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Get Started Free →Create an end-to-end customer journey map with stages, touchpoints, emotions, pain points, and opportunities. Use when mapping the customer experience, identifying friction points, improving onboarding, or visualizing the user journey.
.claude/skills/phuryn-customer-journey-map/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-04 | ✗→✓ | ▲ Improved | 81% | 0% |
| case-01 | ✗→✓ | ▲ Improved | 53% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 13% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 82% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 1% | 0% |
Map the end-to-end customer experience from awareness through advocacy, identifying emotions, pain points, and improvement opportunities at each stage.
You are creating a customer journey map for $ARGUMENTS.
If the user provides files (interview transcripts, survey data, analytics, support tickets, or existing journey maps), read them first. Use web search to understand the product if a URL is provided.
| Stage | Description | |---|---| | Awareness | How do they first learn about the product? | | Consideration | What do they evaluate? What alternatives do they compare? | | Acquisition | How do they sign up or purchase? | | Onboarding | First experience with the product — time to value | | Engagement | Regular usage — building habits | | Retention | What keeps them coming back? What might cause churn? | | Advocacy | When and why do they recommend the product to others? |
| Stage | Touchpoint | User Action | Emotion | Pain Point | Opportunity | |---|---|---|---|---|---|
Think step by step. Save as a markdown document. For visual journey maps, suggest the user create one in Miro or FigJam using this analysis as the foundation.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-04 | fail→pass | 23,219 | 43,062 | +85% | 1 | 1 | 0% | 3,822 | 6,914 | +81% | 0 | 0 | — |
case-01 | fail→pass | 25,392 | 44,595 | +76% | 1 | 1 | 0% | 4,360 | 6,691 | +53% | 0 | 0 | — |
case-02 | fail→pass | 24,294 | 26,572 | +9% | 1 | 1 | 0% | 4,407 | 4,989 | +13% | 0 | 0 | — |
case-03 | fail→fail | 23,976 | 36,988 | +54% | 1 | 1 | 0% | 3,497 | 6,928 | +98% | 0 | 0 | — |
case-05 | fail→pass | 19,671 | 34,842 | +77% | 1 | 1 | 0% | 3,415 | 6,205 | +82% | 0 | 0 | — |
case-06 | fail→fail | 22,630 | 30,885 | +36% | 1 | 1 | 0% | 3,698 | 5,359 | +45% | 0 | 0 | — |
case-07 | fail→pass | 25,755 | 22,989 | -11% | 1 | 1 | 0% | 4,311 | 4,373 | +1% | 0 | 0 | — |
case-08 | fail→fail | 20,664 | 31,376 | +52% | 1 | 1 | 0% | 3,562 | 6,118 | +72% | 0 | 0 | — |
case-09 | fail→fail | 29,768 | 21,709 | -27% | 1 | 1 | 0% | 3,926 | 4,239 | +8% | 0 | 0 | — |
case-10 | fail→pass | 21,419 | 28,321 | +32% | 1 | 1 | 0% | 3,520 | 5,608 | +59% | 0 | 0 | — |
case-11 | fail→pass | 22,649 | 34,238 | +51% | 1 | 1 | 0% | 3,612 | 6,488 | +80% | 0 | 0 | — |
case-12 | fail→pass | 27,955 | 32,674 | +17% | 1 | 1 | 0% | 3,830 | 6,182 | +61% | 0 | 0 | — |
case-13 | fail→pass | 19,571 | 34,966 | +79% | 1 | 1 | 0% | 3,307 | 5,416 | +64% | 0 | 0 | — |
case-14 | fail→pass | 27,808 | 28,777 | +3% | 1 | 1 | 0% | 3,136 | 5,282 | +68% | 0 | 0 | — |
case-15 | fail→pass | 23,248 | 31,673 | +36% | 1 | 1 | 0% | 3,798 | 5,907 | +56% | 0 | 0 | — |
case-16 | fail→fail | 19,331 | 28,424 | +47% | 1 | 1 | 0% | 3,136 | 5,568 | +78% | 0 | 0 | — |
case-17 | fail→fail | 20,423 | 37,449 | +83% | 1 | 1 | 0% | 3,360 | 6,883 | +105% | 0 | 0 | — |
case-18 | pass→fail | 21,911 | 39,352 | +80% | 1 | 1 | 0% | 3,342 | 6,239 | +87% | 0 | 0 | — |
case-19 | fail→pass | 24,805 | 36,722 | +48% | 1 | 1 | 0% | 3,134 | 6,132 | +96% | 0 | 0 | — |
case-20 | pass→pass | 27,887 | 29,234 | +5% | 1 | 1 | 0% | 4,775 | 6,903 | +45% | 0 | 0 | — |
case-21 | pass→pass | 18,088 | 15,742 | -13% | 1 | 1 | 0% | 2,850 | 3,341 | +17% | 0 | 0 | — |
case-22 | pass→pass | 16,102 | 21,587 | +34% | 1 | 1 | 0% | 2,878 | 4,438 | +54% | 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 +50 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.