Retune a Corpus for a New Model

A corpus that degrades on a new model is a measurement problem before it is a writing problem. Reading the prose and rewriting what looks wrong produces a plausible fix list and no way to know whether any item mattered.

Outcome: a corpus whose measured behavior on the target model clears a bar registered before any change, with the regression classes removed and each removal attributable.

Done: the bar is cleared, or the run reports the specific claim it could not support. A green test suite is not done: it proves nothing broke, not that behavior improved.

Non-goal: word reduction. Leanness and performance are separate programs that happen to share a corpus, and only one of them is the result. Report completion, not word count.

Setup

Run this once at the start of this invocation, before any subagent dispatch, and follow the directives it prints — except where one conflicts with this skill's own rules on asking the user questions, whether those rules are scoped to a non-interactive mode or apply in every mode, in which case this skill's rules win and no blocking question is asked. Run the fence exactly as written, as its own command: do not pipe or filter it (no head, tail, or grep), do not truncate its output, and do not bundle it into a batch with other commands. Its output opens with a === skill context header and ends with CE_CONTEXT_END; if you received one of those lines without the other, the output was truncated — rerun the fence verbatim once. That recovery is the only rerun: otherwise do not rerun it within the same invocation; a later invocation of this or any other skill runs its own. If no Node runtime is available the skill proceeds unchanged.