forked from bchanot/claude
Bastien Chanot
4818c6116f
/harden has a real scale (SKILL.md:435 — Critique -15, Haute -8, Moyenne -3, Basse -1, clamp [0,100]). /seo had none: every axis was felt, so two runs over identical code could disagree. That is a credibility problem on its own, and /client-handover gates on 17/20 — a wobbling number makes the gate arbitrary. H2 sharpened it: now that drift reports what actually changed, a score moving on its own is visibly noise. The split is the whole point. WHICH findings exist and how severe each is stays the LLM's judgement — irreducible, and I am not pretending otherwise. The arithmetic stops being judgement: same findings in, same score out. Same principle as grouping cannibalisation rows in the engine rather than handing a model 1000 rows to add up. Reuses /harden's scale, /5 into /20, so the family speaks one vocabulary instead of two. Two things it makes real that were prose: - **N/A is not a zero.** R2 (client-rendered on-page) and I1 (unauditable off-page) both mandate excluding an axis and renormalising the rest. Both left that arithmetic to the model. Now the engine does it and refuses to let N/A behave like a zero — verified: all-20 axes with two N/A still yields global 20.0, not a dragged-down mean. - **Prevalence.** affected/sampled shift severity ONE step (>=50% escalates, a single page de-escalates). A defect on 1 of 12 pages is not the defect on 12 of 12, and flattening the two is part of what made the old numbers move. Malformed input is an error, never a silently wrong number — unlike the fetch verbs, a degrade here would mean bad input, not a network fact. Unknown severity and unknown profile both rejected, tested. Verified: hand-checkable arithmetic (haute+moyenne = 100-11 = 89 → 17.8; critique+haute = 77 → 15.4), identical global across repeated runs, weights renormalised to sum 1.0 with two axes N/A. seo-data 155 -> 167 pass, 0 fail; full suite green; shellcheck + py_compile clean.