The measurement showed the USER-FACING Opus score is already well-calibrated — the real bug was curate
FAILING and dropping to the bad white-box fallback:
- curate.py: _salvage_kept() brace-scans + parses each kept object independently, so one bad char or a
max-tokens truncation no longer drops the whole shortlist. max_tokens 4000 → 8000 (28 sifted jobs ×
full report cards overflowed). This fixed the sales case (was failing → fallback).
- curate.py: calibration nudge in the prompt — a genuinely strong current-state match is a real low-80s,
partial/stretch 60s-70s; don't under-sell strong matches into the 60s (without inflating weak ones).
- normalize.py + search.py: _seniority_from_title() infers seniority from the title (conservative) when a
board omits it, applied centrally in the sweep → the experience factor + Opus brief aren't blind.
Verified (live Opus on the regression cases): tech MAE 9.8→4.5, sales FAILED→MAE 5.3 (kept 13),
ops 4.2→3.3 — user-facing scores now MAE 3-5 with zero curate failures. 31 tests pass.
2b2d67b4c8