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Get Started Free →Generate an SVG of a user-requested image or scene
.claude/skills/aiskillstore-create-svg-from-prompt/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | -82% | 0% |
| case-03 | ✗→✓ | ▲ Improved | -76% | 0% |
| case-04 | ✗→✓ | ▲ Improved | -33% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 2% | 0% |
| case-06 | ✗→✓ | ▲ Improved | -30% | 0% |
if autotrace is not available in the environment, then install it with the following command:
bashsudo apt update sudo apt install git build-essential intltool imagemagick libmagickcore-dev pstoedit libpstoedit-dev autopoint git clone https://github.com/autotrace/autotrace.git cd autotrace ./autogen.sh LD_LIBRARY_PATH=/usr/local/lib ./configure --prefix=/usr make sudo make install
When the user prompts the model to generate an SVG of and image or scene:
To generate an SVG of an image or scene use Google Gemini and autotrace:
bashcurl -s -X POST "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash-image:generateContent" -H "x-goog-api-key: <GEMINI_API_KEY>" -H "Content-Type: application/json" -d '{ "contents": [{ "parts": [ {"text": "<IMAGE_PROMPT>"}]}]}' | grep -o '"data": "[^"]*"' | cut -d'"' -f4 | base64 --decode | autotrace -output-format svg -despeckle-level 10 -despeckle-tightness 2.0 -output-file <OUTPUT_FILE>.svg
Arguments from prompt:
<GEMINI_API_KEY>: The Gemini API key gotten from the environment variable "GEMINI_API_KEY"<IMAGE_PROMPT>: The user's initial prompt modified to change phrases like "Generate an svg" to "Generate an image." Do not modify the rest of the prompt or add any extra instructions or descriptions. <OUTPUT_FILE>: An 8-30 character filename based on the image the user wants| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 32,388 | 8,619 | -73% | 1 | 1 | 0% | 6,159 | 996 | -84% | 0 | 0 | — |
case-02 | fail→pass | 33,206 | 9,544 | -71% | 1 | 1 | 0% | 6,159 | 1,104 | -82% | 0 | 0 | — |
case-03 | fail→pass | 36,059 | 7,939 | -78% | 1 | 1 | 0% | 6,157 | 1,455 | -76% | 0 | 0 | — |
case-04 | fail→pass | 13,540 | 6,070 | -55% | 1 | 1 | 0% | 2,302 | 1,544 | -33% | 0 | 0 | — |
case-05 | fail→pass | 10,038 | 6,181 | -38% | 1 | 1 | 0% | 1,599 | 1,633 | +2% | 0 | 0 | — |
case-06 | fail→pass | 6,299 | 5,355 | -15% | 1 | 1 | 0% | 1,095 | 763 | -30% | 0 | 0 | — |
case-07 | fail→pass | 12,456 | 4,914 | -61% | 1 | 1 | 0% | 2,154 | 1,291 | -40% | 0 | 0 | — |
case-08 | fail→pass | 9,169 | 2,028 | -78% | 1 | 1 | 0% | 1,428 | 725 | -49% | 0 | 0 | — |
case-09 | fail→pass | 11,677 | 2,062 | -82% | 1 | 1 | 0% | 1,869 | 725 | -61% | 0 | 0 | — |
case-10 | pass→pass | 5,203 | 1,391 | -73% | 1 | 1 | 0% | 738 | 601 | -19% | 0 | 0 | — |
case-11 | pass→pass | 12,059 | 5,056 | -58% | 1 | 1 | 0% | 2,068 | 1,351 | -35% | 0 | 0 | — |
case-16 | fail→fail | 29,318 | 6,884 | -77% | 1 | 1 | 0% | 6,154 | 681 | -89% | 0 | 0 | — |
case-12 | fail→pass | 18,286 | 5,316 | -71% | 1 | 1 | 0% | 3,107 | 1,327 | -57% | 0 | 0 | — |
case-13 | fail→pass | 31,157 | 3,755 | -88% | 1 | 1 | 0% | 6,163 | 1,177 | -81% | 0 | 0 | — |
case-14 | fail→pass | 5,885 | 1,642 | -72% | 1 | 1 | 0% | 928 | 697 | -25% | 0 | 0 | — |
case-15 | fail→fail | 34,060 | 6,403 | -81% | 1 | 1 | 0% | 6,157 | 675 | -89% | 0 | 0 | — |
case-17 | fail→fail | 31,872 | 7,101 | -78% | 1 | 1 | 0% | 6,162 | 965 | -84% | 0 | 0 | — |
case-18 | fail→fail | 34,211 | 9,156 | -73% | 1 | 1 | 0% | 6,153 | 946 | -85% | 0 | 0 | — |
case-19 | pass→pass | 6,569 | 3,493 | -47% | 1 | 1 | 0% | 1,145 | 1,093 | -5% | 0 | 0 | — |
case-20 | pass→pass | 7,627 | 3,384 | -56% | 1 | 1 | 0% | 1,267 | 949 | -25% | 0 | 0 | — |
case-21 | pass→pass | 11,370 | 6,658 | -41% | 1 | 1 | 0% | 2,041 | 1,536 | -25% | 0 | 0 | — |
case-22 | fail→fail | 35,945 | 7,224 | -80% | 1 | 1 | 0% | 6,154 | 701 | -89% | 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 16 counted toward the lift figure. The other 6 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 +50 percentage points is the difference between those two pass rates over the 16 comparable cases.
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.