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Get Started Free →Fetch full vehicle specifications from a VIN using the CarsXE API. Use this when the user provides a VIN and wants to know details about a vehicle (make, model, year, engine, trim, equipment, etc.).
.claude/skills/hashgraph-online-vehicle-specs/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-06 | ✗→✓ | ▲ Improved | -81% | 0% |
| case-04 | ✗→✓ | ▲ Improved | -70% | 0% |
| case-07 | ✗→✓ | ▲ Improved | -66% | 0% |
| case-15 | ✗→✓ | ▲ Improved | 68% | 0% |
| case-18 | ✗→✓ | ▲ Improved | -87% | 0% |
When the user provides a VIN and asks about vehicle specs, details, or information:
GET https://api.carsxe.com/specs?key={CARSXE_API_KEY}&vin={VIN}&source=codex_plugin Replace {CARSXE_API_KEY} with the value of the CARSXE_API_KEY environment variable. Optional params: deepdata=true for extended data, disableIntVINDecoding=true to skip the international VIN fallback.
CARSXE_API_KEY environment variable (see AGENTS.md).| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-21 | pass→pass | 16,446 | 12,772 | -22% | 1 | 1 | 0% | 1,592 | 1,308 | -18% | 0 | 0 | — |
case-06 | fail→pass | 23,684 | 7,924 | -67% | 1 | 1 | 0% | 3,368 | 642 | -81% | 0 | 0 | — |
case-01 | fail→fail | 9,866 | 19,196 | +95% | 1 | 1 | 0% | 1,865 | 775 | -58% | 0 | 0 | — |
case-02 | fail→fail | 19,744 | 34,052 | +72% | 1 | 1 | 0% | 2,697 | 5,880 | +118% | 0 | 0 | — |
case-03 | fail→fail | 13,951 | 15,352 | +10% | 1 | 1 | 0% | 2,457 | 1,022 | -58% | 0 | 0 | — |
case-04 | fail→pass | 32,096 | 11,181 | -65% | 1 | 1 | 0% | 3,915 | 1,176 | -70% | 0 | 0 | — |
case-05 | fail→fail | 4,551 | 7,142 | +57% | 1 | 1 | 0% | 631 | 481 | -24% | 0 | 0 | — |
case-07 | fail→pass | 28,039 | 7,421 | -74% | 1 | 1 | 0% | 4,632 | 1,598 | -66% | 0 | 0 | — |
case-08 | pass→pass | 20,579 | 11,324 | -45% | 1 | 1 | 0% | 2,289 | 1,133 | -51% | 0 | 0 | — |
case-09 | pass→pass | 15,972 | 12,234 | -23% | 1 | 1 | 0% | 1,844 | 2,516 | +36% | 0 | 0 | — |
case-10 | pass→pass | 14,013 | 10,502 | -25% | 1 | 1 | 0% | 2,053 | 1,051 | -49% | 0 | 0 | — |
case-11 | pass→pass | 10,117 | 10,224 | +1% | 1 | 1 | 0% | 1,405 | 2,028 | +44% | 0 | 0 | — |
case-12 | pass→pass | 6,239 | 1,669 | -73% | 1 | 1 | 0% | 757 | 513 | -32% | 0 | 0 | — |
case-13 | pass→pass | 7,745 | 1,898 | -75% | 1 | 1 | 0% | 357 | 513 | +44% | 0 | 0 | — |
case-14 | pass→pass | 8,377 | 7,942 | -5% | 1 | 1 | 0% | 450 | 717 | +59% | 0 | 0 | — |
case-15 | fail→pass | 11,586 | 13,187 | +14% | 1 | 1 | 0% | 1,095 | 1,841 | +68% | 0 | 0 | — |
case-16 | pass→pass | 5,933 | 1,657 | -72% | 1 | 1 | 0% | 929 | 522 | -44% | 0 | 0 | — |
case-17 | pass→pass | 11,852 | 7,173 | -39% | 1 | 1 | 0% | 1,026 | 541 | -47% | 0 | 0 | — |
case-18 | fail→pass | 34,823 | 7,112 | -80% | 1 | 1 | 0% | 5,389 | 679 | -87% | 0 | 0 | — |
case-19 | pass→fail | 9,325 | 6,732 | -28% | 1 | 1 | 0% | 593 | 514 | -13% | 0 | 0 | — |
case-20 | pass→pass | 19,198 | 16,861 | -12% | 1 | 1 | 0% | 2,341 | 2,170 | -7% | 0 | 0 | — |
case-22 | pass→pass | 20,451 | 11,237 | -45% | 1 | 1 | 0% | 2,533 | 2,048 | -19% | 0 | 0 | — |
case-23 | pass→pass | 10,973 | 5,721 | -48% | 1 | 1 | 0% | 1,733 | 1,164 | -33% | 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. 23 cases were attempted, and 21 counted toward the lift figure. The other 2 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +17 percentage points is the difference between those two pass rates over the 21 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.