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AI image generation: Gemini and Nano Banana backends; single/series/batch workflows with prompt-to-disk.
Passed 10 of 18 test cases with the skill, and 5 of 18 without it.
and 8 more in the full report
7 fixed6 passed either way10 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
This benchmark measured an earlier version of the skill.
One of 7 test cases the skill fixed. Same model, same prompt, with and without the skill.
When attempting to run Gemini image generation scripts, Python throws 'Missing dependency: google-genai'. What shell command resolves this error?
| Checked | Without skill | With skill |
|---|---|---|
| Recommends executing pip install google-genai pillow to fix missing dependencies. | Missed | Passed |
The instructions your model reads when the skill loads.
Backend-agnostic image generation: single images, series with anchor-chain consistency, batch pipelines. Two backends: Gemini (API) and Nano Banana (local scripts with post-processing).
| Signal | Load | Content |
|---|---|---|
| Gemini backend selected | references/backends/gemini.md | Models, env vars, flag table, examples, error codes |
| Nano Banana needed (post-processing, series, JSON batch, style-match) | references/backends/nano-banana.md | Subcommands, flags, aspect ratios, prompt patterns by asset type |
python3 skills/content/image-gen/scripts/detect-backend.py
gemini -- load references/backends/gemini.mdask -- no key found; ask the user to set GEMINI_API_KEYGate: backend confirmed.
Write all prompts to disk before any API call. Prompt files are the generation record and anchor-chain input for series.
File naming: single prompts/YYYY-MM-DD-{slug}.md, series prompts/{series-name}-01.md through -NN.md.
---
model: gemini-3-pro-image-preview
aspect-ratio: 1:1
flags: []
---
Full prompt text. Be explicit about subject, style, background, constraints.
mkdir -p prompts
test cases passed, vs 5 / 18 without
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