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 pangzhenying2025/hermes-automotive-skills --skill china-scenario-safetygit clone --depth 1 https://github.com/pangzhenying2025/hermes-automotive-skillsWrote 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/pangzhenying2025/hermes-automotive-skills/china-scenario-safety)<a href="https://agentmods.dev/skills/pangzhenying2025/hermes-automotive-skills/china-scenario-safety"><img src="https://agentmods.dev/badge/skills/pangzhenying2025/hermes-automotive-skills/china-scenario-safety/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/pangzhenying2025/hermes-automotive-skills/china-scenario-safety"><img src="https://agentmods.dev/badge/skills/pangzhenying2025/hermes-automotive-skills/china-scenario-safety.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 | $0.00041 | $0.01105 |
| Opus 5.5 | $0.00016 | $0.00442 |
| Sonnet 5.5 | $0.00008 | $0.00221 |
| Haiku 4.5 | $0.00004 | $0.00111 |
Grade A, and why
china-scenario-safety 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 1mo 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 — 113 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Scenario Safety — China Standard
场景安全评估框架 — ISO 34501/34502 + ISO 34503/34504/34505
标准集一览
| 标准编号 | 名称 | 状态 | 推荐等级 |
|---|---|---|---|
| ISO 34501:2022 | 自动驾驶系统测试场景 术语 | 已发布 | P1 |
| ISO 34502:2022 | 基于场景的安全评估框架 | 已发布 | P1 |
| ISO 34502 GB征求意见稿 | 基于场景的安全评估框架(中国版) | 征求意见稿 | P3 |
| ISO 34503/34504/34505 | 场景描述/分类/生成方法 | DIS阶段 | P3 |
ISO 34502:2022 基于场景的安全评估工程框架
核心概念
场景安全评估三层模型
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Functional Scenario (功能场景)
└── 自然语言描述的抽象场景
└── 例:"高速公路上前车突然变道,暴露前方静止车辆"
Logical Scenario (逻辑场景)
└── 参数化描述,参数取值为范围/分布
└── 例:ego_speed ∈ [100,120] km/h,
target_speed = 0 km/h,
cut_out_ttc ∈ [2.0, 5.0] s
Concrete Scenario (具体场景)
└── 所有参数赋具体值的可执行场景
└── 例:ego_speed = 110 km/h,
target_speed = 0 km/h,
cut_out_ttc = 3.2 s
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
安全评估流程
ISO 34502 安全评估流程
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Phase 1: 场景识别
├── 从标准/法规提取
├── 从事故数据提取
├── 从自然驾驶数据提取
└── 专家知识补充
Phase 2: 场景描述与参数化
├── 功能场景定义
├── 逻辑场景参数化
└── 参数空间定义
Phase 3: 场景选择
├── 基于风险的优先级排序
├── 覆盖度分析
└── 测试资源分配
Phase 4: 场景执行
├── 仿真测试(批量执行)
├── 封闭场地测试(关键场景)
└── 开放道路测试(真实环境)
Phase 5: 安全论证
├── 通过率统计
├── 覆盖度论证
├── 残余风险评估
└── 安全案例构建
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
与DFM框架的关联
ISO 34502 ↔ DFM关联
├── ISO 34502定义了场景安全评估的工程框架
├── DFM(驾驶员基础模型)提供:
│ ├── 人类驾驶行为基线(作为安全参考)
│ ├── 场景暴露频率(基于大规模NDD)
│ ├── 场景参数分布(基于7.5M+轨迹数据)
│ └── 场景批评性量化指标
└── 组合使用:DFM为ISO 34502提供数据驱动的场景选择和安全论证
ISO 34503/34504/34505 场景标准族(P3)
ISO 3450x 场景标准族
├── ISO 34503: Specification of Operational Design Domain
│ └── ODD描述方法和分类框架
├── ISO 34504: Scenario Categorization
│ └── 场景分类方法(基于抽象层次)
└── ISO 34505: Scenario Generation and Selection
└── 场景生成和选择方法
相关技能
skills/china-standards/sotif/— SOTIF标准集skills/china-standards/odd/— ODD标准skills/automotive-scenario-driven-testing/— 场景驱动测试方法skills/automotive-dfm-benchmarking/— DFM基准评测
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.
- 1mo ago First seen · 113 lines · 41 tokens per session scan A 61ed90eb27e8
china-scenario-safety is a skill published in the GitHub repository pangzhenying2025/hermes-automotive-skills (10 stars, last pushed 5mo ago), licensed MIT. It adds 41 tokens to every session and 1,105 once invoked, about $0.0002 per session on Opus 5.5. 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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