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feat: add YOLO 2026 OpenVINO detection skill (Docker-based)
- Docker deployment using official openvino/ubuntu22_runtime image - Supports Intel NCS2 (MYRIAD), Intel GPU (iGPU/Arc), and CPU - AUTO device selector lets OpenVINO pick best available - FP16/INT8/FP32 precision options - YOLO26n with Ultralytics OpenVINO backend - JSONL stdin/stdout protocol (same as yolo-detection-2026) - Colab script for model export (runs on any platform)
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‎skills/detection/yolo-detection-2026-openvino/Dockerfile‎

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# ─── OpenVINO Detection — Runtime Image ───────────────────────────────────────
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# Uses official Intel OpenVINO runtime image (includes GPU drivers).
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# Built locally on user's machine.
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#
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# Build: docker build -t aegis-openvino-detect .
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# Run: docker run -i --rm --device /dev/dri --device /dev/bus/usb \
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# -v /tmp/aegis_detection:/tmp/aegis_detection \
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# aegis-openvino-detect
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FROM openvino/ubuntu22_runtime:latest
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USER root
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# ─── System dependencies ─────────────────────────────────────────────────────
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RUN apt-get update && apt-get install -y --no-install-recommends \
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python3-pip \
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usbutils \
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libusb-1.0-0 \
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&& rm -rf /var/lib/apt/lists/*
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# ─── Python dependencies ─────────────────────────────────────────────────────
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COPY requirements.txt /app/requirements.txt
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RUN pip3 install --no-cache-dir -r /app/requirements.txt
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# ─── Application code ────────────────────────────────────────────────────────
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COPY scripts/ /app/scripts/
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COPY models/ /app/models/
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WORKDIR /app
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# ─── Shared volume for frame exchange ────────────────────────────────────────
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VOLUME ["/tmp/aegis_detection"]
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ENV PYTHONUNBUFFERED=1
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# ─── Entry point ─────────────────────────────────────────────────────────────
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ENTRYPOINT ["python3", "scripts/detect.py"]

