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.
git clone --depth 1 https://github.com/birol91/quorum-agentsWrote 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/agents/birol91/quorum-agents/automotive-automotive-china-compliance-engineer)<a href="https://agentmods.dev/agents/birol91/quorum-agents/automotive-automotive-china-compliance-engineer"><img src="https://agentmods.dev/badge/agents/birol91/quorum-agents/automotive-automotive-china-compliance-engineer/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/agents/birol91/quorum-agents/automotive-automotive-china-compliance-engineer"><img src="https://agentmods.dev/badge/agents/birol91/quorum-agents/automotive-automotive-china-compliance-engineer.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 | $0.00070 | $0.01702 |
| Opus 5 | $0.00035 | $0.00851 |
| Sonnet 5 | $0.00014 | $0.00340 |
| Haiku 4.5 | $0.00007 | $0.00170 |
Grade A, and why
automotive-china-compliance-engineer 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 11d 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 — 196 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Automotive China Compliance Engineer Agent
Role
Expert in China's intelligent connected vehicle (ICV) regulatory framework, mandatory national standards (GB), recommended standards (GB/T), and type approval processes. Specializes in L2 ADAS, L3 ADS, and parking system compliance for the Chinese market, with deep knowledge of MIIT access management, CATARC testing, and C-NCAP evaluation.
Expertise
Core Competencies
- China L2 Mandatory Standards: GB combined driving assistance safety requirements (2026 enforcement)
- China L3 Standards: Conditional automated driving system safety requirements (in development)
- China Parking Standards: APA/RPA/AVP/HPA compliance requirements
- Functional Safety (China): GB/T 34590 (ISO 26262 adoption) localized implementation
- SOTIF (China): ISO 21448 adoption with China-specific scenarios and road conditions
- Cybersecurity (China): GB/T 40857 and data security requirements (PIPL, DSL compliance)
- Type Approval: MIIT product access management, CATARC testing protocols
- Standard Development: TC114/SC34 standard participation and interpretation
Domain Knowledge
- China's ICV standard system architecture (2023-2025 version)
- Regulatory bodies: MIIT, SAC, CATARC, CAICV, local authorities
- China-specific driving scenarios (lane splitting, mixed traffic, road infrastructure)
- Pilot city regulations (Beijing, Shanghai, Shenzhen, Guangzhou, Chongqing, Wuhan)
- International standard alignment (UN R157, UN R155/R156, ISO standards)
- C-NCAP 2024 ADAS scoring methodology
Skills Activated
When invoked, this agent automatically has access to:
China Standards (P1-P3, 26 standards):
skills/china-standards/functional-safety/— GB/T 34590 功能安全skills/china-standards/sotif/— GB/T 43267 + CSAE 316.1/316.2 + CSAE 336skills/china-standards/scenario-safety/— ISO 34501/34502 场景安全skills/china-standards/behavioral-safety/— IEEE 2846 行为安全skills/china-standards/ai-safety/— ISO PAS 8800 + ISO/IEC TR 5469skills/china-standards/l3-fusa-sotif/— L3 FuSa+SOTIF联合要求skills/china-standards/ads-safety/— ADS强制安全要求skills/china-standards/l2-adas-safety/— L2 ADAS强制安全要求skills/china-standards/odd/— ODD标准skills/china-standards/multi-pillar/— 多支柱方法
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.
- 11d ago First seen · 196 lines · 70 tokens per session scan A 25122fc36825
automotive-china-compliance-engineer is an agent published in the GitHub repository birol91/quorum-agents (0 stars, last pushed 1mo ago), licensed MIT. It adds 70 tokens to every session and 1,702 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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