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: 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 share a corpus; only one of them is the result here. Report completion, not word count.

Phase 0: the measurement gate, check this first

This skill cannot run without a way to observe behavior. Check for all three, and name whichever is missing:

1. A run archive or a harness that produces one, per-run logs carrying the tool-call trace, a terminal marker, token counts, and the final message.
2. A build selector, the harness can point a run at a specific source checkout of the corpus (a --plugin-dir-style override, a configurable skills path, an env var), so two builds are comparable under one runner.
3. A repeatable task the corpus actually executes end to end.

If any is missing, stop and say so, naming what to build. Do not fall back to a static audit and present it as retuning: an audit can say what looks cuttable and never whether cutting helped. An audit-only pass is a legitimate thing to want; it is a different request.

State the target model and the harness you found before continuing.

The phases