How evals went from research curiosity to the only thing that ships — a five-year history — step 8 of 8

Final drill. Synthesize the chapter's history into one function: triage_teams(teams) that takes a list of team profiles and returns a dict with two fields:

• verdicts: a dict mapping each team's name to its verdict string (same four tiers as step 07)
• most_at_risk: the name of the team with the LOWEST readiness score (the team most likely to break on the next model launch)

Score each team using the same rules as step 07:

• has_test_set True: +25
• has_judge_prompt True: +15
• ci_runs_evals True: +25
• tracks_eval_history True: +15
• eval_count_per_feature >= 20: +20

Verdict tiers: >=80 "eval-mature", >=50 "eval-aware", >=20 "eval-curious", else "vibes era".

On ties for lowest score, return the FIRST tied team (Python's min with key= preserves stable order).

Five teams run. Expected output:

verdicts: {'CursorClone': 'eval-mature', 'SeriesBAI': 'eval-mature', 'SeedAI': 'eval-curious', 'ConsultingShop': 'eval-curious', 'PromptShopCo': 'vibes era'}
most at risk: PromptShopCo