Evolution Review (data-driven portfolio selection)

Turn monthly portfolio reviews from "gut feeling + sunk-cost attachment" into natural selection: judge by outcome data, fix winners into default recipes, kill losers without sentiment, and budget exploration. Distilled from a real 4.5-month AI-music business (307 releases, ¥95 total revenue) where it ran as an evolution mechanism — the business failed, the mechanism survived.

Why this skill exists

• Prior lock-in: creators keep funding their favorite direction regardless of data (sunk cost + self-image)
• Fake reviews: most retrospectives produce vague "direction A feels right" conclusions — no kill decisions, no budget reallocation, next month repeats
• No extinction = no selection: a review without kill decisions is a diary; a review without fixation (distilling what won) is just venting
• Single-month whiplash: one hit or one flop triggering a direction change = being led by noise

Core mechanism (six steps)