Use WinML CLI with AI Agents

This section documents three complementary agent workflows: using the CLI to build and run models, contributing missing model support, and investigating ONNX inference latency.

Choose a skill

Goal	Skill	Guide
Build, optimize, compile, benchmark, or troubleshoot a supported model	use-winml-cli	Use WinML CLI with an AI agent
Add or repair model support and prepare a validated contribution	adding-model-support	Add model support with an AI agent
Investigate latency or validate optimization candidates on an EP/device	auto-optimize	Auto Optimize

Use use-winml-cli for normal model workflows, including targeting available CPU, GPU, and NPU execution providers.

Use adding-model-support when a model needs a recipe, exporter, resolver, task mapping, dataset adapter, evaluator, or shared CLI fix. It handles one model per run and drives the contribution through independent validation and review.

Use auto-optimize for an experimental optimization search or validation of an existing candidate. It checks correctness before paired performance measurements and publishes a validated replayable bundle. Routine optimization with existing CLI features remains part of use-winml-cli.

How to use a skill

Make the selected directory under skills/ available to an agent runtime that supports skills or custom instructions, then describe your goal in natural language. Each guide provides example prompts and explains what the skill handles automatically. Repository source directories are not automatically registered by every host. For loading diagnostics and stale-copy checks, see Auto Optimize loading and verification.