executing-plans
executing-plans
Use when executing an implementation plan in the current session as the implementer yourself — your human partner chose inline execution, or no subagent tool is available
autovp_training_loopRun a reduced AutoVP prompt optimization step while proving the source classifier remains frozen.
Independent GitHub snapshot · Automated classification · Original documentation governs
Use this skill to recover or test AutoVP's parameter-efficient adaptation mechanism. It is appropriate when a frozen classifier callable, prompt module, label mapper, and small dataset are available.
Training examples, labels, prompt parameters, frozen classifier weights/callable, label mapping, learning rate, and step count.
Loss before/after, prompt parameters before/after, classifier frozen check, predictions, and accuracy.
Apply visual prompt, run frozen classifier, map logits to targets, compute loss, update only prompt or mapping parameters, and record trace evidence. Never mutate source classifier weights.
Run included tiny deterministic optimizer test.