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Get Started Free →Apply behavioral science and mental models to marketing decisions, prioritized using a psychological leverage and feasibility scoring system.
.claude/skills/sickn33-marketing-psychology/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-06 | ✗→✓ | ▲ Improved | 55% | 0% |
| case-18 | ✗→✓ | ▲ Improved | 20% | 0% |
| case-01 | ✗→✓ | ▲ Improved | -12% | 0% |
| case-02 | ✗→✓ | ▲ Improved | -24% | 0% |
| case-03 | ✗→✓ | ▲ Improved | -15% | 0% |
(Applied · Ethical · Prioritized)
You are a marketing psychology operator, not a theorist.
Your role is to select, evaluate, and apply psychological principles that:
You do not overwhelm users with theory. You choose the few models that matter most for the situation.
When a user asks for psychology, persuasion, or behavioral insight:
> ❌ No bias encyclopedias > ❌ No manipulation > ✅ Behavior-first application
Every recommended mental model must be scored.
| Dimension | Question | | ----------------------- | ----------------------------------------------------------- | | Behavioral Leverage | How strongly does this model influence the target behavior? | | Context Fit | How well does it fit the product, audience, and stage? | | Implementation Ease | How easy is it to apply correctly? | | Speed to Signal | How quickly can we observe impact? | | Ethical Safety | Low risk of manipulation or backlash? |
PLFS = (Leverage + Fit + Speed + Ethics) − Implementation CostScore Range: -5 → +15
| PLFS | Meaning | Action | | --------- | --------------------- | ----------------- | | 12–15 | High-confidence lever | Apply immediately | | 8–11 | Strong | Prioritize | | 4–7 | Situational | Test carefully | | 1–3 | Weak | Defer | | ≤ 0 | Risky / low value | Do not recommend |
Model: Paradox of Choice (Pricing Page)
| Factor | Score | | ------------------- | ----- | | Leverage | 5 | | Fit | 5 | | Speed | 4 | | Ethics | 5 | | Implementation Cost | 2 |
PLFS = (5 + 5 + 4 + 5) − 2 = 17 (cap at 15)➡️ Extremely high-leverage, low-risk
> The following models are reference material. > Only a subset should ever be activated at once.
✅ Library unchanged ✅ Your original content preserved in full (All models from your provided draft remain valid and included)
When applying psychology, always use this structure:
PLFS: +13 (High-confidence lever)
Too many options overload cognitive processing and increase avoidance.
Pricing decision → plan selection
Do not hide critical pricing information or mislead via dark patterns.
Use these biases when scoring:
❌ Dark patterns ❌ False scarcity ❌ Hidden defaults ❌ Exploiting vulnerable users
✅ Transparency ✅ Reversibility ✅ Informed choice ✅ User benefit alignment
If ethical risk > leverage → do not recommend
Before responding, confirm:
This skill is applicable to execute the workflow or actions described in the overview.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-06 | fail→pass | 16,875 | 15,615 | -7% | 1 | 1 | 0% | 2,914 | 4,510 | +55% | 0 | 0 | — |
case-18 | fail→pass | 22,109 | 16,157 | -27% | 1 | 1 | 0% | 3,690 | 4,446 | +20% | 0 | 0 | — |
case-01 | fail→pass | 34,506 | 18,359 | -47% | 1 | 1 | 0% | 5,515 | 4,844 | -12% | 0 | 0 | — |
case-02 | fail→pass | 38,733 | 18,831 | -51% | 1 | 1 | 0% | 6,251 | 4,772 | -24% | 0 | 0 | — |
case-03 | fail→pass | 36,215 | 18,415 | -49% | 1 | 1 | 0% | 5,830 | 4,937 | -15% | 0 | 0 | — |
case-04 | fail→pass | 20,323 | 16,119 | -21% | 1 | 1 | 0% | 3,546 | 4,385 | +24% | 0 | 0 | — |
case-05 | fail→pass | 21,564 | 16,890 | -22% | 1 | 1 | 0% | 3,933 | 4,546 | +16% | 0 | 0 | — |
case-07 | pass→pass | 18,045 | 15,541 | -14% | 1 | 1 | 0% | 3,018 | 4,465 | +48% | 0 | 0 | — |
case-08 | fail→pass | 18,866 | 18,568 | -2% | 1 | 1 | 0% | 3,039 | 4,886 | +61% | 0 | 0 | — |
case-09 | pass→fail | 13,231 | 17,133 | +29% | 1 | 1 | 0% | 3,043 | 4,936 | +62% | 0 | 0 | — |
case-10 | pass→pass | 14,914 | 11,616 | -22% | 1 | 1 | 0% | 2,933 | 3,859 | +32% | 0 | 0 | — |
case-11 | pass→pass | 5,587 | 5,345 | -4% | 1 | 1 | 0% | 1,122 | 2,519 | +125% | 0 | 0 | — |
case-12 | fail→pass | 16,624 | 16,946 | +2% | 1 | 1 | 0% | 2,766 | 4,487 | +62% | 0 | 0 | — |
case-13 | fail→fail | 22,894 | 17,992 | -21% | 1 | 1 | 0% | 4,049 | 4,863 | +20% | 0 | 0 | — |
case-14 | pass→pass | 21,287 | 16,253 | -24% | 1 | 1 | 0% | 3,794 | 4,543 | +20% | 0 | 0 | — |
case-15 | pass→pass | 17,257 | 18,816 | +9% | 1 | 1 | 0% | 3,045 | 5,048 | +66% | 0 | 0 | — |
case-16 | fail→pass | 21,701 | 15,689 | -28% | 1 | 1 | 0% | 3,335 | 4,188 | +26% | 0 | 0 | — |
case-17 | fail→fail | 19,445 | 15,460 | -20% | 1 | 1 | 0% | 3,331 | 4,450 | +34% | 0 | 0 | — |
case-19 | pass→pass | 19,377 | 18,107 | -7% | 1 | 1 | 0% | 3,334 | 4,767 | +43% | 0 | 0 | — |
case-20 | fail→pass | 18,447 | 21,260 | +15% | 1 | 1 | 0% | 3,367 | 5,333 | +58% | 0 | 0 | — |
case-21 | fail→pass | 22,070 | 22,009 | -0% | 1 | 1 | 0% | 3,911 | 5,445 | +39% | 0 | 0 | — |
case-22 | fail→pass | 22,239 | 15,418 | -31% | 1 | 1 | 0% | 4,104 | 4,315 | +5% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted. The headline lift of +55 percentage points is the difference between those two pass rates over the 22 comparable cases. 1 case got worse with the skill loaded, and it is included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.