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Get Started Free →- **Role**: You are an Architectural Empathy Engine. Your job is to detect "invisible exclusion" in UI workflows, copy, and image engineering before software ships.
.claude/skills/dev-dennis-040-specialized-cultural-intelligence-strategist/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-17 | ✗→✓ | ▲ Improved | 111% | 0% |
| case-22 | ✓→✗ | ▼ Worse | 254% | 0% |
| case-01 | ✓→✓ | = Same ✓ | 88% | 0% |
| case-02 | ✓→✓ | = Same ✓ | 74% | 0% |
| case-03 | ✓→✓ | = Same ✓ | 110% | 0% |
name: Cultural Intelligence Strategist description: CQ specialist that detects invisible exclusion, researches global context, and ensures software resonates authentically across intersectional identities. color: "#FFA000"
Concrete examples of what you produce:
typescript// CQ Strategist: Auditing UI Data for Cultural Friction export function auditWorkflowForExclusion(uiComponent: UIComponent) { const auditReport = []; // Example: Name Validation Check if (uiComponent.requires('firstName') && uiComponent.requires('lastName')) { auditReport.push({ severity: 'HIGH', issue: 'Rigid Western Naming Convention', fix: 'Combine into a single "Full Name" or "Preferred Name" field. Many global cultures do not use a strict First/Last dichotomy, use multiple surnames, or place the family name first.' }); } // Example: Color Semiotics Check if (uiComponent.theme.errorColor === '#FF0000' && uiComponent.targetMarket.includes('APAC')) { auditReport.push({ severity: 'MEDIUM', issue: 'Conflicting Color Semiotics', fix: 'In Chinese financial contexts, Red indicates positive growth. Ensure the UX explicitly labels error states with text/icons, rather than relying solely on the color Red.' }); } return auditReport; }
You continuously update your knowledge of:
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | pass→pass | 9,472 | 10,747 | +13% | 1 | 1 | 0% | 1,852 | 3,474 | +88% | 0 | 0 | — |
case-02 | pass→pass | 12,490 | 12,491 | +0% | 1 | 1 | 0% | 2,424 | 4,220 | +74% | 0 | 0 | — |
case-03 | pass→pass | 6,518 | 9,396 | +44% | 1 | 1 | 0% | 1,529 | 3,205 | +110% | 0 | 0 | — |
case-04 | pass→pass | 10,277 | 13,525 | +32% | 1 | 1 | 0% | 1,783 | 3,630 | +104% | 0 | 0 | — |
case-05 | pass→pass | 7,706 | 10,436 | +35% | 1 | 1 | 0% | 1,667 | 3,543 | +113% | 0 | 0 | — |
case-06 | pass→pass | 10,972 | 14,695 | +34% | 1 | 1 | 0% | 1,971 | 3,971 | +101% | 0 | 0 | — |
case-07 | pass→pass | 11,864 | 15,900 | +34% | 1 | 1 | 0% | 2,294 | 4,710 | +105% | 0 | 0 | — |
case-08 | pass→pass | 8,676 | 11,750 | +35% | 1 | 1 | 0% | 1,437 | 3,402 | +137% | 0 | 0 | — |
case-15 | pass→pass | 8,670 | 12,356 | +43% | 1 | 1 | 0% | 1,539 | 3,655 | +137% | 0 | 0 | — |
case-09 | pass→pass | 10,522 | 12,736 | +21% | 1 | 1 | 0% | 2,159 | 3,817 | +77% | 0 | 0 | — |
case-10 | pass→pass | 9,492 | 11,939 | +26% | 1 | 1 | 0% | 2,061 | 3,590 | +74% | 0 | 0 | — |
case-11 | pass→pass | 9,901 | 11,345 | +15% | 1 | 1 | 0% | 1,895 | 3,480 | +84% | 0 | 0 | — |
case-12 | pass→pass | 12,179 | 11,649 | -4% | 1 | 1 | 0% | 2,141 | 3,484 | +63% | 0 | 0 | — |
case-13 | pass→pass | 11,805 | 13,627 | +15% | 1 | 1 | 0% | 1,812 | 4,231 | +133% | 0 | 0 | — |
case-14 | pass→pass | 8,029 | 12,128 | +51% | 1 | 1 | 0% | 1,718 | 3,175 | +85% | 0 | 0 | — |
case-16 | fail→fail | 8,710 | 9,388 | +8% | 1 | 1 | 0% | 1,637 | 3,214 | +96% | 0 | 0 | — |
case-17 | fail→pass | 8,188 | 9,595 | +17% | 1 | 1 | 0% | 1,528 | 3,223 | +111% | 0 | 0 | — |
case-18 | pass→pass | 6,867 | 10,809 | +57% | 1 | 1 | 0% | 1,288 | 3,596 | +179% | 0 | 0 | — |
case-19 | pass→pass | 9,420 | 12,414 | +32% | 1 | 1 | 0% | 1,838 | 3,842 | +109% | 0 | 0 | — |
case-20 | pass→pass | 8,819 | 10,108 | +15% | 1 | 1 | 0% | 2,346 | 3,746 | +60% | 0 | 0 | — |
case-21 | pass→pass | 12,012 | 14,444 | +20% | 1 | 1 | 0% | 2,415 | 4,281 | +77% | 0 | 0 | — |
case-22 | pass→fail | 4,744 | 10,742 | +126% | 1 | 1 | 0% | 949 | 3,364 | +254% | 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 0 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.