cmd-ai-ml-ml

What problem does it solve?

Guides ML/AI practitioners through diagnostic setup and scoping of ML projects by analyzing project configuration, artifact availability, and tooling setup to accelerate training, inference pipelines, and deployment planning.

Core Features & Use Cases

• Diagnostic context gathering: Detect ML framework from project config (requirements.txt, pyproject.toml, setup.py) and identify common frameworks such as torch, tensorflow, jax, sklearn, and transformers.
• Artifact discovery: Locate model artifacts like .pt, .onnx, .safetensors, .pkl, .h5, saved_model/, or checkpoints to inform training or deployment steps.
• Tooling and scope definition: Review ML tooling configurations (mlflow, wandb, dvc, hydra configs) and outline project scope by scanning for directories (models/, training/, data/, pipelines/).
• Actionable guidance: Provide a plan for model training, inference pipelines, and deployment tasks based on the gathered context.

Quick Start

Begin by providing your project details and let the skill detect the ML framework, artifacts, tooling config, and scope to guide training, inference, and deployment planning.