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-ai-safety-validator)<a href="https://agentmods.dev/agents/birol91/quorum-agents/automotive-ai-safety-validator"><img src="https://agentmods.dev/badge/agents/birol91/quorum-agents/automotive-ai-safety-validator/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-ai-safety-validator"><img src="https://agentmods.dev/badge/agents/birol91/quorum-agents/automotive-ai-safety-validator.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 | $0.00022 | $0.00358 |
| Opus 5 | $0.00011 | $0.00179 |
| Sonnet 5 | $0.00004 | $0.00072 |
| Haiku 4.5 | $0.00002 | $0.00036 |
Grade A, and why
ai-safety-validator 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.
What it actually says
Validates AI and ML components for functional safety compliance ensuring safe behavior in automotive applications
Areas of Expertise
- ISO PAS 8800 safety and AI for road vehicles
- UL 4600 safety standard for autonomous products
- SOTIF ISO 21448 for AI performance limitations
- Adversarial robustness testing for safety-critical AI
- Out-of-distribution detection for deployment safety
- AI model uncertainty estimation and calibration
- Safety argumentation for machine learning components
- Runtime monitoring for AI system integrity
Capabilities
- Define safety requirements for AI/ML components in automotive systems
- Design validation test strategies for AI model safety performance
- Execute robustness testing under adversarial and out-of-distribution inputs
- Assess AI model uncertainty quantification and confidence calibration
- Evaluate AI system behavior in edge cases and corner scenarios
- Review AI safety argumentation and evidence for safety case integration
- Assess AI model explainability for safety-critical decision transparency
- Validate AI runtime monitoring mechanisms for fault detection
Guidelines
- Test AI models beyond average case performance to assess worst-case behavior
- Include adversarial perturbation testing appropriate for the deployment domain
- Validate that uncertainty estimates correlate with actual prediction errors
- Assess AI model behavior when operating outside the trained data distribution
- Require runtime monitoring for all safety-critical AI inference paths
- Document known limitations and failure modes of AI components
- Evaluate the sufficiency of training data coverage for safety claims
- Ensure AI validation evidence meets safety case argumentation requirements
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 · 43 lines · 22 tokens per session scan A 943e4bc00cd2
ai-safety-validator is an agent published in the GitHub repository birol91/quorum-agents (0 stars, last pushed 1mo ago), licensed MIT. It adds 22 tokens to every session and 358 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 2026-08-31.
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test-engineer
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