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Get Started Free →Design a detailed value proposition using a 6-part JTBD template — Who, Why, What before, How, What after, Alternatives. Use when creating a value proposition, analyzing customer value delivery, or articulating why customers should choose your product.
.claude/skills/phuryn-value-proposition/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 93% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 58% | 0% |
| case-09 | ✗→✓ | ▲ Improved | 11% | 0% |
| case-15 | ✗→✓ | ▲ Improved | 71% | 0% |
| case-19 | ✗→✓ | ▲ Improved | 59% | 0% |
You are a product strategist designing a clear value proposition for $ARGUMENTS.
Your task is to develop a comprehensive value proposition that articulates the customer value delivered by the product.
1. Who
2. Why (Problem)
3. What Before
4. How (Solution)
5. What After
6. Alternatives
This template vs Strategyzer's Value Proposition Canvas: Strategyzer's canvas (by Alexander Osterwalder) is widely used but has structural limitations. This 6-part JTBD template (by Paweł Huryn and Aatir Abdul Rauf) addresses them:
Use Strategyzer's Value Proposition Canvas when you need a detailed pains/gains decomposition for a mature product with complex customer needs. Use this 6-part template for clarity, speed, and actionable output.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 14,912 | 13,223 | -11% | 1 | 1 | 0% | 1,886 | 3,634 | +93% | 0 | 0 | — |
case-02 | pass→pass | 15,724 | 14,393 | -8% | 1 | 1 | 0% | 2,141 | 3,309 | +55% | 0 | 0 | — |
case-08 | fail→pass | 13,406 | 7,976 | -41% | 1 | 1 | 0% | 1,774 | 2,807 | +58% | 0 | 0 | — |
case-03 | pass→pass | 13,164 | 10,548 | -20% | 1 | 1 | 0% | 2,196 | 3,134 | +43% | 0 | 0 | — |
case-04 | pass→pass | 14,953 | 15,483 | +4% | 1 | 1 | 0% | 2,664 | 3,853 | +45% | 0 | 0 | — |
case-05 | pass→pass | 15,294 | 18,979 | +24% | 1 | 1 | 0% | 2,363 | 3,673 | +55% | 0 | 0 | — |
case-06 | pass→pass | 14,001 | 15,890 | +13% | 1 | 1 | 0% | 2,247 | 3,956 | +76% | 0 | 0 | — |
case-07 | pass→pass | 12,396 | 8,080 | -35% | 1 | 1 | 0% | 1,849 | 2,650 | +43% | 0 | 0 | — |
case-09 | fail→pass | 15,868 | 11,474 | -28% | 1 | 1 | 0% | 2,506 | 2,783 | +11% | 0 | 0 | — |
case-10 | pass→fail | 15,308 | 21,211 | +39% | 1 | 1 | 0% | 3,055 | 4,669 | +53% | 0 | 0 | — |
case-11 | pass→fail | 19,344 | 26,516 | +37% | 1 | 1 | 0% | 3,403 | 5,719 | +68% | 0 | 0 | — |
case-12 | pass→pass | 19,265 | 27,158 | +41% | 1 | 1 | 0% | 3,179 | 4,818 | +52% | 0 | 0 | — |
case-13 | pass→pass | 12,903 | 11,029 | -15% | 1 | 1 | 0% | 2,075 | 3,030 | +46% | 0 | 0 | — |
case-14 | pass→pass | 14,593 | 9,184 | -37% | 1 | 1 | 0% | 1,863 | 2,809 | +51% | 0 | 0 | — |
case-15 | fail→pass | 10,332 | 10,106 | -2% | 1 | 1 | 0% | 1,724 | 2,947 | +71% | 0 | 0 | — |
case-16 | fail→fail | 10,847 | 8,558 | -21% | 1 | 1 | 0% | 1,696 | 2,889 | +70% | 0 | 0 | — |
case-17 | pass→pass | 9,713 | 8,424 | -13% | 1 | 1 | 0% | 1,561 | 2,771 | +78% | 0 | 0 | — |
case-18 | pass→pass | 14,567 | 12,949 | -11% | 1 | 1 | 0% | 2,456 | 3,340 | +36% | 0 | 0 | — |
case-19 | fail→pass | 7,855 | 4,533 | -42% | 1 | 1 | 0% | 1,284 | 2,042 | +59% | 0 | 0 | — |
case-20 | pass→pass | 18,360 | 19,262 | +5% | 1 | 1 | 0% | 3,140 | 4,000 | +27% | 0 | 0 | — |
case-21 | pass→pass | 20,480 | 11,001 | -46% | 1 | 1 | 0% | 2,702 | 3,284 | +22% | 0 | 0 | — |
case-22 | fail→pass | 13,731 | 3,589 | -74% | 1 | 1 | 0% | 2,043 | 1,897 | -7% | 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 +18 percentage points is the difference between those two pass rates over the 22 comparable cases. 2 cases got worse with the skill loaded, and they are 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.