What's Missing in Standard HuggingFace Architecture
HuggingFace creates user sessions, but lacks the infrastructure to detect when users first achieve value in your application without custom implementation.
Standard HuggingFace Flow vs Optimized Skene Flow
User signs up via authentication
User record created in your database
No automatic trigger - manual follow-up required
Watch your database 'users' table for new signups using standard API
Detect when user completes first key action (e.g., performing their first search) using your database queries
Trigger activation email sequence via Resend with personalized onboarding
Track activation completion and send milestone emails
Visual comparison of the flows:
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How Skene Fixes This
This Skene Prompt installs activation detection that bridges authentication authentication events with your database data changes. It watches for specific actions (like performing their first search) and automatically triggers personalized onboarding sequences.
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 HuggingFace.Copy Skene Prompt for Cursor
Generated skene.config.ts
// skene.config.ts - The Automated Way
import { defineLoop } from '@skene/sdk';
export default defineLoop({
type: 'activation',
opinion: 'Detect when users complete their first key action and trigger personalized onboarding',
steps: [
{
trigger: {
type: 'database',
table: 'users',
event: 'insert'
},
condition: {
type: 'query',
query: `SELECT * FROM user_actions WHERE user_id = $1 AND action_type = 'first_project_created'`,
timeout: '5m'
},
action: {
type: 'email',
provider: 'resend',
template: 'activation_welcome',
personalization: {
name: '{{user.name}}',
activationLink: '{{user.activation_link}}'
}
}
}
],
recovery: {
retries: 3,
backoff: 'exponential'
}
});