继续流程或不确定阶段时，先回到
paper-workflow-orchestrator判断当前 S0-S8 阶段。 - 启动或继续本 skill 的正式任务前，必须运行：
python .claude/skills/paper-workflow-orchestrator/scripts/workflow_guard.py --skill model-code-and-result-generator - 如果输出
[WORKFLOW FAIL]或报告status != "PASS"，停止本 skill，按paper_output/qa/workflow_guard_report.json的失败项回补前置阶段，不得凭记忆继续。 - 本 skill 只写入自己契约范围内的
paper_output/产物；完成后必须回到paper-workflow-orchestrator判断下一步，并用context-memory-keeper记录已完成产物、阻塞项和下一步。 - 长对话中如果上下文变长、阶段不确定或用户分开调用 skill，先运行：
再读取python .claude/skills/paper-workflow-orchestrator/scripts/workflow_guard.py --statuspaper_output/qa/workflow_guard_report.json、paper_output/preflight_report.json、paper_output/input_manifest.json、paper_output/results/run_manifest.json和本 skill 的上游 JSON 契约，按报告里的recommended_skill与next_action继续。 - 继续流程前，必须把
paper_output/context/workflow_memory.json视为长期断点记录；若其中的current_step、next_step、recommended_skill与workflow_guard.py --status不一致，以 guard 报告为准。 - 每次完成本 skill 的产物后，先回到
paper-workflow-orchestrator或运行workflow_guard.py --status，再更新 workflow memory：
更新后读取python .claude/skills/context-memory-keeper/scripts/update_workflow_memory.pypaper_output/context/workflow_memory.json/.md，确认下一步和推荐 skill 已记录。
目标
本 skill 不是万能自动建模系统。它的作用是给 Agent 一个稳定的“结果证据层”和可运行的赛题专用建模代码起点，避免正文只根据模型路线空写，也避免 Agent 面对数据时无头乱转。
真实赛题中，Agent 必须根据 model_route.json、数据字段、题目约束和评分要求二次修改生成的 q*_model.py。生成代码固定放在 paper_output/code/modeling/，不要写回 skill 包的 scripts/。
执行契约
- 上游输入：优先读取
paper_output/plan/model_route.json、data_plan.json、visualization_plan.json，并扫描paper_output/data_cleaned/。 - 必须输出：
paper_output/results/model_results.json、metrics.json、conclusions.json、run_manifest.json、paper_output/tables/table_index.json、paper_output/tables/*.csv。 - 建模代码输出：
paper_output/code/modeling/result_contract_io.py、run_modeling.py、q1_model.py、q2_model.py、q3_model.py或与question_id对应的q*_model.py。 - 下游交接：
quality-assurance-auditor直接审计结果、指标、表格、图表和结论；证据门禁 PASS 后由paper-formal-writer构建正式写作计划。 - 失败回退：如果没有清洗数据或真实建模代码，仍生成契约骨架，并用
needs_real_modeling标记，不伪装成最终比赛结果。
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday Changed 42860026c14b
- 5d ago First seen · 122 lines · 99 tokens per session scan A 254f450daa2f
model-code-and-result-generator is a skill published in the GitHub repository yushui2022/MathModel-Skill (350 stars, last pushed today), licensed MIT. It adds 99 tokens to every session and 2,269 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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