MaxKmet Skill: retention-predictor
Purpose
Retention determines LTV. An app that churns users in week 1 can't build a business regardless of acquisition. This skill evaluates how sticky the idea is structurally — not based on feature lists, but on the underlying usage pattern and habit formation potential.
Input
- Idea slug
- App concept description
memory/ideas/<slug>/desire_scores.json(desire strength informs habit potential)memory/ideas/<slug>/user_extraction.json(usage frequency from pain map)
Evaluation Factors
| Factor | High Retention Signal | Low Retention Signal |
|---|---|---|
| Usage frequency | Daily or multiple times/day | Weekly or less |
| External trigger | Clear real-world trigger (meal, workout, payday) | No natural trigger |
| Progress/reward loop | Clear progress visible over time | No feedback loop |
| Network effects | Gets better with more users | No network component |
| Data lock-in | User data accumulates | Nothing to lose by leaving |
| Habit stack | Fits into existing daily routine | Requires behavior change |
Process
- Estimate natural usage frequency based on the problem (daily tooth brushing vs. annual tax filing).
- Identify external triggers that would cue app usage.
- Score habit formation potential (1–5) across the six factors above.
- Estimate D1, D7, D30 retention benchmarks for the app category.
- Flag high churn risk factors.
Output
Write to memory/ideas/<slug>/retention.json:
{
"natural_usage_frequency": "multiple daily | daily | weekly | monthly | infrequent",
"external_trigger": "",
"habit_formation_score": 0,
"churn_risk_factors": [],
"estimated_retention": {
"d1": 0,
"d7": 0,
"d30": 0
},
"churn_risk": "low | medium | high",
"retention_verdict": "sticky | moderate | disposable"
}