cac-modeler
Models LTV, CAC by channel, LTV:CAC ratios, and payback period for an indie developer. Uses…
Scores the strength of core human desire motivations (survival, status, belonging, control, curiosity) for a given app idea to predict user pull and retention potential.
$ npx -y skills add MaxKmet/idea-validation-agents --skill desire-evaluator --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/desire-evaluatorContext preview
The summary Claude sees to decide when to auto-load this skill.
Scores the strength of core human desire motivations (survival, status, belonging, control, curiosity) for a given app idea to predict user pull and retention potential.
name: desire-evaluator description: Scores the strength of core human desire motivations (survival, status, belonging, control, curiosity) for a given app idea to predict user pull and retention potential.
<!-- version: 0.1.0 | outputs: memory/ideas/<slug>/desire_scores.json -->
Apps that tap into primal human desires outperform apps that only solve functional problems. This skill scores how strongly an app idea connects to core motivational drivers, which predicts organic virality, retention, and pricing power.
| Dimension | Description | Example App | |---|---|---| | Survival | Health, safety, financial security | Calorie tracker, budgeting app | | Status | Looking good, achieving, winning | Fitness leaderboard, portfolio tracker | | Belonging | Community, connection, not being alone | Group savings, running clubs | | Control | Mastery, autonomy, reducing chaos | Task manager, habit tracker | | Curiosity | Learning, discovery, novelty | Language app, quiz game |
<!-- TODO: Add scoring rubric with question bank per dimension --> <!-- TODO: Define what score justifies proceeding (e.g., at least one dimension ≥ 4) -->
1. Score each dimension 1–5 based on how directly the app taps into it. 2. Identify the primary and secondary desire drivers. 3. Compute overall desire strength (weighted average). 4. Flag if no dimension scores ≥ 3 (weak desire signal → high churn risk).
Write to `memory/ideas/<slug>/desire_scores.json`:
{
"scores": {
"survival": 0,
"status": 0,
"belonging": 0,
"control": 0,
"curiosity": 0
},
"primary_driver": "",
"secondary_driver": "",
"desire_strength": 0,
"desire_strength_label": "strong | moderate | weak",
"virality_potential": "high | medium | low",
"notes": ""
}<!-- TODO: Add cross-reference with retention-predictor — desire strength should inform habit score -->
AI agents that act as your personal venture analyst - from startup idea brainstorming to full validation and go-to-market strategy. Built for developers who'd rather validate in 10 minutes than regret in six months. Powered by Claude Code, OpenAI Codex, and Cursor.
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