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Affichage de SKILL.md
Basé sur la classification professionnelle SOC
| name | yolo-detection-2026 |
| description | YOLO 2026 — state-of-the-art real-time object detection |
| version | 2.0.0 |
| icon | assets/icon.png |
| entry | scripts/detect.py |
| deploy | deploy.sh |
| requirements | {"python":">=3.9","ultralytics":">=8.3.0","torch":">=2.4.0","platforms":["linux","macos","windows"]} |
| parameters | [{"name":"auto_start","label":"Auto Start","type":"boolean","default":false,"description":"Start this skill automatically when Aegis launches","group":"Lifecycle"},{"name":"model_size","label":"Model Size","type":"select","options":["nano","small","medium","large"],"default":"nano","description":"Larger models are more accurate but slower","group":"Model"},{"name":"confidence","label":"Confidence Threshold","type":"number","min":0.1,"max":1,"default":0.8,"group":"Model"},{"name":"classes","label":"Detect Classes","type":"string","default":"person,car,dog,cat","description":"Comma-separated COCO class names (80 classes available)","group":"Model"},{"name":"fps","label":"Processing FPS","type":"select","options":[0.2,0.5,1,3,5,15],"default":5,"description":"Frames per second — higher = more CPU/GPU usage","group":"Performance"},{"name":"device","label":"Inference Device","type":"select","options":["auto","cpu","cuda","mps","rocm"],"default":"auto","description":"auto = best available GPU, else CPU","group":"Performance"},{"name":"use_optimized","label":"Hardware Acceleration","type":"boolean","default":true,"description":"Auto-convert model to optimized format for faster inference","group":"Performance"},{"name":"compute_units","label":"Apple Compute Units","type":"select","options":["auto","cpu_and_ne","all","cpu_only","cpu_and_gpu"],"default":"auto","description":"CoreML compute target — 'auto' routes to Neural Engine (NPU), leaving GPU free for LLM/VLM","group":"Performance","platform":"macos"}] |
| capabilities | {"live_detection":{"script":"scripts/detect.py","description":"Real-time object detection on live camera frames"}} |
Real-time object detection using the latest YOLO 2026 models. Detects 80+ COCO object classes including people, vehicles, animals, and everyday objects. Outputs bounding boxes with labels and confidence scores.
| Size | Speed | Accuracy | Best For |
|---|---|---|---|
| nano | Fastest | Good | Real-time on CPU, edge devices |
| small | Fast | Better | Balanced speed/accuracy |
| medium | Moderate | High | Accuracy-focused deployments |
| large | Slower | Highest | Maximum detection quality |
The skill uses env_config.py to automatically detect hardware and convert the model to the fastest format for your platform. Conversion happens once during deployment and is cached.
| Platform | Backend | Optimized Format | Compute Units | Expected Speedup |
|---|---|---|---|---|
| NVIDIA GPU | CUDA | TensorRT .engine | GPU | ~3-5x |
| Apple Silicon (M1+) | MPS | CoreML .mlpackage | Neural Engine (NPU) | ~2x |
| Intel CPU/GPU/NPU | OpenVINO | OpenVINO IR .xml | CPU/GPU/NPU | ~2-3x |
| AMD GPU | ROCm | ONNX Runtime | GPU | ~1.5-2x |
| CPU (any) | CPU | ONNX Runtime | CPU | ~1.5x |
Apple Silicon Note: Detection defaults to
cpu_and_ne(CPU + Neural Engine), keeping the GPU free for LLM/VLM inference. Setcompute_units: allto include GPU if not running local LLM.
deploy.sh detects your hardware via env_config.HardwareEnv.detect()requirements_{backend}.txt (e.g. CUDA → includes tensorrt)detect.py loads the cached optimized model automaticallySet use_optimized: false to disable auto-conversion and use raw PyTorch.
Set auto_start: true in the skill config to start detection automatically when Aegis launches. The skill will begin processing frames from the selected camera immediately.
auto_start: true
model_size: nano
fps: 5
The skill emits perf_stats events every 50 frames with aggregate timing:
{"event": "perf_stats", "total_frames": 50, "timings_ms": {
"inference": {"avg": 3.4, "p50": 3.2, "p95": 5.1},
"postprocess": {"avg": 0.15, "p50": 0.12, "p95": 0.31},
"total": {"avg": 3.6, "p50": 3.4, "p95": 5.5}
}}
Communicates via JSON lines over stdin/stdout.
{"event": "frame", "frame_id": 42, "camera_id": "front_door", "timestamp": "...", "frame_path": "/tmp/aegis_detection/frame_front_door.jpg", "width": 1920, "height": 1080}
{"event": "ready", "model": "yolo2026n", "device": "mps", "backend": "mps", "format": "coreml", "gpu": "Apple M3", "classes": 80, "fps": 5}
{"event": "detections", "frame_id": 42, "camera_id": "front_door", "timestamp": "...", "objects": [
{"class": "person", "confidence": 0.92, "bbox": [100, 50, 300, 400]}
]}
{"event": "perf_stats", "total_frames": 50, "timings_ms": {"inference": {"avg": 3.4}}}
{"event": "error", "message": "...", "retriable": true}
[x_min, y_min, x_max, y_max] — pixel coordinates (xyxy).
{"command": "stop"}
The deploy.sh bootstrapper handles everything — Python environment, GPU backend detection, dependency installation, and model optimization. No manual setup required.
./deploy.sh
| File | Backend | Key Deps |
|---|---|---|
requirements_cuda.txt | NVIDIA | torch (cu124), tensorrt |
requirements_mps.txt | Apple | torch, coremltools |
requirements_intel.txt | Intel | torch, openvino |
requirements_rocm.txt | AMD | torch (rocm6.2), onnxruntime-rocm |
requirements_cpu.txt | CPU | torch (cpu), onnxruntime |