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Get Started Free →Translate visa application documents (images) to English and create a bilingual PDF with original and translation
.claude/skills/loulanyue-visa-doc-translate/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 36% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 143% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 214% | 0% |
| case-11 | ✗→✓ | ▲ Improved | 15% | 0% |
| case-17 | ✗→✓ | ▲ Improved | 41% | 0% |
You are helping translate visa application documents for visa applications.
When the user provides an image file path, AUTOMATICALLY execute the following steps WITHOUT asking for confirmation:
sips -s format png <input> --out <output><original_filename>_Translated.pdf in the same directorypython import Vision from Foundation import NSURL
bash pip install easyocr
bash brew install tesseract tesseract-lang pip install pytesseract
bashpip install pillow reportlab
For macOS Vision framework:
bashpip install pyobjc-framework-Vision pyobjc-framework-Quartz
bash/visa-doc-translate RetirementCertificate.PNG /visa-doc-translate BankStatement.HEIC /visa-doc-translate EmploymentLetter.jpg
The skill will:
<filename>_Translated.pdf with:Perfect for visa applications to Australia, USA, Canada, UK, and other countries requiring translated documents.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 22,460 | 23,337 | +4% | 1 | 1 | 0% | 4,158 | 5,666 | +36% | 0 | 0 | — |
case-02 | fail→fail | 6,668 | 7,477 | +12% | 1 | 1 | 0% | 451 | 1,279 | +184% | 0 | 0 | — |
case-03 | fail→fail | 15,067 | 8,406 | -44% | 1 | 1 | 0% | 2,885 | 1,562 | -46% | 0 | 0 | — |
case-04 | pass→pass | 11,045 | 5,568 | -50% | 1 | 1 | 0% | 1,885 | 1,955 | +4% | 0 | 0 | — |
case-05 | fail→pass | 4,518 | 6,347 | +40% | 1 | 1 | 0% | 775 | 1,884 | +143% | 0 | 0 | — |
case-06 | pass→pass | 7,804 | 4,036 | -48% | 1 | 1 | 0% | 1,244 | 1,581 | +27% | 0 | 0 | — |
case-07 | pass→pass | 10,643 | 6,549 | -38% | 1 | 1 | 0% | 1,783 | 1,972 | +11% | 0 | 0 | — |
case-08 | pass→pass | 16,355 | 3,726 | -77% | 1 | 1 | 0% | 2,593 | 1,469 | -43% | 0 | 0 | — |
case-09 | pass→fail | 11,962 | 3,146 | -74% | 1 | 1 | 0% | 1,903 | 1,447 | -24% | 0 | 0 | — |
case-10 | fail→pass | 3,087 | 2,492 | -19% | 1 | 1 | 0% | 411 | 1,291 | +214% | 0 | 0 | — |
case-11 | fail→pass | 12,287 | 6,857 | -44% | 1 | 1 | 0% | 1,912 | 2,201 | +15% | 0 | 0 | — |
case-12 | fail→fail | 5,066 | 3,067 | -39% | 1 | 1 | 0% | 775 | 1,384 | +79% | 0 | 0 | — |
case-13 | fail→fail | 10,540 | 6,012 | -43% | 1 | 1 | 0% | 1,749 | 1,903 | +9% | 0 | 0 | — |
case-14 | pass→pass | 5,460 | 1,435 | -74% | 1 | 1 | 0% | 961 | 1,103 | +15% | 0 | 0 | — |
case-15 | pass→pass | 8,285 | 2,181 | -74% | 1 | 1 | 0% | 1,392 | 1,197 | -14% | 0 | 0 | — |
case-16 | pass→pass | 9,621 | 4,644 | -52% | 1 | 1 | 0% | 1,637 | 1,678 | +3% | 0 | 0 | — |
case-17 | fail→pass | 6,008 | 1,739 | -71% | 1 | 1 | 0% | 829 | 1,169 | +41% | 0 | 0 | — |
case-18 | pass→pass | 4,124 | 2,087 | -49% | 1 | 1 | 0% | 706 | 1,215 | +72% | 0 | 0 | — |
case-19 | fail→pass | 6,156 | 2,148 | -65% | 1 | 1 | 0% | 1,035 | 1,259 | +22% | 0 | 0 | — |
case-20 | pass→pass | 10,827 | 9,519 | -12% | 1 | 1 | 0% | 1,887 | 2,469 | +31% | 0 | 0 | — |
case-21 | pass→pass | 12,113 | 10,234 | -16% | 1 | 1 | 0% | 2,057 | 2,725 | +32% | 0 | 0 | — |
case-22 | pass→pass | 10,072 | 8,572 | -15% | 1 | 1 | 0% | 1,609 | 2,305 | +43% | 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, and 21 counted toward the lift figure. The other 1 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 +23 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.