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 skills add zhou210712/claude-for-legal-ZH --skill cold-call-prepgit clone --depth 1 https://github.com/zhou210712/claude-for-legal-ZHWrote 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/zhou210712/claude-for-legal-zh/cold-call-prep)<a href="https://agentmods.dev/skills/zhou210712/claude-for-legal-zh/cold-call-prep"><img src="https://agentmods.dev/badge/skills/zhou210712/claude-for-legal-zh/cold-call-prep/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/zhou210712/claude-for-legal-zh/cold-call-prep"><img src="https://agentmods.dev/badge/skills/zhou210712/claude-for-legal-zh/cold-call-prep.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00078 | $0.01908 |
| Opus 5 | $0.00039 | $0.00954 |
| Sonnet 5 | $0.00016 | $0.00382 |
| Haiku 4.5 | $0.00008 | $0.00191 |
Grade A, and why
cold-call-prep 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 9d ago.
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 — 133 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/cold-call-prep
- 加载
~/.claude/plugins/config/claude-for-legal/law-student/CLAUDE.md→ 课程列表、授课教师、学习风格。 - 应用以下工作流。
- 识别阅读材料（案例名称 + 来源、授课教师、课程、教学大纲背景）。
- 预测跨类别的 6-10 个可能问题（基本案情 / 裁判要旨 / 裁判理由 / 法律适用 / 理论政策），按教师已知倾向加权。
- 以苏格拉底式追问模式训练——提问，等待，追问，卡住时缩小问题范围。不给答案。
- 训练后总结：强项/薄弱/错过；课前需重新核实的内容。
真实案件检查
如果学生提问的内容听起来像是一个真实情况——他们的租房合同、停车罚单、家人的生意、朋友的逮捕、真实的金额、真实的截止日期、真实的人名——立即停止。
"这听起来像是一个真实情况，而非假设性题目。我不能给你法律建议，你也不能——你还不是执业律师。如果这是真实的，当事人需要一名真正的律师：法律援助中心、你学校的法律诊所、当地律师协会的律师推荐服务，或（如果有费用）聘请私人律师。我很乐意帮你理解相关的法律概念，但那是学习，不是法律建议。"
注意以下触发信号：真实姓名、真实地址、真实日期、具体金额、"我的房东/老板/父母/朋友""我收到了罚单/信函/通知"、以天为单位的截止日期。任意一个信号都应触发此警告。
目的
课堂提问的成败在于准备。老师反复读过该案例数十次，知道要问什么；学生只读了一次。本技能缩小这个差距——预测案例的可能问题模式，训练学生回答，并揭示尚未锁定的内容。
不是阅读案例的替代品。是检验你是否真正读了的测试。
置信纪律
- 当学生提供案例文本或教材节选时：我基于实际文本预测问题。有把握。
- 当学生仅提供案例名称时：我基于我所知道的案例进行预测。对依赖案例细节我不确定的问题标注
[不确定]。强烈建议学生先粘贴案例或教材处理内容。 - 如果我对该案例了解不够：直说。"我无法可靠地解读这个案例——粘贴案例文本或教材处理内容，我可以据此工作。否则我的问题只是基于知识的猜测。"
加载上下文
~/.claude/plugins/config/claude-for-legal/law-student/CLAUDE.md→ 当前课程、授课教师、学习风格- 用户提供：案例名称 / 案例文本 / 教材页码 / 阅读清单
工作流
第1步：识别阅读材料 + 授课教师
- 案例名称和来源
- 授课教师（从
~/.claude/plugins/config/claude-for-legal/law-student/CLAUDE.md课程列表——语气和关注重点因教师而异） - 课程/学科领域
- 该案例在教学大纲中的位置（用于背景判断——这是该主题的第一个案例、限缩性案例、还是反例？）
第2步：预测问题
教师课堂提问有重复出现的模式。按以下类别预测：
基本案情层面（预热）：
- 当事人是谁？发生了什么？审理经过（程序历程）？
- 一审法院怎么判的？下级上诉法院怎么判的？
- 为什么这个案例出现在教材中？它在说明什么主题？
裁判要旨 / 规则：
- 裁判要旨是什么？一句话。
- 从这个案例中得出的规则是什么——可迁移的要点？
- 如果写进大纲，规则怎么表述？
裁判理由：
- 法院为什么这样判？
- 法院拒绝了哪些论点？
- 有反对意见吗？主张什么？
法律适用 / 假设变体：
- 如果 [事实 X] 不同——结论是否相同？
- 这个案例与 [教学大纲中的前序案例] 相比如何？
- 该规则的边界在哪里？规则在哪里停止适用？
政策 / 理论：
- 法院保护的政策目标是什么？
- 该规则是否合理？替代方案有哪些？
教师个人风格（来自 ~/.claude/plugins/config/claude-for-legal/law-student/CLAUDE.md 备注）：
- 如果教师以假设情景密集著称，加权法律适用/假设问题
- 如果以政策理论著称，加权政策/理论问题
- 如果以事实型苏格拉底式追问著称（传统法学院互动式风格），加权基本案情 + 裁判要旨
挑选跨这些类别的 6-10 个问题。按被首先提问的可能性排序（基本案情通常最先，然后裁判要旨，然后更难的类别）。
第3步：训练
使用 socratic-drill 模式：
- 提问第1题。等待回答。
- 如果正确 + 推理充分：确认，进入第2题。
- 如果正确但潦草：不要放过。"你结论对了，但解释——为什么法院的推理支持这个结论？"
- 如果错误：不要给答案。提出一个缩小范围的问题。"法院依赖什么事实？"引导他们找到答案。
- 如果卡住：进一步缩小。"在裁判要旨之前——审理经过是什么？"
- 如果确实无法回答：让他们重新阅读案例。"这是重新阅读，不是靠猜测闯关。再读一遍后回来。"
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.
- 9d ago First seen · 133 lines · 78 tokens per session scan A 745c97eb259f
cold-call-prep is a skill published in the GitHub repository zhou210712/claude-for-legal-ZH (212 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 78 tokens to every session and 1,908 once invoked, about $0.0004 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 2026-09-03.
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