What's Missing in Standard Helicone Architecture
While Helicone offers powerful AI features, there's no system to identify users who haven't tried AI functionality and guide them to discover value.
Standard Helicone Flow vs Optimized Skene Flow
Standard Flow
1
User has access to Helicone features
2
Features available but usage not monitored
3
No activation prompts for unused features
Skene Flow
1
Monitor Helicone usage patterns and feature adoption
2
Identify users who haven't used key features
3
Trigger feature discovery emails with examples and use cases
4
Track feature adoption and send advanced usage guides
Visual comparison of the flows:
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How Skene Fixes This
The Skene infrastructure for Helicone monitors AI feature usage and automatically celebrates when users first achieve value through AI, while sending discovery prompts to inactive users.
Implementation Comparison
Using Skene Infrastructure
Auto-managed state. Installed via Prompt.
Install via Prompt
cursor.mdc
@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 Helicone.Copy Skene Prompt for Cursor
Generated skene.config.ts
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'
}
});