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Get Started Free →Use when tasks involve reading, creating, or reviewing PDF files where rendering and layout matter; prefer visual checks by rendering pages (Poppler) and use Python tools such as `reportlab`, `pdfplumber`, and `pypdf` for generation and extraction.
.claude/skills/ethanyoq-pdf/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-14 | ✗→✓ | ▲ Improved | -12% | 0% |
| case-08 | ✗→✓ | ▲ Improved | -53% | 0% |
| case-09 | ✗→✓ | ▲ Improved | -59% | 0% |
| case-16 | ✗→✓ | ▲ Improved | 92% | 0% |
| case-19 | ✗→✓ | ▲ Improved | -41% | 0% |
pdftoppm if available.reportlab to generate PDFs when creating new documents.pdfplumber (or pypdf) for text extraction and quick checks; do not rely on it for layout fidelity.tmp/pdfs/ for intermediate files; delete when done.output/pdf/ when working in this repo.Prefer uv for dependency management.
Python packages:
uv pip install reportlab pdfplumber pypdfIf uv is unavailable:
python3 -m pip install reportlab pdfplumber pypdfSystem tools (for rendering):
# macOS (Homebrew)
brew install poppler
# Ubuntu/Debian
sudo apt-get install -y poppler-utilsIf installation isn't possible in this environment, tell the user which dependency is missing and how to install it locally.
No required environment variables.
pdftoppm -png $INPUT_PDF $OUTPUT_PREFIX| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-13 | pass→pass | 12,235 | 2,970 | -76% | 1 | 1 | 0% | 2,456 | 1,123 | -54% | 0 | 0 | — |
case-01 | fail→fail | 27,540 | 6,090 | -78% | 1 | 1 | 0% | 6,180 | 791 | -87% | 0 | 0 | — |
case-02 | fail→fail | 4,217 | 5,592 | +33% | 1 | 1 | 0% | 669 | 839 | +25% | 0 | 0 | — |
case-03 | fail→fail | 25,067 | 6,623 | -74% | 1 | 1 | 0% | 6,180 | 854 | -86% | 0 | 0 | — |
case-04 | pass→fail | 22,434 | 8,703 | -61% | 1 | 1 | 0% | 6,189 | 1,366 | -78% | 0 | 0 | — |
case-14 | fail→pass | 9,332 | 5,328 | -43% | 1 | 1 | 0% | 1,904 | 1,678 | -12% | 0 | 0 | — |
case-05 | pass→pass | 5,514 | 1,542 | -72% | 1 | 1 | 0% | 1,107 | 865 | -22% | 0 | 0 | — |
case-06 | pass→pass | 1,267 | 1,400 | +10% | 1 | 1 | 0% | 268 | 794 | +196% | 0 | 0 | — |
case-07 | pass→pass | 2,094 | 2,017 | -4% | 1 | 1 | 0% | 399 | 882 | +121% | 0 | 0 | — |
case-08 | fail→pass | 7,734 | 1,207 | -84% | 1 | 1 | 0% | 1,575 | 739 | -53% | 0 | 0 | — |
case-15 | pass→pass | 6,907 | 2,696 | -61% | 1 | 1 | 0% | 1,493 | 1,071 | -28% | 0 | 0 | — |
case-09 | fail→pass | 9,742 | 1,440 | -85% | 1 | 1 | 0% | 1,825 | 752 | -59% | 0 | 0 | — |
case-10 | pass→pass | 7,092 | 1,620 | -77% | 1 | 1 | 0% | 1,370 | 829 | -39% | 0 | 0 | — |
case-11 | pass→pass | 5,097 | 2,283 | -55% | 1 | 1 | 0% | 1,273 | 1,017 | -20% | 0 | 0 | — |
case-12 | pass→pass | 8,030 | 3,925 | -51% | 1 | 1 | 0% | 1,801 | 1,453 | -19% | 0 | 0 | — |
case-16 | fail→pass | 5,896 | 10,971 | +86% | 1 | 1 | 0% | 1,155 | 2,217 | +92% | 0 | 0 | — |
case-17 | pass→pass | 9,127 | 1,687 | -82% | 1 | 1 | 0% | 1,973 | 816 | -59% | 0 | 0 | — |
case-18 | pass→pass | 12,810 | 8,982 | -30% | 1 | 1 | 0% | 2,450 | 2,243 | -8% | 0 | 0 | — |
case-19 | fail→pass | 11,760 | 4,617 | -61% | 1 | 1 | 0% | 2,557 | 1,496 | -41% | 0 | 0 | — |
case-20 | pass→pass | 14,940 | 13,450 | -10% | 1 | 1 | 0% | 3,603 | 3,673 | +2% | 0 | 0 | — |
case-21 | pass→pass | 8,973 | 6,942 | -23% | 1 | 1 | 0% | 2,240 | 2,180 | -3% | 0 | 0 | — |
case-22 | pass→pass | 12,228 | 20,607 | +69% | 1 | 1 | 0% | 3,336 | 6,698 | +101% | 0 | 0 | — |
case-23 | pass→pass | 9,901 | 9,157 | -8% | 1 | 1 | 0% | 2,347 | 2,773 | +18% | 0 | 0 | — |
case-24 | pass→pass | 11,238 | 13,170 | +17% | 1 | 1 | 0% | 2,654 | 3,601 | +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. 24 cases were attempted, and 20 counted toward the lift figure. The other 4 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 20 comparable cases. 3 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.