‎skills/detection/yolo-detection-2026-openvino/SKILL.md‎

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---
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name: yolo-detection-2026-openvino
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description: "OpenVINO — real-time object detection via Docker (NCS2, Intel GPU, CPU)"
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version: 1.0.0
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icon: assets/icon.png
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entry: scripts/detect.py
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deploy: deploy.sh
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runtime: docker
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requirements:
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docker: ">=20.10"
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platforms: ["linux", "macos", "windows"]
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parameters:
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- name: auto_start
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label: "Auto Start"
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type: boolean
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default: false
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group: Lifecycle
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- name: confidence
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label: "Confidence Threshold"
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type: number
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min: 0.1
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max: 1.0
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default: 0.5
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group: Model
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- name: classes
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label: "Detect Classes"
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type: string
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default: "person,car,dog,cat"
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group: Model
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- name: fps
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label: "Processing FPS"
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type: select
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options: [0.2, 0.5, 1, 3, 5, 15]
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default: 5
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group: Performance
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- name: input_size
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label: "Input Resolution"
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type: select
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options: [320, 640]
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default: 640
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group: Performance
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- name: device
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label: "Inference Device"
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type: select
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options: ["AUTO", "CPU", "GPU", "MYRIAD"]
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default: "AUTO"
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description: "AUTO lets OpenVINO pick the fastest available device"
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group: Performance
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- name: precision
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label: "Model Precision"
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type: select
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options: ["FP16", "INT8", "FP32"]
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default: "FP16"
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group: Performance
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capabilities:
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live_detection:
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script: scripts/detect.py
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description: "Real-time object detection via OpenVINO"
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category: detection
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mutex: detection
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---
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# OpenVINO Object Detection
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Real-time object detection using Intel OpenVINO runtime. Runs inside Docker for cross-platform support. Supports Intel NCS2 USB stick, Intel integrated GPU, Intel Arc discrete GPU, and any x86_64 CPU.
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## Requirements
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- **Docker Desktop 4.35+** (all platforms)
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- **Optional hardware**: Intel NCS2 USB, Intel iGPU, Intel Arc GPU
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- Falls back to CPU if no accelerator present
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## How It Works
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```
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┌─────────────────────────────────────────────────────┐
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│ Host (Aegis-AI) │
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│ frame.jpg → /tmp/aegis_detection/ │
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│ stdin ──→ ┌──────────────────────────────┐ │
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│ │ Docker Container │ │
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│ │ detect.py │ │
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│ │ ├─ loads OpenVINO IR model │ │
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│ │ ├─ reads frame from volume │ │
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│ │ └─ runs inference on device │ │
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│ stdout ←── │ → JSONL detections │ │
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│ └──────────────────────────────┘ │
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│ USB ──→ /dev/bus/usb (NCS2) │
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│ DRI ──→ /dev/dri (Intel GPU) │
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└─────────────────────────────────────────────────────┘
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```
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## Supported Devices
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| Device | Flag | Precision | ~Speed |
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|--------|------|-----------|--------|
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| Intel NCS2 | `MYRIAD` | FP16 | ~15ms |
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| Intel iGPU | `GPU` | FP16/INT8 | ~8ms |
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| Intel Arc | `GPU` | FP16/INT8 | ~4ms |
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| Any CPU | `CPU` | FP32/INT8 | ~25ms |
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| Auto | `AUTO` | Best | Auto |
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## Protocol
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Same JSONL as `yolo-detection-2026`:
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```jsonl
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{"event": "ready", "model": "yolo26n_openvino", "device": "GPU", "format": "openvino_ir", "classes": 80}
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{"event": "detections", "frame_id": 42, "camera_id": "front_door", "objects": [{"class": "person", "confidence": 0.85, "bbox": [100, 50, 300, 400]}]}
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{"event": "perf_stats", "total_frames": 50, "timings_ms": {"inference": {"avg": 8.1, "p50": 7.9, "p95": 10.2}}}
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```
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## Installation
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```bash
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./deploy.sh
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```

‎skills/detection/yolo-detection-2026-openvino/config.yaml‎

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# OpenVINO Detection Skill — Configuration Schema
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# Parsed by Aegis skill-registry-service.cjs → parseConfigYaml()
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params:
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- key: auto_start
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label: Auto Start
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type: boolean
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default: false
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description: "Start this skill automatically when Aegis launches"
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- key: confidence
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label: Confidence Threshold
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type: number
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default: 0.5
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description: "Minimum detection confidence (0.1–1.0)"
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- key: fps
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label: Frame Rate
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type: select
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default: 5
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description: "Detection processing rate"
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options:
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- { value: 0.2, label: "Ultra Low (0.2 FPS)" }
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- { value: 0.5, label: "Low (0.5 FPS)" }
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- { value: 1, label: "Normal (1 FPS)" }
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- { value: 3, label: "Active (3 FPS)" }
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- { value: 5, label: "High (5 FPS)" }
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- { value: 15, label: "Real-time (15 FPS)" }
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- key: classes
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label: Detection Classes
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type: string
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default: "person,car,dog,cat"
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description: "Comma-separated COCO class names to detect"
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- key: input_size
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label: Input Resolution
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type: select
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default: 640
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description: "Image size for inference — OpenVINO handles 640 well on most hardware"
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options:
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- { value: 320, label: "320×320 (fastest)" }
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- { value: 640, label: "640×640 (recommended)" }
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- key: device
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label: Inference Device
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type: select
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default: AUTO
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description: "OpenVINO device — AUTO picks best available"
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options:
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- { value: AUTO, label: "Auto-detect (best available)" }
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- { value: CPU, label: "CPU" }
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- { value: GPU, label: "Intel GPU (iGPU / Arc)" }
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- { value: MYRIAD, label: "Intel NCS2 (Myriad X)" }
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- key: precision
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label: Model Precision
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type: select
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default: FP16
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description: "FP16 is fastest on GPU/NCS2; INT8 is fastest on CPU; FP32 is most accurate"
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options:
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- { value: FP16, label: "FP16 (recommended for GPU/NCS2)" }
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- { value: INT8, label: "INT8 (fastest on CPU)" }
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- { value: FP32, label: "FP32 (most accurate)" }

