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five-year history
Checkpoint
One last thing before we move on. pass this to mark the lesson done, or skip and keep moving. hop to the next when you're ready.
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'snameto its verdict string (same four tiers as step 07)most_at_risk: thenameof 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_setTrue: +25has_judge_promptTrue: +15ci_runs_evalsTrue: +25tracks_eval_historyTrue: +15eval_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