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Get Started Free →Brainstorm 5 unique, memorable product names with rationale aligned to brand values and target audience. Use when naming a new product, rebranding, or exploring product name ideas.
.claude/skills/phuryn-product-name/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | 19% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 63% | 0% |
| case-04 | ✗→✓ | ▲ Improved | 90% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 41% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 57% | 0% |
Brainstorm unique, memorable product names with rationale aligned to brand values and target audience. Use when naming a new product, rebranding, or exploring name options that strengthen your brand positioning.
You are an experienced branding consultant with expertise in product naming, brand architecture, and market positioning.
Based on the following company and product context: $ARGUMENTS
Suggest five unique, memorable product names that align with the company's brand values, target audience, and market positioning.
For each name suggestion, provide:
Prioritize names that are:
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 6,060 | 9,044 | +49% | 1 | 1 | 0% | 1,189 | 1,884 | +58% | 0 | 0 | — |
case-02 | fail→pass | 11,510 | 12,334 | +7% | 1 | 1 | 0% | 1,910 | 2,282 | +19% | 0 | 0 | — |
case-03 | fail→pass | 10,900 | 19,844 | +82% | 1 | 1 | 0% | 1,807 | 2,952 | +63% | 0 | 0 | — |
case-04 | fail→pass | 9,622 | 12,734 | +32% | 1 | 1 | 0% | 1,285 | 2,444 | +90% | 0 | 0 | — |
case-05 | fail→fail | 8,221 | 11,402 | +39% | 1 | 1 | 0% | 1,354 | 1,906 | +41% | 0 | 0 | — |
case-06 | fail→pass | 10,897 | 21,881 | +101% | 1 | 1 | 0% | 1,868 | 2,642 | +41% | 0 | 0 | — |
case-07 | fail→pass | 10,263 | 13,984 | +36% | 1 | 1 | 0% | 1,666 | 2,617 | +57% | 0 | 0 | — |
case-08 | fail→pass | 12,697 | 17,044 | +34% | 1 | 1 | 0% | 1,917 | 3,239 | +69% | 0 | 0 | — |
case-09 | fail→pass | 7,357 | 16,006 | +118% | 1 | 1 | 0% | 836 | 3,153 | +277% | 0 | 0 | — |
case-10 | fail→pass | 8,485 | 11,685 | +38% | 1 | 1 | 0% | 1,470 | 2,249 | +53% | 0 | 0 | — |
case-11 | fail→pass | 11,296 | 24,632 | +118% | 1 | 1 | 0% | 1,886 | 3,650 | +94% | 0 | 0 | — |
case-12 | fail→pass | 10,968 | 15,701 | +43% | 1 | 1 | 0% | 1,831 | 3,010 | +64% | 0 | 0 | — |
case-13 | pass→pass | 13,828 | 12,565 | -9% | 1 | 1 | 0% | 1,855 | 2,441 | +32% | 0 | 0 | — |
case-14 | fail→pass | 10,799 | 18,373 | +70% | 1 | 1 | 0% | 1,783 | 2,734 | +53% | 0 | 0 | — |
case-15 | pass→pass | 11,990 | 15,635 | +30% | 1 | 1 | 0% | 2,041 | 2,823 | +38% | 0 | 0 | — |
case-16 | fail→pass | 15,081 | 13,449 | -11% | 1 | 1 | 0% | 1,855 | 2,642 | +42% | 0 | 0 | — |
case-17 | fail→pass | 16,728 | 17,212 | +3% | 1 | 1 | 0% | 2,177 | 3,469 | +59% | 0 | 0 | — |
case-18 | fail→fail | 16,243 | 17,229 | +6% | 1 | 1 | 0% | 1,986 | 3,123 | +57% | 0 | 0 | — |
case-19 | fail→pass | 10,248 | 16,871 | +65% | 1 | 1 | 0% | 1,770 | 2,426 | +37% | 0 | 0 | — |
case-20 | pass→pass | 9,442 | 7,589 | -20% | 1 | 1 | 0% | 1,854 | 1,707 | -8% | 0 | 0 | — |
case-21 | pass→pass | 19,723 | 15,591 | -21% | 1 | 1 | 0% | 2,618 | 2,597 | -1% | 0 | 0 | — |
case-22 | pass→pass | 18,165 | 13,113 | -28% | 1 | 1 | 0% | 3,093 | 2,647 | -14% | 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 +64 percentage points is the difference between those two pass rates over the 22 comparable cases.
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.