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/problem-doc-model-selectornpx skills add yushui2022/MathModel-Skill --skill problem-doc-model-selectorgit 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/problem-doc-model-selector)<a href="https://agentmods.dev/skills/yushui2022/mathmodel-skill/problem-doc-model-selector"><img src="https://agentmods.dev/badge/skills/yushui2022/mathmodel-skill/problem-doc-model-selector.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 | $0.00048 | $0.03026 |
| Opus 5 | $0.00024 | $0.01513 |
| Sonnet 5 | $0.00010 | $0.00605 |
| Haiku 4.5 | $0.00005 | $0.00303 |
Grade A, and why
problem-doc-model-selector 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 today.
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 — 148 lines — stays where its author put it; the contents beside it link to each section on GitHub.
赛题文档自动解析与模型选型
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 problem-doc-model-selector - 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。
执行契约
- 上游输入：
problem_files/中的赛题 PDF/Word/TXT 和附件数据。 - 必须输出：
paper_output/step1/problem_analysis.json，以及A_题意对齐.md、B_论文大纲.md、C_评分点对齐表.md、D_模型路线.json。 - 下游交接：
modeling-paper-rubric-and-model-selector读取problem_analysis.json生成模型路线；完整 workflow 由paper-workflow-orchestrator串联。 - 推荐下一步：完成题意分析后进入
modeling-paper-rubric-and-model-selector；如果用户目标是完整论文，回到paper-workflow-orchestrator判断后续阶段。 - 失败回退：若
problem_files/为空，应停止并提示补齐赛题；若部分文档无法解析，保留可解析内容并在输出中记录字段画像缺失。
目标
- 输入赛题 PDF/Word 文档或题面文本，自动抽取“每一问”的任务类型、数据条件与约束，并输出贴题的模型选型与验证路线。
- 生成评分友好型交付：一页纸题意对齐、论文大纲、评分点对齐表、模型路线（基线/改进/验证/风险）。
阶段流转
- 解析完成后，不要停留在“是否满意”的泛泛询问；应说明已生成的题意契约，并给出下一推荐阶段。
- 若用户意图是完整论文，在输出 A/B/C/D 后回到
paper-workflow-orchestrator，由总入口决定继续生成模型路线、数据计划、QA 和正文。 - 拒绝偷懒: 必须输出完整的 A/B/C/D 四部分，不得省略。
What ships with it
1 file 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.
- today Changed 8f79d0815529
- 4d ago First seen · 148 lines · 48 tokens per session scan A 9d9ddeafa711
problem-doc-model-selector is a skill published in the GitHub repository yushui2022/MathModel-Skill (340 stars, last pushed yesterday), licensed MIT. It adds 48 tokens to every session and 3,026 once invoked, about $0.0002 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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