Intel AI PC Vision
Run local image classification as an Agent Skill. Return only actual OpenVINO output; never invent hardware availability or benchmark numbers.
Hardware Capability
Designed for Intel AI PC.
Supported acceleration:
- Intel CPU
- Intel GPU
- Intel NPU through OpenVINO
Runtime: OpenVINO
Model: MobileNetV3-Small
Precision: FP16
AUTO selects the first available device in this order: NPU, GPU, CPU. If the requested device is unavailable, fall back to a real available device and report the selected device in JSON.
Execute
Resolve every bundled path from the directory containing this SKILL.md. WorkBuddy exposes that directory as ${CODEBUDDY_SKILL_DIR}. For a user-provided image on Windows, run:
powershell -ExecutionPolicy Bypass -File "${CODEBUDDY_SKILL_DIR}\scripts\run_skill.ps1" -ImagePath "C:\absolute\path\to\image.jpg" -Device AUTO
For a safe verification using the bundled competition sample, run:
powershell -ExecutionPolicy Bypass -File "${CODEBUDDY_SKILL_DIR}\scripts\run_skill.ps1" -ImagePath "${CODEBUDDY_SKILL_DIR}\assets\examples\dog.jpg" -Device AUTO
Agent Response
Read the JSON from standard output. Present:
- actual
device - measured
latency_ms model_precision- Top-5 labels and confidence values
State that latency is a measurement from the current machine. Do not claim NPU execution unless the JSON says "device": "NPU".
Read references/usage.md when installation or dependency details are needed. Read references/agent-demo.md for the tested WorkBuddy prompt and references/test-report.md for competition evidence.
Expected JSON contract:
{
"image": "dog.jpg",
"device": "GPU",
"latency_ms": 5.2,
"model_precision": "FP16",
"top5": [
{"label": "Blenheim spaniel", "confidence": 0.86}
]
}
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