Validate AI-Generated Supabase Code (Teach Your Coding Agent to Prove It)
AI can generate Supabase code in seconds — but can it prove it’s correct?
In this deCoded episode, we validate AI-generated Supabase inserts, Row Level Security (RLS), and TypeScript schema drift using an evidence-based workflow.
We walk through three common failure patterns in AI-generated Supabase projects:
• Silent inserts (no returned row evidence)
• False RLS validation (service role illusion)
• Schema drift (database ≠ generated TypeScript types)
Then we install a Supabase validation skill that enforces:
Fail → Fix → Prove → Show Diff
If there’s no evidence, it’s not done.
⸻
Install the Validation Skill:
npx skills add visaoenhance/supabase-debug-playground
GitHub Repo:
https://github.com/visaoenhance/supab...
⸻
Chapters:
00:00 – The Problem with AI-Generated Supabase Code
01:05 – Current vs Evidence-Based AI Workflow
02:00 – Silent Insert Failure in Supabase
03:00 – Row Level Security (RLS) Validation Across Roles
04:15 – Schema Drift Detection in Supabase + TypeScript
05:15 – Install the Supabase Validation Skill
⸻
If you’re building production systems with Supabase and AI coding agents, validation must be enforced.
Subscribe for more AI engineering breakdowns.
#supabase #aivideo #coding #cursor #typescript