Is your feature request related to a problem? Please describe.
Agents implementing machine-learning workflows can introduce leakage, fit preprocessing on held-out rows, lose stable output keys, or claim GPU acceleration without dispatch evidence. The repository does not currently provide an Agent Skills-compatible guide and executable routing/correctness evaluations grounded in the checked-in APIs.
Describe the solution you'd like
Add a generalized cuml-machine-learning skill for native cuML and cuml.accel. Cover task definition, deployment-aligned splitting, training-only preprocessing, baselines, supervised and unsupervised evaluation, stable result handoff, acceleration/fallback evidence, and library boundaries.
Describe alternatives you've considered
Relying only on estimator API reference pages does not provide an end-to-end statistical and execution-evidence contract. A workflow-specific guide would not generalize across cuML use cases.
Additional context
A draft implementation is available in #8639.
Is your feature request related to a problem? Please describe.
Agents implementing machine-learning workflows can introduce leakage, fit preprocessing on held-out rows, lose stable output keys, or claim GPU acceleration without dispatch evidence. The repository does not currently provide an Agent Skills-compatible guide and executable routing/correctness evaluations grounded in the checked-in APIs.
Describe the solution you'd like
Add a generalized
cuml-machine-learningskill for native cuML andcuml.accel. Cover task definition, deployment-aligned splitting, training-only preprocessing, baselines, supervised and unsupervised evaluation, stable result handoff, acceleration/fallback evidence, and library boundaries.Describe alternatives you've considered
Relying only on estimator API reference pages does not provide an end-to-end statistical and execution-evidence contract. A workflow-specific guide would not generalize across cuML use cases.
Additional context
A draft implementation is available in #8639.