yolo-detection-2026-coral-tpu-win-wsl - SKILL.md Agent Skill

name: yolo-detection-2026-coral-tpu-win-wsl description: "Google Coral Edge TPU — real-time object detection natively via Windows WSL" version: 1.0.0 icon: assets/icon.png entry: scripts/wsl_wrapper.cjs deploy: windows: deploy.bat runtime: wsl-python

requirements: platforms: ["windows"]

parameters:

name: auto_start label: "Auto Start" type: boolean default: false description: "Start this skill automatically when Aegis launches" group: Lifecycle
name: confidence label: "Confidence Threshold" type: number min: 0.1 max: 1.0 default: 0.5 description: "Minimum detection confidence — lower than GPU models due to INT8 quantization" 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 — Edge TPU handles 15+ FPS easily" group: Performance
name: input_size label: "Input Resolution" type: select options: [320, 640] default: 320 description: "320 fits fully on TPU (~~4ms), 640 partially on CPU (~~20ms)" group: Performance
name: tpu_device label: "TPU Device" type: select options: ["auto", "0", "1", "2", "3"] default: "auto" description: "Which Edge TPU to use — auto selects first available" group: Performance
name: clock_speed label: "TPU Clock Speed" type: select options: ["standard", "max"] default: "standard" description: "Max is faster but runs hotter — needs active cooling for sustained use" group: Performance

capabilities: live_detection: script: scripts/detect.py description: "Real-time object detection on live camera frames via Edge TPU inside WSL"

category: detection mutex: detection

Coral TPU Object Detection (Windows WSL)

Real-time object detection natively utilizing the Google Coral Edge TPU accelerator on your local hardware via Windows Subsystem for Linux (WSL). Detects 80 COCO classes (person, car, dog, cat, etc.) with ~4ms inference on 320x320 input.

Requirements

Google Coral USB Accelerator (USB 3.0 port recommended)
WSL2 installed and running on Windows
usbipd-win installed on the Windows host

How It Works

┌─────────────────────────────────────────────────────┐
│ Host (Aegis-AI on Windows)                          │
│   frame.jpg → /tmp/aegis_detection/                 │
│   stdin  ──→ ┌──────────────────────────────┐       │
│              │ WSL Container / Environment   │       │
│              │   detect.py                   │       │
│              │   ├─ loads _edgetpu.tflite     │       │
│              │   ├─ reads frame from disk     │       │
│              │   └─ runs inference on TPU    │       │
│   stdout ←── │   → JSONL detections          │       │
│              └──────────────────────────────┘       │
│   USB ──→ usbipd-win bridge to WSL                  │
└─────────────────────────────────────────────────────┘

Aegis writes camera frame JPEG to shared /tmp/aegis_detection/ workspace
Sends frame event via stdin JSONL to the WSL Python instance
detect.py invokes PyCoral and executes natively on the mapped USB Edge TPU inside Linux
Returns detections event via stdout JSONL back to Windows Host

Performance

Input Size	Inference	On-chip	Notes
320x320	~4ms	100%	Fully on TPU, best for real-time
640x640	~20ms	Partial	Some layers on CPU (model segmented)

Cooling: The USB Accelerator aluminum case acts as a heatsink. If too hot to touch during continuous inference, it will thermal-throttle. Consider active cooling or clock_speed: standard.

Installation

Windows (WSL)

Run deploy.bat — this will:

Verify usbipd is installed and bind the 18d1:9302 and 1a6e:089a Edge TPU hardware IDs.
Setup a Python virtual environment exclusively within WSL.
Install the Edge TPU libraries and dependencies within the WSL boundary.
Auto-attach the device using usbipd seamlessly during invocation.