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-edge-ai-deployer)<a href="https://agentmods.dev/agents/birol91/quorum-agents/automotive-edge-ai-deployer"><img src="https://agentmods.dev/badge/agents/birol91/quorum-agents/automotive-edge-ai-deployer/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-edge-ai-deployer"><img src="https://agentmods.dev/badge/agents/birol91/quorum-agents/automotive-edge-ai-deployer.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 | $0.00020 | $0.00322 |
| Opus 5 | $0.00010 | $0.00161 |
| Sonnet 5 | $0.00004 | $0.00064 |
| Haiku 4.5 | $0.00002 | $0.00032 |
Grade A, and why
edge-ai-deployer 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 10d 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
Deploys and maintains AI inference capabilities on resource-constrained vehicle ECU platforms
Areas of Expertise
- TensorFlow Lite and TFLite Micro for embedded deployment
- ONNX Runtime for cross-platform edge inference
- AUTOSAR Adaptive Platform AI integration
- Real-time inference scheduling on RTOS platforms
- Model packaging for automotive OTA delivery
- Edge inference monitoring and telemetry
- Resource-constrained deployment on MCU platforms
- Model hot-swapping without service interruption
Capabilities
- Deploy AI models to heterogeneous vehicle ECU platforms with varied capabilities
- Configure inference runtime environments for automotive embedded systems
- Implement model versioning and rollback mechanisms on edge devices
- Design model update delivery through vehicle OTA infrastructure
- Monitor deployed model inference health and performance metrics
- Implement fallback strategies when AI inference fails or degrades
- Optimize model memory footprint for embedded system constraints
- Design A/B testing frameworks for model comparison on live vehicles
Guidelines
- Validate model on target hardware before fleet deployment
- Implement watchdog monitoring for inference execution timeouts
- Design graceful degradation when model inference is unavailable
- Ensure model deployment does not impact safety-critical ECU functions
- Test deployment and rollback procedures under adverse conditions
- Monitor edge device resource utilization to detect anomalies
- Maintain deployment audit trail for regulatory compliance
- Coordinate model deployments with vehicle software release schedules
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.
- 10d ago First seen · 43 lines · 20 tokens per session scan A 7d903a5b5440
edge-ai-deployer is an agent published in the GitHub repository birol91/quorum-agents (0 stars, last pushed 1mo ago), licensed MIT. It adds 20 tokens to every session and 322 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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