‎skills/detection/yolo-detection-2026-openvino/deploy.bat‎

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@echo off
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REM deploy.bat — Docker-based bootstrapper for OpenVINO Detection Skill (Windows)
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setlocal enabledelayedexpansion
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set "SKILL_DIR=%~dp0"
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set "IMAGE_NAME=aegis-openvino-detect"
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set "IMAGE_TAG=latest"
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set "LOG_PREFIX=[openvino-deploy]"
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REM ─── Check Docker ────────────────────────────────────────────────────────
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where docker >nul 2>&1
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if %errorlevel% neq 0 (
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echo %LOG_PREFIX% ERROR: Docker not found. 1>&2
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echo {"event": "error", "stage": "docker", "message": "Docker not found"}
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exit /b 1
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)
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docker info >nul 2>&1
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if %errorlevel% neq 0 (
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echo %LOG_PREFIX% ERROR: Docker daemon not running. 1>&2
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echo {"event": "error", "stage": "docker", "message": "Docker daemon not running"}
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exit /b 1
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)
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echo {"event": "progress", "stage": "docker", "message": "Docker ready"}
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REM ─── Build Docker image ──────────────────────────────────────────────────
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echo %LOG_PREFIX% Building Docker image... 1>&2
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echo {"event": "progress", "stage": "build", "message": "Building Docker image..."}
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docker build -t %IMAGE_NAME%:%IMAGE_TAG% "%SKILL_DIR%"
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if %errorlevel% neq 0 (
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echo {"event": "error", "stage": "build", "message": "Docker image build failed"}
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exit /b 1
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)
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echo {"event": "progress", "stage": "build", "message": "Docker image ready"}
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REM ─── Probe devices ──────────────────────────────────────────────────────
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docker run --rm --privileged %IMAGE_NAME%:%IMAGE_TAG% python3 scripts/device_probe.py >nul 2>&1
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REM ─── Set run command ─────────────────────────────────────────────────────
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set "RUN_CMD=docker run -i --rm --privileged -v /tmp/aegis_detection:/tmp/aegis_detection --env AEGIS_SKILL_ID --env AEGIS_SKILL_PARAMS --env PYTHONUNBUFFERED=1 %IMAGE_NAME%:%IMAGE_TAG%"
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echo {"event": "complete", "status": "success", "run_command": "%RUN_CMD%", "message": "OpenVINO skill installed"}
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exit /b 0

