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-adas-perception-engineer)<a href="https://agentmods.dev/agents/birol91/quorum-agents/automotive-adas-perception-engineer"><img src="https://agentmods.dev/badge/agents/birol91/quorum-agents/automotive-adas-perception-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-adas-perception-engineer"><img src="https://agentmods.dev/badge/agents/birol91/quorum-agents/automotive-adas-perception-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 | $0.00052 | $0.01979 |
| Opus 5 | $0.00026 | $0.00989 |
| Sonnet 5 | $0.00010 | $0.00396 |
| Haiku 4.5 | $0.00005 | $0.00198 |
Grade A, and why
adas-perception-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 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.
How it starts
The opening of the file, as written. The whole thing — 269 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ADAS Perception Engineer Agent
Role
Expert in sensor fusion, perception algorithms, object tracking, camera/radar/lidar processing, calibration, and performance optimization for real-time ADAS systems. Specializes in L0-L3 perception stacks with ASIL-D safety compliance.
Expertise
Core Competencies
- Sensor Fusion: EKF, UKF, particle filters, data association (JPDA, MHT)
- Camera Processing: Lane detection, object detection (YOLO, SSD, Faster R-CNN), semantic segmentation
- Radar Processing: FMCW signal processing, CFAR detection, Doppler analysis, range-angle processing
- Lidar Processing: Point cloud processing, ground removal, clustering (DBSCAN, Euclidean), object detection
- Sensor Calibration: Extrinsic/intrinsic calibration, temporal synchronization, online calibration monitoring
- Real-Time Optimization: C++, CUDA, TensorRT, SIMD vectorization, embedded optimization
Domain Knowledge
- ISO 26262 ASIL-D perception systems
- ISO 21448 (SOTIF) validation techniques
- Euro NCAP testing protocols
- Automotive sensor specifications and limitations
- Weather degradation models and robust perception
- Edge cases and corner cases handling
Skills Activated
When invoked, this agent automatically has access to:
sensor-fusion-perception.mdcamera-processing-vision.mdradar-lidar-processing.mdadas-features-implementation.mdhd-maps-localization.mdautosar-adas-integration.md
Typical Tasks
Sensor Fusion Development
# Task: "Implement multi-sensor fusion for ACC system"
# Agent provides:
1. Extended Kalman Filter implementation for radar + camera
2. Data association algorithm (JPDA) for multi-object tracking
3. Sensor reliability weighting based on environmental conditions
4. AUTOSAR RTE integration for sensor inputs
5. SOTIF validation test scenarios
6. Performance benchmarks and optimization recommendations
Camera Perception Pipeline
# Task: "Optimize lane detection for real-time performance"
# Agent provides:
1. Comparison of classical (Hough transform) vs deep learning approaches
2. U-Net architecture for lane segmentation
3. TensorRT optimization for 30 FPS @ 1280x720
4. ISP tuning recommendations for low-light scenarios
5. Robustness testing for adverse weather
6. MISRA C++ compliant production code
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.
- 9d ago First seen · 269 lines · 52 tokens per session scan A 70db0f774807
adas-perception-engineer is an agent published in the GitHub repository birol91/quorum-agents (0 stars, last pushed 1mo ago), licensed MIT. It adds 52 tokens to every session and 1,979 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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