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 ThomasMoreAI/legal-skills-open --skill traffic-accident-assessorgit clone --depth 1 https://github.com/ThomasMoreAI/legal-skills-openWrote 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/thomasmoreai/legal-skills-open/traffic-accident-assessor)<a href="https://agentmods.dev/skills/thomasmoreai/legal-skills-open/traffic-accident-assessor"><img src="https://agentmods.dev/badge/skills/thomasmoreai/legal-skills-open/traffic-accident-assessor/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/thomasmoreai/legal-skills-open/traffic-accident-assessor"><img src="https://agentmods.dev/badge/skills/thomasmoreai/legal-skills-open/traffic-accident-assessor.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00130 | $0.04528 |
| Opus 5 | $0.00065 | $0.02264 |
| Sonnet 5 | $0.00026 | $0.00906 |
| Haiku 4.5 | $0.00013 | $0.00453 |
Grade A, and why
traffic-accident-assessor 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 — 449 lines — stays where its author put it; the contents beside it link to each section on GitHub.
交通事故责任评估专业技能
概述
本技能基于《中华人民共和国道路交通安全法》及配套法规、司法解释，提供专业的交通事故责任评估服务。支持事故情况描述分析、现场照片识别辅助判定、法律责任引用和专业的责任划分结果输出。
目录结构
traffic-accident-assessor/
├── SKILL.md # 技能定义文件（本文件）
├── LICENSE # 许可证
├── references/ # 参考资料库
│ ├── traffic-laws.md # 道路交通安全法核心条文
│ ├── liability-rules.md # 责任判定规则体系
│ └── accident-types.md # 常见事故类型判定指南
└── evals/ # 测试用例
└── evals.json # 评估测试集
触发条件
自动触发：
- 用户描述交通事故经过并询问责任划分
- 用户上传事故现场照片要求分析
- 用户询问交通法规相关条款及适用性
- 用户需要交通事故赔偿或法律建议
- 涉及车辆、行人、非机动车的道路交通事故咨询
手动触发：
- 用户输入
/traffic、/accident、/事故评估等命令
核心工作流程
第一步：信息采集与结构化
当用户提供事故信息时，按以下结构进行信息提取和补全：
必采信息清单
| 信息类别 | 具体字段 | 重要性 |
|---|---|---|
| 时间信息 | 事故发生日期、具体时段（白天/夜间/黄昏）、天气状况 | 必须 |
| 地点信息 | 道路类型（城市道路/高速公路/乡村道路/路口等）、车道数、是否有隔离设施 | 必须 |
| 当事人信息 | 甲车类型（机动车/非机动车）、乙车/人类型、各方行驶方向 | 必须 |
| 行为信息 | 各方事发时的具体行为（速度、变道、转弯、超车、闯红灯等） | 核心 |
| 环境信息 | 交通标志、标线、信号灯状态、路面状况、视线条件 | 重要 |
| 后果信息 | 人员伤亡、财产损失程度 | 辅助 |
信息不足处理：
- 如果用户提供的描述存在关键信息缺失，主动追问以补全
- 对于模糊表述（如"开得很快"），引导用户给出更具体的描述
- 明确告知用户哪些信息的缺失可能影响判定准确性
第二步：照片分析与关键信息提取
当用户上传事故现场照片时，执行以下分析流程：
图像识别检查项
照片分析流程：
1. 确认照片清晰度和拍摄角度
2. 识别以下关键要素：
□ 车辆位置关系（相对方位、距离）
□ 碰撞痕迹位置（前部/后部/侧面、高度）
□ 路面标线（车道线、停止线、人行横道线）
□ 交通标志（限速、禁行、让行、停车等标志）
□ 信号灯状态（如有）
□ 刹车痕迹长度和方向
□ 散落物分布范围
□ 道路环境（路口形态、视距遮挡等）
3. 综合多张照片还原事故现场态势图
4. 标注各要素对责任判定的潜在影响
注意事项：
- 单张照片可能无法完整呈现事故全貌，建议用户尽可能提供多角度照片
- 夜间照片需特别关注照明条件和可见度
- 如照片质量不足以支撑判断，应明确说明局限性
- 图像分析结果作为辅助参考，不替代现场勘查结论
第三步：责任判定分析
基于采集的信息，按照以下判定框架进行分析：
责任判定层级框架
第一层：违法行为识别
↓ 逐一比对各方行为与法条
第二层：过错程度比较
↓ 分析各违法行为的危险性和因果关系
第三层：责任比例确定
↓ 结合全部因素综合评定
第四层：法律依据匹配
↓ 精确到具体条款编号
常见违法行为与对应法条速查
| 违法行为 | 主要法条依据 | 典型责任倾向 |
|---|---|---|
| 闯红灯 | 道交法第38条、第44条 | 主要以上责任 |
| 逆行 | 道交法第35条 | 全部或主要责任 |
| 超速 | 道交法第42条、第67条 | 视情节定责 |
| 违规变道 | 道交法第45条 | 主要或同等责任 |
| 未让行 | 道交法第47条、第52条 | 主要以上责任 |
| 酒驾/毒驾 | 道交法第22条第2款 | 加重责任 |
| 无证驾驶 | 道交法第19条 | 与事故因果关联后加责 |
| 疲劳驾驶 | 道交法第22条第2款 | 视疲劳程度定责 |
| 未保持安全距离 | 道交法第43条 | 后车主要或同等责任 |
| 违章停车 | 道交法第56条 | 视是否影响通行定责 |
| 行人违规横穿 | 道交法第62条、第76条 | 减轻机动车方责任 |
| 非机动车走机动车道 | 道交法第57条 | 承担相应责任 |
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 · 449 lines · 130 tokens per session scan A f07594f49f55
traffic-accident-assessor is a skill published in the GitHub repository ThomasMoreAI/legal-skills-open (72 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 130 tokens to every session and 4,528 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 2026-09-03.
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