What's Missing in Standard Supabase Vector Architecture
Supabase Vector integrates AI, but AI APIs remain unused because there's no automated tracking to celebrate when users first achieve value through AI features.
Standard Supabase Vector Flow vs Optimized Skene Flow
User has access to Supabase Vector features
Features available but usage not monitored
No activation prompts for unused features
Monitor Supabase Vector usage patterns and feature adoption
Identify users who haven't used key features
Trigger feature discovery emails with examples and use cases
Track feature adoption and send advanced usage guides
Visual comparison of the flows:
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How Skene Fixes This
We created a Skene Prompt that installs AI activation for Supabase Vector. It monitors AI API usage and automatically sends feature discovery emails to users who haven't tried AI features after 7 days.
Implementation Comparison
Using Skene Infrastructure
Install via Prompt
@task: Initialize Skene.
@action: Analyze my local code, validating subscription via `npx skene login`, and generate `skene.config.ts` to implement the AI Activation pattern for Supabase Vector.Copy Skene Prompt for Cursor
Generated skene.config.ts
// skene.config.ts - The Automated Way
import { defineLoop } from '@skene/sdk';
export default defineLoop({
type: 'retention',
opinion: 'Detect dormant users and trigger personalized re-engagement campaigns',
steps: [
{
trigger: {
type: 'schedule',
cron: '0 9 * * *' // Daily at 9 AM
},
condition: {
type: 'query',
query: `SELECT * FROM users WHERE last_activity_at < NOW() - INTERVAL '7 days' AND re_engagement_sent = false`,
timeout: '10m'
},
action: {
type: 'email',
provider: 'resend',
template: 're_engagement',
personalization: {
name: '{{user.name}}',
lastActivity: '{{user.last_activity_at}}'
}
}
}
],
recovery: {
retries: 3,
backoff: 'exponential'
}
});