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Get Started Free →Extract a license plate number from an image URL using the CarsXE Plate Recognition API. Use this when a user shares a photo of a vehicle or license plate and wants to identify the plate number.
.claude/skills/hashgraph-online-plate-image-recognition/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-04 | ✗→✓ | ▲ Improved | -55% | 0% |
| case-06 | ✗→✓ | ▲ Improved | -52% | 0% |
| case-07 | ✗→✓ | ▲ Improved | -65% | 0% |
| case-09 | ✗→✓ | ▲ Improved | -55% | 0% |
| case-11 | ✗→✓ | ▲ Improved | -75% | 0% |
When the user shares an image containing a license plate and wants to extract the plate number:
https://api.carsxe.com/platerecognition?key={CARSXE_API_KEY}&source=codex_pluginContent-Type: application/jsonjson { "image": "{IMAGE_URL}" }
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-01 | fail→fail | 2,643 | 12,695 | +380% | 1 | 1 | 0% | 381 | 1,529 | +301% | 0 | 0 | — |
case-02 | fail→fail | 7,219 | 12,722 | +76% | 1 | 1 | 0% | 307 | 1,593 | +419% | 0 | 0 | — |
case-03 | fail→fail | 7,476 | 7,884 | +5% | 1 | 1 | 0% | 349 | 1,494 | +328% | 0 | 0 | — |
case-04 | fail→pass | 9,708 | 7,925 | -18% | 1 | 1 | 0% | 1,607 | 728 | -55% | 0 | 0 | — |
case-05 | pass→pass | 11,958 | 7,487 | -37% | 1 | 1 | 0% | 1,569 | 618 | -61% | 0 | 0 | — |
case-06 | fail→pass | 14,008 | 7,287 | -48% | 1 | 1 | 0% | 1,274 | 611 | -52% | 0 | 0 | — |
case-07 | fail→pass | 13,814 | 8,364 | -39% | 1 | 1 | 0% | 1,736 | 604 | -65% | 0 | 0 | — |
case-08 | pass→pass | 3,825 | 7,137 | +87% | 1 | 1 | 0% | 666 | 515 | -23% | 0 | 0 | — |
case-09 | fail→pass | 15,153 | 3,130 | -79% | 1 | 1 | 0% | 1,343 | 598 | -55% | 0 | 0 | — |
case-10 | pass→pass | 15,285 | 8,218 | -46% | 1 | 1 | 0% | 1,621 | 713 | -56% | 0 | 0 | — |
case-11 | fail→pass | 10,504 | 8,039 | -23% | 1 | 1 | 0% | 1,766 | 442 | -75% | 0 | 0 | — |
case-12 | fail→pass | 20,204 | 15,035 | -26% | 1 | 1 | 0% | 2,146 | 1,860 | -13% | 0 | 0 | — |
case-13 | fail→pass | 9,667 | 6,923 | -28% | 1 | 1 | 0% | 808 | 454 | -44% | 0 | 0 | — |
case-14 | fail→fail | 13,448 | 7,417 | -45% | 1 | 1 | 0% | 1,262 | 534 | -58% | 0 | 0 | — |
case-15 | fail→pass | 9,967 | 7,194 | -28% | 1 | 1 | 0% | 1,702 | 512 | -70% | 0 | 0 | — |
case-16 | fail→pass | 15,089 | 2,404 | -84% | 1 | 1 | 0% | 1,702 | 624 | -63% | 0 | 0 | — |
case-17 | fail→pass | 11,089 | 7,776 | -30% | 1 | 1 | 0% | 1,866 | 553 | -70% | 0 | 0 | — |
case-18 | fail→pass | 14,162 | 4,181 | -70% | 1 | 1 | 0% | 1,448 | 698 | -52% | 0 | 0 | — |
case-19 | fail→pass | 6,226 | 8,638 | +39% | 1 | 1 | 0% | 872 | 820 | -6% | 0 | 0 | — |
case-20 | pass→pass | 11,351 | 6,386 | -44% | 1 | 1 | 0% | 1,628 | 389 | -76% | 0 | 0 | — |
case-21 | fail→pass | 10,058 | 5,455 | -46% | 1 | 1 | 0% | 1,860 | 1,160 | -38% | 0 | 0 | — |
case-22 | pass→pass | 8,444 | 13,814 | +64% | 1 | 1 | 0% | 1,418 | 1,367 | -4% | 0 | 0 | — |
case-23 | pass→pass | 14,728 | 15,362 | +4% | 1 | 1 | 0% | 1,710 | 2,018 | +18% | 0 | 0 | — |
case-24 | pass→fail | 3,760 | 16,195 | +331% | 1 | 1 | 0% | 435 | 2,083 | +379% | 0 | 0 | — |
case-25 | pass→pass | 11,639 | 7,483 | -36% | 1 | 1 | 0% | 1,189 | 1,616 | +36% | 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. 25 cases were attempted. The headline lift of +48 percentage points is the difference between those two pass rates over the 25 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.