Menü
Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.
Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
Ein direkter Befehl überspringt den Prüf-Prompt. Prüfen Sie die Quelle, bevor Sie ihn ausführen.
npx skills add https://github.com/SharpAI/DeepCamera --skill yolo-detection-2026Der Befehl bleibt in einer Zeile. Scrollen Sie horizontal, um ihn vor dem Kopieren vollständig zu prüfen.
Sie bevorzugen eine lokale Kopie? Laden Sie die Dateien herunter, die SkillsMP derzeit vorliegen.
Real-time depth map privacy transforms using Depth Anything v2 (CoreML + PyTorch)
LLM & VLM evaluation suite for home security AI applications
Google Coral Edge TPU — real-time object detection natively (macOS / Linux)
Basierend auf der SOC-Berufsklassifikation
SKILL.md wird angezeigt
| 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 |