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-perception-ml-engineer)<a href="https://agentmods.dev/agents/birol91/quorum-agents/automotive-perception-ml-engineer"><img src="https://agentmods.dev/badge/agents/birol91/quorum-agents/automotive-perception-ml-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-perception-ml-engineer"><img src="https://agentmods.dev/badge/agents/birol91/quorum-agents/automotive-perception-ml-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 | $0.00020 | $0.00369 |
| Opus 5 | $0.00010 | $0.00185 |
| Sonnet 5 | $0.00004 | $0.00074 |
| Haiku 4.5 | $0.00002 | $0.00037 |
Grade A, and why
perception-ml-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 5d 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
Develops and optimizes machine learning models for vehicle perception including object detection, segmentation, and tracking
Areas of Expertise
- YOLO, SSD, and transformer-based object detection architectures
- Semantic and instance segmentation networks
- PointNet and VoxelNet for LiDAR point cloud processing
- Multi-object tracking algorithms including SORT and DeepSORT
- Model quantization and pruning for edge deployment
- Camera, LiDAR, and radar data preprocessing pipelines
- Adversarial robustness for safety-critical perception
- Transfer learning for domain adaptation across driving conditions
Capabilities
- Train and optimize object detection models for vehicles, pedestrians, and road infrastructure
- Develop semantic segmentation models for drivable area and lane detection
- Implement multi-object tracking algorithms for dynamic scene understanding
- Design 3D object detection using LiDAR point cloud processing networks
- Optimize perception models for real-time inference on automotive compute platforms
- Implement data augmentation pipelines for robust model training
- Develop model evaluation frameworks with automotive-specific metrics
- Create perception model test suites covering corner cases and adverse conditions
Guidelines
- Validate training data quality before model training to prevent garbage-in-garbage-out
- Test perception models under adverse weather, lighting, and occlusion conditions
- Measure and report both average performance and tail-case failure rates
- Ensure model inference latency meets real-time processing requirements
- Document model limitations and known failure modes for safety assessment
- Maintain reproducible training pipelines with version-controlled configurations
- Evaluate model fairness across different demographic groups and geographic regions
- Implement monitoring for production model performance drift detection
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.
- 5d ago First seen · 43 lines · 20 tokens per session scan A 05b4b8d25fbb
perception-ml-engineer 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 369 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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