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Get Started Free →Expert skill in map updates focusing on navigation domain applications. Covers 40 topics across navigation domain. Includes 40 skill files covering ASPICE Level 3, AUTOSAR 4.4, ISO 21434, ISO 26262.
.claude/skills/pangzhenying2025-automotive-navigation/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-04 | ✗→✓ | ▲ Improved | 69% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 69% | 0% |
| case-15 | ✗→✓ | ▲ Improved | 121% | 0% |
| case-05 | ✓→✗ | ▼ Worse | 69% | 0% |
| case-07 | ✓→✗ | ▼ Worse | 69% | 0% |
40 skill files covering navigation domain for automotive software engineering.
Expert in map updates for automotive navigation systems.
Expert in map updates for automotive navigation systems.
Expert in map updates for automotive navigation systems.
Expert in map updates for automotive navigation systems.
Expert in map updates for automotive navigation systems.
Expert in map updates for automotive navigation systems.
Expert in map updates for automotive navigation systems.
Expert in map updates for automotive navigation systems.
Expert in map updates for automotive navigation systems.
Expert in map updates for automotive navigation systems.
Expert in poi for automotive navigation systems.
Expert in poi for automotive navigation systems.
Expert in poi for automotive navigation systems.
Expert in poi for automotive navigation systems.
Expert in poi for automotive navigation systems.
Expert in poi for automotive navigation systems.
Expert in poi for automotive navigation systems.
Expert in poi for automotive navigation systems.
Expert in poi for automotive navigation systems.
Expert in poi for automotive navigation systems.
Expert in real time traffic for automotive navigation systems.
Expert in real time traffic for automotive navigation systems.
Expert in real time traffic for automotive navigation systems.
Expert in real time traffic for automotive navigation systems.
Expert in real time traffic for automotive navigation systems.
Expert in real time traffic for automotive navigation systems.
Expert in real time traffic for automotive navigation systems.
Expert in real time traffic for automotive navigation systems.
Expert in real time traffic for automotive navigation systems.
Expert in real time traffic for automotive navigation systems.
Expert in routing for automotive navigation systems.
Expert in routing for automotive navigation systems.
Expert in routing for automotive navigation systems.
Expert in routing for automotive navigation systems.
Expert in routing for automotive navigation systems.
Expert in routing for automotive navigation systems.
Expert in routing for automotive navigation systems.
Expert in routing for automotive navigation systems.
Expert in routing for automotive navigation systems.
Expert in routing for automotive navigation systems.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 32,130 | 27,289 | -15% | 1 | 1 | 0% | 6,199 | 10,496 | +69% | 0 | 0 | — |
case-02 | fail→fail | 26,540 | 25,595 | -4% | 1 | 1 | 0% | 6,190 | 10,487 | +69% | 0 | 0 | — |
case-03 | fail→fail | 33,430 | 24,012 | -28% | 1 | 1 | 0% | 6,189 | 10,486 | +69% | 0 | 0 | — |
case-04 | fail→pass | 27,383 | 27,216 | -1% | 1 | 1 | 0% | 6,209 | 10,506 | +69% | 0 | 0 | — |
case-05 | pass→fail | 29,540 | 26,483 | -10% | 1 | 1 | 0% | 6,222 | 10,519 | +69% | 0 | 0 | — |
case-06 | fail→fail | 33,486 | 26,874 | -20% | 1 | 1 | 0% | 6,222 | 10,519 | +69% | 0 | 0 | — |
case-07 | pass→fail | 28,532 | 28,173 | -1% | 1 | 1 | 0% | 6,202 | 10,499 | +69% | 0 | 0 | — |
case-08 | fail→fail | 26,125 | 28,711 | +10% | 1 | 1 | 0% | 6,196 | 10,493 | +69% | 0 | 0 | — |
case-09 | fail→fail | 28,594 | 27,495 | -4% | 1 | 1 | 0% | 6,192 | 10,489 | +69% | 0 | 0 | — |
case-10 | fail→pass | 28,680 | 29,122 | +2% | 1 | 1 | 0% | 6,195 | 10,492 | +69% | 0 | 0 | — |
case-11 | fail→fail | 28,531 | 28,655 | +0% | 1 | 1 | 0% | 6,209 | 10,506 | +69% | 0 | 0 | — |
case-12 | fail→fail | 31,570 | 27,574 | -13% | 1 | 1 | 0% | 6,207 | 10,504 | +69% | 0 | 0 | — |
case-13 | fail→fail | 37,529 | 31,712 | -16% | 1 | 1 | 0% | 6,199 | 10,496 | +69% | 0 | 0 | — |
case-14 | pass→fail | 31,783 | 29,328 | -8% | 1 | 1 | 0% | 6,198 | 10,495 | +69% | 0 | 0 | — |
case-15 | fail→pass | 24,382 | 30,370 | +25% | 1 | 1 | 0% | 4,749 | 10,504 | +121% | 0 | 0 | — |
case-16 | fail→fail | 32,122 | 31,545 | -2% | 1 | 1 | 0% | 6,216 | 10,513 | +69% | 0 | 0 | — |
case-17 | fail→fail | 32,341 | 31,947 | -1% | 1 | 1 | 0% | 6,210 | 10,507 | +69% | 0 | 0 | — |
case-18 | fail→fail | 28,008 | 29,764 | +6% | 1 | 1 | 0% | 6,191 | 10,488 | +69% | 0 | 0 | — |
case-19 | fail→fail | 32,606 | 31,630 | -3% | 1 | 1 | 0% | 6,207 | 10,504 | +69% | 0 | 0 | — |
case-20 | fail→fail | 20,449 | 22,523 | +10% | 1 | 1 | 0% | 4,064 | 9,041 | +122% | 0 | 0 | — |
case-21 | fail→fail | 31,791 | 28,703 | -10% | 1 | 1 | 0% | 6,186 | 10,483 | +69% | 0 | 0 | — |
case-22 | fail→fail | 13,773 | 22,818 | +66% | 1 | 1 | 0% | 2,787 | 9,156 | +229% | 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. 4 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.