china-behavioral-safety

china-behavioral-safety is a skill for Claude Code, Codex from pangzhenying2025/hermes-automotive-skills. It costs 29 tokens per session (857 once invoked), scanned A, original, MIT.

A guide to IEEE 2846-2022, a standard for stating the safety assumptions behind automated driving systems. It covers expected road-user behavior, safe responses, collision responsibility, and safe-distance models.

In plain words
What is it for?
Use it to define behavioral safety rules, calculate safe following distances, and reason about responsibility and safety responses in automated-driving scenarios.
Why use it?
It helps teams make their assumptions about road situations explicit and assess whether an automated vehicle behaves safely when hazards arise.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to define behavioral safety rules, calculate safe following distances, and reason about responsibility and safety responses in automated-driving scenarios.

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Install with agentmods
npx agentmods add skills/pangzhenying2025/hermes-automotive-skills/china-behavioral-safety
Install

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.

Any agent
npx skills add pangzhenying2025/hermes-automotive-skills --skill china-behavioral-safety
Clone the repo
git clone --depth 1 https://github.com/pangzhenying2025/hermes-automotive-skills

Made for: Claude Code, Codex.

Wrote 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.

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README.md
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Per session 29 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 857 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00029 $0.00857
Opus 5 $0.00015 $0.00428
Sonnet 5 $0.00006 $0.00171
Haiku 4.5 $0.00003 $0.00086

Measured 9d ago against content hash 18dc1f852bc7, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

china-behavioral-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 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.

skills/china-behavioral-safety/SKILL.md · 85 lines

What it actually says

Behavioral Safety — China Standard

IEEE 2846-2022 自动驾驶系统安全假设

标准信息

属性
标准编号 IEEE 2846-2022
名称 A Framework for ADS Safety Assumptions
状态 已发布(2022年)
推荐等级 P1-强烈推荐
核心内容 定义ADS行为安全模型,含RSS(Responsibility-Sensitive Safety)框架

核心概念

安全假设框架

IEEE 2846 安全假设体系
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
1. 合理预见行为 (Reasonably Foreseeable Behavior)
   └── 其他道路参与者的预期行为范围

2. 安全响应规则 (Safety Response Rules)
   └── ADS在各场景下的安全行为要求

3. 事故责任判定 (Blame Attribution)
   └── 当碰撞不可避免时的责任归属

4. 安全距离模型 (Safe Distance Model)
   ├── 纵向安全距离 = f(v_ego, v_front, a_max_brake, reaction_time)
   ├── 横向安全距离 = f(v_lateral, lane_width)
   └── 交叉路口安全模型
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

RSS安全距离模型

# RSS纵向安全距离计算
def rss_longitudinal_safe_distance(
    v_ego: float,        # 自车速度 (m/s)
    v_front: float,      # 前车速度 (m/s)
    rho: float,          # 反应时间 (s), 通常取0.5-1.0s
    a_max_accel: float,  # 最大加速度 (m/s²)
    a_max_brake: float,  # 最大制动减速度 (m/s²)
    a_min_brake: float,  # 前车最小制动减速度 (m/s²)
) -> float:
    """
    d_safe = v_ego * rho + 0.5 * a_max_accel * rho²
             + (v_ego + rho * a_max_accel)² / (2 * a_max_brake)
             - v_front² / (2 * a_min_brake)
    """
    v_ego_after_reaction = v_ego + rho * a_max_accel
    d_ego_reaction = v_ego * rho + 0.5 * a_max_accel * rho**2
    d_ego_brake = v_ego_after_reaction**2 / (2 * a_max_brake)
    d_front_brake = v_front**2 / (2 * a_min_brake)
    return max(0, d_ego_reaction + d_ego_brake - d_front_brake)

中国场景适配

IEEE 2846 中国场景适配考虑
├── 反应时间参数:需考虑中国交通密度
├── 行为假设:电动自行车/三轮车行为模型
├── 交叉路口:中国特有交通规则(右转不停车等)
├── 高速公路:匝道合流行为差异
└── 责任判定:与中国交通法规对齐

相关技能

  • skills/china-standards/ads-safety/ — ADS安全要求
  • skills/china-standards/ai-safety/ — AI安全标准
  • skills/automotive-e2e-safety-analysis/ — 端到端安全分析
Changes

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.

  1. 9d ago First seen · 85 lines · 29 tokens per session scan A 18dc1f852bc7

Subscribe to this mod's changes

china-behavioral-safety is a skill published in the GitHub repository pangzhenying2025/hermes-automotive-skills (5 stars, last pushed 3mo ago), licensed MIT. It adds 29 tokens to every session and 857 once invoked, about $0.0001 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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