AI code reviewers are powerful, but without structured rules they give generic feedback. Here's how structured knowledge bases make AI review actually useful.
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.