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Get Started Free →Generate/edit images with Nano Banana Pro (Gemini 3 Pro Image). Use for image create/modify requests incl. edits. Supports text-to-image + image-to-image; 1K/2K/4K; use --input-image.
.claude/skills/dicklesworthstone-nano-banana-pro/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-09 | ✗→✓ | ▲ Improved | -20% | 0% |
| case-15 | ✗→✓ | ▲ Improved | 325% | 0% |
| case-20 | ✗→✓ | ▲ Improved | 39% | 0% |
| case-22 | ✗→✓ | ▲ Improved | 250% | 0% |
| case-13 | ✓→✗ | ▼ Worse | 476% | 0% |
Generate new images or edit existing ones using Google's Nano Banana Pro API (Gemini 3 Pro Image).
Run the script using absolute path (do NOT cd to skill directory first):
Generate new image:
bashuv run {baseDir}/scripts/generate_image.py --prompt "your image description" --filename "output-name.png" [--resolution 1K|2K|4K] [--api-key KEY]
Edit existing image:
bashuv run {baseDir}/scripts/generate_image.py --prompt "editing instructions" --filename "output-name.png" --input-image "path/to/input.png" [--resolution 1K|2K|4K] [--api-key KEY]
Important: Always run from the user's current working directory so images are saved where the user is working, not in the skill directory.
Goal: fast iteration without burning time on 4K until the prompt is correct.
uv run {baseDir}/scripts/generate_image.py --prompt "<draft prompt>" --filename "yyyy-mm-dd-hh-mm-ss-draft.png" --resolution 1K--input-image for every iteration until you’re happy.uv run {baseDir}/scripts/generate_image.py --prompt "<final prompt>" --filename "yyyy-mm-dd-hh-mm-ss-final.png" --resolution 4KThe Gemini 3 Pro Image API supports three resolutions (uppercase K required):
Map user requests to API parameters:
1K1K2K4KThe script checks for API key in this order:
--api-key argument (use if user provided key in chat)GEMINI_API_KEY environment variableIf neither is available, the script exits with an error message.
command -v uv (must exist)test -n \"$GEMINI_API_KEY\" (or pass --api-key)test -f \"path/to/input.png\"Error: No API key provided. → set GEMINI_API_KEY or pass --api-keyError loading input image: → wrong path / unreadable file; verify --input-image points to a real imageGenerate filenames with the pattern: yyyy-mm-dd-hh-mm-ss-name.png
Format: {timestamp}-{descriptive-name}.png
yyyy-mm-dd-hh-mm-ss (24-hour format)x9k2, a7b3)Examples:
2025-11-23-14-23-05-japanese-garden.png2025-11-23-15-30-12-sunset-mountains.png2025-11-23-16-45-33-robot.png2025-11-23-17-12-48-x9k2.pngWhen the user wants to modify an existing image:
--input-image parameter with the path to the imageFor generation: Pass user's image description as-is to --prompt. Only rework if clearly insufficient.
For editing: Pass editing instructions in --prompt (e.g., "add a rainbow in the sky", "make it look like a watercolor painting")
Preserve user's creative intent in both cases.
Use templates when the user is vague or when edits must be precise.
Generate new image:
bashuv run {baseDir}/scripts/generate_image.py --prompt "A serene Japanese garden with cherry blossoms" --filename "2025-11-23-14-23-05-japanese-garden.png" --resolution 4K
Edit existing image:
bashuv run {baseDir}/scripts/generate_image.py --prompt "make the sky more dramatic with storm clouds" --filename "2025-11-23-14-25-30-dramatic-sky.png" --input-image "original-photo.jpg" --resolution 2K
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-02 | fail→fail | 10,063 | 7,286 | -28% | 1 | 1 | 0% | 796 | 2,011 | +153% | 0 | 0 | — |
case-21 | fail→fail | 5,549 | 11,189 | +102% | 1 | 1 | 0% | 1,027 | 2,025 | +97% | 0 | 0 | — |
case-01 | fail→fail | 6,176 | 12,025 | +95% | 1 | 1 | 0% | 1,030 | 2,032 | +97% | 0 | 0 | — |
case-03 | fail→fail | 6,166 | 6,796 | +10% | 1 | 1 | 0% | 965 | 1,999 | +107% | 0 | 0 | — |
case-04 | fail→fail | 8,068 | 7,027 | -13% | 1 | 1 | 0% | 1,524 | 1,946 | +28% | 0 | 0 | — |
case-05 | fail→fail | 39,417 | 6,896 | -83% | 1 | 1 | 0% | 8,225 | 1,971 | -76% | 0 | 0 | — |
case-06 | fail→fail | 12,564 | 7,199 | -43% | 1 | 1 | 0% | 631 | 1,937 | +207% | 0 | 0 | — |
case-07 | fail→fail | 6,250 | 9,390 | +50% | 1 | 1 | 0% | 867 | 2,033 | +134% | 0 | 0 | — |
case-08 | fail→fail | 9,630 | 8,297 | -14% | 1 | 1 | 0% | 1,401 | 2,059 | +47% | 0 | 0 | — |
case-09 | fail→pass | 14,395 | 3,578 | -75% | 1 | 1 | 0% | 2,812 | 2,259 | -20% | 0 | 0 | — |
case-10 | fail→fail | 10,328 | 7,013 | -32% | 1 | 1 | 0% | 2,037 | 2,067 | +1% | 0 | 0 | — |
case-11 | pass→pass | 3,646 | 11,990 | +229% | 1 | 1 | 0% | 564 | 3,875 | +587% | 0 | 0 | — |
case-12 | fail→fail | 8,781 | 6,934 | -21% | 1 | 1 | 0% | 1,238 | 2,064 | +67% | 0 | 0 | — |
case-13 | pass→fail | 8,804 | 10,018 | +14% | 1 | 1 | 0% | 367 | 2,113 | +476% | 0 | 0 | — |
case-14 | fail→fail | 4,973 | 9,594 | +93% | 1 | 1 | 0% | 735 | 2,165 | +195% | 0 | 0 | — |
case-15 | fail→pass | 7,270 | 22,854 | +214% | 1 | 1 | 0% | 1,146 | 4,870 | +325% | 0 | 0 | — |
case-16 | fail→fail | 11,637 | 9,259 | -20% | 1 | 1 | 0% | 1,767 | 2,113 | +20% | 0 | 0 | — |
case-17 | pass→pass | 5,555 | 4,557 | -18% | 1 | 1 | 0% | 885 | 2,367 | +167% | 0 | 0 | — |
case-18 | fail→fail | 12,639 | 8,399 | -34% | 1 | 1 | 0% | 204 | 2,058 | +909% | 0 | 0 | — |
case-19 | fail→fail | 12,832 | 7,030 | -45% | 1 | 1 | 0% | 690 | 1,967 | +185% | 0 | 0 | — |
case-20 | fail→pass | 9,153 | 3,013 | -67% | 1 | 1 | 0% | 1,514 | 2,109 | +39% | 0 | 0 | — |
case-22 | fail→pass | 6,302 | 21,717 | +245% | 1 | 1 | 0% | 1,011 | 3,537 | +250% | 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 7 counted toward the lift figure. The other 15 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 +14 percentage points is the difference between those two pass rates over the 7 comparable cases. 2 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.