What's Missing in Standard MongoDB Atlas Architecture
MongoDB Atlas manages data correctly, but lacks the infrastructure to track user activity and automatically segment users by engagement levels without custom implementation.
Standard MongoDB Atlas Flow vs Optimized Skene Flow
User completes onboarding
User activity tracked manually (if at all)
No automated re-engagement when activity drops
Monitor your database 'events' table for user activity patterns via standard features
Detect when user becomes dormant (declining activity week-over-week)
Trigger re-engagement email sequence with personalized content
Track re-engagement success and adjust campaigns
Visual comparison of the flows:
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How Skene Fixes This
The Skene infrastructure for MongoDB Atlas monitors your database event patterns and identifies when users hit milestones or become inactive. It then automatically sends targeted re-engagement sequences based on their last activity.
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 Retention Loop pattern for MongoDB Atlas.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'
}
});