Desire Evaluator

Skill: desire-evaluator

Purpose

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.

Input

• Idea slug
• memory/ideas/<slug>/idea.md (app concept, emotional trigger, audience vocabulary, business_model)
• memory/market_insights/<niche>-*-<YYYY>-<MM>.md (pain language, complaint patterns; use all available platform files)
• Reference: memory/extra-context/core-human-desires.md (what drives core human desires)

Desire Dimensions

Scoring Rubric

Score each desire 1–5 by answering its three diagnostic questions. Each "yes" backed by evidence from idea.md or market_insights is one point above the base of 1; two yes answers give 3, three give 4; award 5 only when all three are yes AND the audience's own vocabulary names the desire (a hashtag, a recurring complaint, a search query).

Anchors: 1 = the desire is absent or incidental; 3 = present but not the reason to buy; 5 = the desire is what the audience says out loud about the problem.

B2B lane

When business_model is b2b-smb or b2b2c, the buyer is a business but the desires still belong to a person: the owner, operator, or manager who signs. Map the five desires this way and score them for that person:

Proceed Threshold

• If at least one desire scores ≥ 4: desire_strength_label is "strong" when the weighted mean (primary ×2, secondary ×1, rest ×0.5) is ≥ 3.5, else "moderate".
• If no desire scores ≥ 3: desire_strength_label is "weak", flag high churn risk in notes, and say so when presenting. Downstream, pricing applies no desire premium and retention treats habit formation as unlikely.
• virality_potential: "high" when Status or Belonging is the primary driver and scores 5; "medium" when either scores ≥ 4 or Curiosity scores 5; else "low".

Process

1. Read idea.md (problem, emotional trigger, audience vocabulary, business model) and the market_insights narratives for the audience's own words.
2. Score each desire 1–5 with the rubric, writing a one-sentence rationale per desire that cites the evidence (a quote, a hashtag, a complaint pattern, a stated cost).
3. Identify the primary and secondary drivers (highest two; break ties toward the one the audience names).
4. Compute desire_strength as the weighted mean above and assign the label.
5. Assign virality_potential.
6. In notes, state how the primary desire behaves commercially: is it continuous or episodic, does it convert to revenue or cost for the buyer, and what re-triggers it. This paragraph is what retention-predictor and pricing-and-wtp read.

Output

Write to memory/ideas/<slug>/desire_scores.json:

{
  "idea_slug": "",
  "evaluated_at": "YYYY-MM-DD",
  "lane": "b2c | b2b",
  "scores": {
    "survival": 0,
    "status": 0,
    "belonging": 0,
    "control": 0,
    "curiosity": 0
  },
  "score_rationale": {
    "survival": "",
    "status": "",
    "belonging": "",
    "control": "",
    "curiosity": ""
  },
  "primary_driver": "",
  "secondary_driver": "",
  "desire_strength": 0,
  "desire_strength_label": "strong | moderate | weak",
  "virality_potential": "high | medium | low",
  "notes": ""
}

Notes

• retention-predictor reads primary_driver, desire_strength_label and notes: Survival and Control drivers raise habit-formation potential; a Status driver with a recurring external judgement moment (client review, audit) is the strongest B2B retention signal; Curiosity alone predicts novelty churn.
• pricing-and-wtp reads primary_driver for the desire-premium multiplier and applies none when the label is "weak".