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 AutoZYX-Labs/ADSafetyPilot --skill sotif-deepgit clone --depth 1 https://github.com/AutoZYX-Labs/ADSafetyPilotWrote 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/autozyx-labs/adsafetypilot/sotif-deep)<a href="https://agentmods.dev/skills/autozyx-labs/adsafetypilot/sotif-deep"><img src="https://agentmods.dev/badge/skills/autozyx-labs/adsafetypilot/sotif-deep/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/autozyx-labs/adsafetypilot/sotif-deep"><img src="https://agentmods.dev/badge/skills/autozyx-labs/adsafetypilot/sotif-deep.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 | $0.00056 | $0.00735 |
| Opus 5 | $0.00028 | $0.00367 |
| Sonnet 5 | $0.00011 | $0.00147 |
| Haiku 4.5 | $0.00006 | $0.00073 |
Grade A, and why
sotif-deep 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 8d 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.
What it actually says
SOTIF 深度分析 | Deep SOTIF Analysis
本技能提供 ISO 21448 预期功能安全的全生命周期分析支持。
核心能力
1. 四象限分析框架
- Area 1 (已知安全) → Area 2 (已知不安全) → Area 3 (未知安全) → Area 4 (未知不安全)
- SOTIF目标：最小化Area 4，通过系统化方法将场景从Area 4转移到Area 2（已识别→已缓解）或Area 1（已验证）
2. 触发条件分类体系
基于张玉新团队的研究框架，系统化分类：
感知局限:
- 摄像头：光照（眩光、隧道明暗交替、夜间）、天气（暴雨>25mm/h、雾<200m、雪、霾PM2.5>200）、遮挡（镜头污染、虫渍）
- 毫米波雷达：金属反射、护栏虚警、隧道多径效应
- 激光雷达：雨雾散射、黑色目标低反射、玻璃透射
- 中国特有：沙尘暴（北方）、霾（华北）、冰雪（东北）
算法不足:
- 感知：误检/漏检、分类错误、跟踪丢失
- 预测：异常行为预测失败、中国驾驶员特有行为（高频加塞、非机动车混行）
- 决策：保守/激进策略不匹配、中国交通流特征不适配
人因误用:
- 模式混淆、过度信任、注意力不集中
- 中国特有：手机导航依赖、副驾干预、后排乘客干扰
3. 场景驱动验证策略
- ISO 21448 Clause 10/11 验证方法论
- 仿真 + 封闭场地 + 开放道路三层验证
- 与JAMA V4.0框架对接的验证矩阵
4. SOTIF审核清单
5级成熟度模型（初始→管理→定义→量化管理→优化）
使用示例
用户：我的ACC系统需要做SOTIF分析，ODD是高速公路60-120km/h
输出：
1. 触发条件清单（按感知/算法/人因分类）
2. 四象限场景分布
3. 验证策略建议（仿真覆盖度目标+实车测试方案）
4. 残余风险评估框架
参考标准
- ISO 21448:2022 Road vehicles — Safety of the intended functionality
- GB/T 43267-2023 道路车辆 预期功能安全
- ISO 34502 场景驱动安全评估框架
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.
- 8d ago First seen · 58 lines · 56 tokens per session scan A 91a49aa7d673
sotif-deep is a skill published in the GitHub repository AutoZYX-Labs/ADSafetyPilot (2 stars, last pushed 2mo ago), licensed MIT. It adds 56 tokens to every session and 735 once invoked, about $0.0003 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 2026-08-31.
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