Skin in the Game
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A developer shares how they built a multi-layer quality control system to ship 30+ AI-generated extensions reliably. The system combines a CLAUDE.md rules file (populated from real bugs), an adversarial automated code review that runs before and during CI, and a workflow skill that enforces the full PR lifecycle. Three concrete bug classes are documented: instance-name collisions, pagination dishonesty, and rate-limit fragility. The key insight is that AI agents produce locally correct but globally fragile code, and only a human with deep daily context can catch architectural blind spots. Rules accumulate from real failures, not speculation, and the system improves through continuous use.
