You've probably tried asking an AI to review your code. The feedback is often generic:
These suggestions aren't wrong, but they're not specific enough to be actionable. They sound like a junior developer reading a best practices blog post, not a senior engineer who knows your stack.
The difference between useful and useless code review feedback comes down to context. A senior engineer reviewing a Next.js app knows:
use server functions shouldn't expose internal IDsgenerateStaticParams for ISRThis knowledge is structured — it maps to specific frameworks, specific patterns, and specific impact levels. An AI reviewer needs this same structure to give useful feedback.
Each BeforeMerge rule is a structured document that gives AI reviewers the context they need:
title: Enable RLS on Every Table
impact: CRITICAL
section: security
skill: supabase-nextjsInstead of generic advice, the AI can now say:
"This migration creates a
user_profilestable without enabling Row Level Security. This is a CRITICAL security issue — any authenticated user can read all rows. AddALTER TABLE user_profiles ENABLE ROW LEVEL SECURITYand create appropriate policies."
That's the difference between noise and signal.
Rather than trying to encode every possible rule into a model's training data, we maintain a living knowledge base that:
Browse the rule catalog to see what's available. If you're using Supabase with Next.js, start with the supabase-nextjs skill — it has rules covering security, performance, and architectural best practices that are commonly missed in review.