Injecting skills into a frozen language model currently costs a million parameters and a reinforcement-learning pipeline.
We introduce \method{}, a System-1 decision operator trained by behavior cloning that lowers this cost by roughly two orders of magnitude.
The Default Operator
The default operator uses 330K parameters to match a 1.33M-parameter operator trained with reinforcement learning, exceeds or achieve comparable performance, while collapsing 3,685-token deliberation into a 6-token decision with no loss in accuracy.
Rank-4 Variant
A rank-4 variant with 23K parameters, 1/58 of the strongest published skill operator, suffices for SearchQA and near-suffices for LiveMath, where higher rank still helps;
the same recipe transfers across five tasks and three backbones, with out-of-distribution gains persisting on LiveMath problems released months after training.
Training and Architecture
The gap to prior work is trainability, and it is set jointly by initialization and architecture:
The initialization of prior operators zeroes the gradient of both large factor matrices at the first optimization step, whereas our zero-initialized output projection inside a shared low-rank backbone receives a gradient immediately, which a gradient-flow probe confirms directly.
Gain Analysis
The gain isn't chain-of-thought compression:
23 of 57 LiveMath points beat the base model's best-of-8 sampling, and a logit-lens probe shows the operator amplifies the answer along the model's existing late-layer pathway, not writing it earlier.
Gains track the base model's headroom across 13 base--task pairs, and skills compose as approximately linear operators that can be added, interpolated, and hot-swapped at inference time.