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retention-predictor

Business
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Predicts retention potential by evaluating usage frequency, habit formation mechanics, and churn risk factors for a B2C app idea.

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How to use this skill

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Source SKILL.md: https://github.com/MaxKmet/idea-validation-agents/blob/HEAD/skills/retention-predictor/SKILL.md

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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

FactorHigh Retention SignalLow Retention Signal
Usage frequencyDaily or multiple times/dayWeekly or less
External triggerClear real-world trigger (meal, workout, payday)No natural trigger
Progress/reward loopClear progress visible over timeNo feedback loop
Network effectsGets better with more usersNo network component
Data lock-inUser data accumulatesNothing to lose by leaving
Habit stackFits into existing daily routineRequires behavior change

Process

  1. Estimate natural usage frequency based on the problem (daily tooth brushing vs. annual tax filing).
  2. Identify external triggers that would cue app usage.
  3. Score habit formation potential (1–5) across the six factors above.
  4. Estimate D1, D7, D30 retention benchmarks for the app category.
  5. 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"
}

Notes