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 chronologygit 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/chronology)<a href="https://agentmods.dev/skills/zhou210712/claude-for-legal-zh/chronology"><img src="https://agentmods.dev/badge/skills/zhou210712/claude-for-legal-zh/chronology/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/chronology"><img src="https://agentmods.dev/badge/skills/zhou210712/claude-for-legal-zh/chronology.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 | $0.00093 | $0.01864 |
| Opus 5 | $0.00046 | $0.00932 |
| Sonnet 5 | $0.00019 | $0.00373 |
| Haiku 4.5 | $0.00009 | $0.00186 |
Grade A, and why
chronology 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.
Copies of this mod
3 near-identical copies found in the catalogue:
- matter-update — 86% identical, 190 lines differ
- matter-briefing — 84% identical, 167 lines differ
- matter-intake — 81% identical, 319 lines differ
How it starts
The opening of the file, as written. The whole thing — 154 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/chronology
- 加载
~/.claude/plugins/config/claude-for-legal/litigation-legal/matters/[slug]/matter.md→ 案件理论、关键事实。 - 加载
~/.claude/plugins/config/claude-for-legal/litigation-legal/CLAUDE.md→ 文件存储来源、默认案件文件夹模式。 - 按以下工作流。
- 按顺序识别来源：本次会话用户提供的路径、默认案件文件夹、配置中声明的来源。
- 对于可读来源：提取带日期的事件。对于不可达来源：在缺口中注明。
- 去重，合并每个事件的来源列表。
- 按案件理论标记重要性（🔴/🟡/⚪）。
- 写入
~/.claude/plugins/config/claude-for-legal/litigation-legal/matters/[slug]/chronology.md。 - 如先行版本存在：版本号递增，向用户呈现diff摘要。
- 最终确定前确认："这是我构建的内容。浏览🔴条目——有无我判定错误的地方？"
大事记（Chronology）
目的
事实按顺序发生。大事记是每个叙事依赖的骨架——代理词的事实部分、法律意见、庭前准备。手工建立大事记很慢；AI擅长结构化提取。要点：输入垃圾则输出垃圾。本技能从配置声明的来源和用户上传的材料中提取。
侧重点框架（重要性标签）
同一事件因执业者是在证明主张还是反驳主张而具有不同的重要性：
- 原告/主张方（进攻框架） —— 🔴 标记确立请求权要件的事件（责任、因果关系、损害、通知）、关闭对方将试图打开的缺口的事件，或启动诉讼时效的事件。🟡 标记支持主张但可被质疑的事件。⚪ 是背景。
- 被告/抗辩方（防御框架） —— 🔴 标记打破请求权要件的事件（因果关系断裂、通知缺失、依赖缺失）、开启诉讼时效或管辖权抗辩的事件，或支持积极抗辩（免责、弃权、过错相抵）的事件。🟡 标记削弱对方叙事的事件。⚪ 是背景。
加载上下文
- 插件配置 CLAUDE.md → 案件理论上下文、
## Outputs获取工作成果标头 - 本案件的先行
chronology.md（如存在） - 用户上传或提供的任何文件
工作流
步骤0：保密门禁（每次先运行）
大事记从文件中提取。文件可能包含保密或受保护信息。
提取前：这些来源是否已经过保密筛选？
- A. 所有来源已清理——你已经筛选过。我提取时不加保密标记。
- B. 混合或尚未筛选——我提取并为每个条目加标记。
- C. 中止——先筛选——暂停技能。筛选来源。返回重新运行。
步骤1：识别文件来源
- 用户提供的路径——本次会话中放入的任何内容。
- 默认案件文件夹——从配置的文件存储模式展开。
- 声明的来源——配置中声明的来源。
- 询问——如果来源看起来不足，提示用户。
步骤2：提取 + 读取
对于每个有可读文件的来源：
- PDF、邮件、docx、txt —— 直接读取。
- 如果技能无法访问某个声明的来源，在输出的缺口部分中明确命名。
来源标注。 为每个大事记条目标注信息来源。对于任何无法追溯到提取文件的条目——例如从模型知识回忆的事实、通过联网搜索找到的公开记录事件——内联标注：
[联网检索——需复核]、[模型知识——需验证]或[用户提供]。不得删除或压缩标签。
步骤3：提取事件
对于每个文件，识别带日期的事件：
- 邮件：
[日期] [发送人] 告知 [收件人] [主题/内容] - 会议：
[日期] [参加人] 就 [主题] 开会 - 决定：
[日期] [决策人] 决定 [什么] - 诉讼文件：
[日期] [当事人] 提交 [起诉状/答辩状/上诉状] - 外部事件：
[日期] [事情发生]（合同签署、产品发布、监管行动等）
步骤4：去重
同一事件可能出现在多份文件中——这是一个有多个来源的事件。合并。合并条目引用所有来源。
步骤5：按案件理论标记重要性
- 🔴 关键——事件是关键事实的一部分
- 🟡 相关——背景、模式证据、支持次要论点
- ⚪ 背景——对完整性有用，不进入代理词
纪律：300条条目中有300条🔴标签等于没有标签。为真正能移动事实认定者的事件保留🔴。
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 · 154 lines · 93 tokens per session scan A 9c8cb1a2992d
chronology 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 93 tokens to every session and 1,864 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 2026-09-03.
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