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 LawMotion-AI/Vibe-Lawyering --skill legal-risk-visualizationgit clone --depth 1 https://github.com/LawMotion-AI/Vibe-LawyeringWrote 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/lawmotion-ai/vibe-lawyering/legal-risk-visualization)<a href="https://agentmods.dev/skills/lawmotion-ai/vibe-lawyering/legal-risk-visualization"><img src="https://agentmods.dev/badge/skills/lawmotion-ai/vibe-lawyering/legal-risk-visualization/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/lawmotion-ai/vibe-lawyering/legal-risk-visualization"><img src="https://agentmods.dev/badge/skills/lawmotion-ai/vibe-lawyering/legal-risk-visualization.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 | $0.00127 | $0.05289 |
| Opus 5 | $0.00063 | $0.02645 |
| Sonnet 5 | $0.00025 | $0.01058 |
| Haiku 4.5 | $0.00013 | $0.00529 |
Grade A, and why
legal-risk-visualization 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 12d 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 — 424 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Legal Risk Visualization
Overview
- 输入： 法律分析文本（简要或详尽均可）
- 输出： 结构化风险分析报告（Markdown 格式 + 4 张 PNG 图片）
- 报告语言： 始终中文
- 核心理念： 第三层（影响路径图）为唯一数据源头，其他三层从中派生
- 渲染工具： matplotlib（雷达图、风险矩阵）+ mmdc（Mermaid 影响路径图、决策树）
- 读者定位： 法律人 + 业务决策者，报告主体不使用工程化缩写
本 skill 将线性法律分析文本转化为网络化风险结构，支持内部决策与系统化风险管理。
不是简单风险评分工具，而是法律推理结构的可视化表达系统。
术语规范
全技能范围内使用以下中文术语，主报告中不使用英文缩写：
| 术语 | 含义 | 仅附录中可出现的缩写 |
|---|---|---|
| 风险指数 | 单节点综合风险评分 | NRS |
| 传导风险值 | 沿路径累积的风险 | PCR |
| 发生可能性 | 该风险发生的概率 | P |
| 影响严重性 | 该风险造成的损害程度 | I |
| 可干预程度 | 当事人可主动干预的程度 | C |
| 可补救程度 | 风险后果可修复的程度 | R |
节点类型在图表中使用颜色 + 图例区分，不在节点文本中显示类型标签：
| 内部分类 | 图表显示标签 | 颜色 |
|---|---|---|
| 事实节点 | 风险源头 | 🔴 红色 #E74C3C |
| 法律判断节点 | 中间环节 | 🔵 蓝色 #3498DB |
| 风险结果节点 | 风险后果 | 🟠 橙色 #F39C12 |
| 商业影响节点 | 业务影响 | 🟣 紫色 #9B59B6 |
边标签简化规则（数值权重仅保留在附录）：
| 传导强度 | 图中表现 | 附录中完整标注 |
|---|---|---|
| 强传导 | 粗箭头 ==> |
强因果 0.9 |
| 中传导 | 普通箭头 --> |
中因果 0.6 |
| 弱传导/待确认 | 虚线 -.-> + "待确认" |
弱因果 0.3 |
统一配色方案（全部图表一致）：
| 颜色 | 色号 | 含义 |
|---|---|---|
| 🟢 绿色 | #27AE60 |
安全/较低（1-2级） |
| 🟡 黄色 | #F39C12 |
关注/中等（3级） |
| 🔴 红色 | #E74C3C |
警戒/较高（4-5级） |
| 🔵 蓝色 | #3498DB |
可干预节点 |
| ⚫ 灰色 | #95A5A6 |
待确认关系 |
Workflow（7 步顺序执行）
Step 1：读取并理解法律分析文本
- 通读全文，识别法律领域（合同纠纷、知识产权、劳动争议、公司治理等）
- 确定具体场景和涉及方
- 选择适用的风险维度（从以下维度中选择 4-6 个）：
- 合规风险、诉讼风险、财务风险、声誉风险、政策风险、执行风险、运营风险
- 可根据行业特征增加特定维度
- 维度选择依据：文本涉及的风险类别覆盖情况
Step 2：抽取风险节点
从文本中识别以下类型的表述，每个表述对应一个风险节点：
| 表述类型 | 识别特征 |
|---|---|
| 风险描述 | "存在…风险""可能面临…" |
| 不确定性判断 | "尚不明确""有待确认""存在争议" |
| 可能后果 | "可能导致""将面临""后果为" |
| 条件触发语句 | "若…则…""一旦…就…""在…情况下" |
输出节点列表，每个节点包含：
- 编号（N1, N2, ...）
- 名称（简短描述）
- 来源文本依据（原文引用）
Step 3：分类节点并建立因果
3a. 节点分类
将每个节点归类为以下四种类型之一：
| 类型 | 定义 | 典型特征 | 图表标签 | 颜色 |
|---|---|---|---|---|
| 事实节点 | 客观存在的状态或条件 | 合同条款、已发生事件 | 风险源头 | 🔴 红色 |
| 法律判断节点 | 需要法律推理才能确定的判断 | 责任认定、合规性判断 | 中间环节 | 🔵 蓝色 |
| 风险结果节点 | 最终的法律后果 | 赔偿、处罚、判决结果 | 风险后果 | 🟠 橙色 |
| 商业影响节点 | 对业务运营的实际影响 | 资金流、声誉、运营 | 业务影响 | 🟣 紫色 |
What ships with it
8 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.
- assets/report-template.md 7.0 KB
- references/anchoring-tables.md 4.6 KB
- references/case-example.md 18 KB
- references/causal-patterns.md 3.1 KB
- references/formulas.md 4.7 KB
- scripts/render_mermaid.py 7.2 KB runs code
- scripts/render_radar.py 6.8 KB runs code
- scripts/render_risk_matrix.py 7.7 KB runs code
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.
- 12d ago First seen · 424 lines · 127 tokens per session scan A 721499c623dc
legal-risk-visualization is a skill published in the GitHub repository LawMotion-AI/Vibe-Lawyering (20 stars, last pushed 4mo ago), licensed MIT. It adds 127 tokens to every session and 5,289 once invoked, about $0.0006 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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