SKILL.md
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:
- 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.
- 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. - A repeatable task the corpus actually executes end to end.