‎skills/detection/yolo-detection-2026-openvino/deploy.sh‎

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#!/usr/bin/env bash
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# deploy.sh — Docker-based bootstrapper for OpenVINO Detection Skill
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#
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# Builds the Docker image locally and verifies device availability.
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# Called by Aegis skill-runtime-manager during installation.
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set -euo pipefail
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SKILL_DIR="$(cd "$(dirname "$0")" && pwd)"
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IMAGE_NAME="aegis-openvino-detect"
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IMAGE_TAG="latest"
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LOG_PREFIX="[openvino-deploy]"
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log() { echo "$LOG_PREFIX $*" >&2; }
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emit() { echo "$1"; }
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# ─── Step 1: Check Docker ────────────────────────────────────────────────────
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DOCKER_CMD=""
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for cmd in docker podman; do
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if command -v "$cmd" &>/dev/null; then
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DOCKER_CMD="$cmd"
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break
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fi
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done
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if [ -z "$DOCKER_CMD" ]; then
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log "ERROR: Docker (or Podman) not found."
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emit '{"event": "error", "stage": "docker", "message": "Docker not found. Install Docker Desktop 4.35+"}'
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exit 1
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fi
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if ! "$DOCKER_CMD" info &>/dev/null; then
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log "ERROR: Docker daemon is not running."
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emit '{"event": "error", "stage": "docker", "message": "Docker daemon not running"}'
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exit 1
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fi
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DOCKER_VER=$("$DOCKER_CMD" version --format '{{.Server.Version}}' 2>/dev/null || echo "unknown")
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log "Using $DOCKER_CMD (version: $DOCKER_VER)"
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emit "{\"event\": \"progress\", \"stage\": \"docker\", \"message\": \"Docker ready ($DOCKER_VER)\"}"
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# ─── Step 2: Detect platform for device access ──────────────────────────────
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PLATFORM="$(uname -s)"
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DEVICE_FLAGS=""
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case "$PLATFORM" in
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Linux)
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# Pass Intel GPU and USB devices
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[ -d /dev/dri ] && DEVICE_FLAGS="--device /dev/dri"
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[ -d /dev/bus/usb ] && DEVICE_FLAGS="$DEVICE_FLAGS --device /dev/bus/usb"
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[ -z "$DEVICE_FLAGS" ] && DEVICE_FLAGS="--privileged"
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log "Platform: Linux — devices: $DEVICE_FLAGS"
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;;
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Darwin)
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log "Platform: macOS — Docker Desktop USB/IP for NCS2, CPU fallback available"
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DEVICE_FLAGS="--privileged"
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;;
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MINGW*|MSYS*|CYGWIN*)
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log "Platform: Windows — Docker Desktop USB/IP"
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DEVICE_FLAGS="--privileged"
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;;
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*)
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DEVICE_FLAGS="--privileged"
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;;
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esac
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emit "{\"event\": \"progress\", \"stage\": \"platform\", \"message\": \"Platform: $PLATFORM\"}"
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# ─── Step 3: Build Docker image ─────────────────────────────────────────────
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log "Building Docker image: $IMAGE_NAME:$IMAGE_TAG ..."
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emit '{"event": "progress", "stage": "build", "message": "Building Docker image..."}'
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if "$DOCKER_CMD" build -t "$IMAGE_NAME:$IMAGE_TAG" "$SKILL_DIR" 2>&1 | while read -r line; do
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log "$line"
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done; then
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log "Docker image built successfully"
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emit '{"event": "progress", "stage": "build", "message": "Docker image ready"}'
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else
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log "ERROR: Docker build failed"
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emit '{"event": "error", "stage": "build", "message": "Docker image build failed"}'
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exit 1
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fi
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# ─── Step 4: Probe devices ──────────────────────────────────────────────────
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log "Probing OpenVINO devices..."
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emit '{"event": "progress", "stage": "probe", "message": "Checking OpenVINO devices..."}'
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PROBE_OUTPUT=$("$DOCKER_CMD" run --rm $DEVICE_FLAGS \
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"$IMAGE_NAME:$IMAGE_TAG" python3 scripts/device_probe.py 2>/dev/null) || true
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if echo "$PROBE_OUTPUT" | grep -q '"accelerator_found": true'; then
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log "Hardware accelerator detected (GPU/NCS2)"
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emit '{"event": "progress", "stage": "probe", "message": "Hardware accelerator detected"}'
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else
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log "No accelerator — CPU mode available"
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emit '{"event": "progress", "stage": "probe", "message": "CPU mode (no GPU/NCS2 detected)"}'
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fi
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# ─── Step 5: Build run command ───────────────────────────────────────────────
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RUN_CMD="$DOCKER_CMD run -i --rm $DEVICE_FLAGS"
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RUN_CMD="$RUN_CMD -v /tmp/aegis_detection:/tmp/aegis_detection"
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RUN_CMD="$RUN_CMD --env AEGIS_SKILL_ID --env AEGIS_SKILL_PARAMS --env PYTHONUNBUFFERED=1"
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RUN_CMD="$RUN_CMD $IMAGE_NAME:$IMAGE_TAG"
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log "Runtime command: $RUN_CMD"
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emit "{\"event\": \"complete\", \"status\": \"success\", \"run_command\": \"$RUN_CMD\", \"message\": \"OpenVINO skill installed\"}"
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log "Done!"
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exit 0

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