Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/mkdev-me/agent-skillsnpx agentmods add skills/mkdev-me/agent-skills/gemini-image-generatorWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/mkdev-me/agent-skills/gemini-image-generator)<a href="https://agentmods.dev/skills/mkdev-me/agent-skills/gemini-image-generator"><img src="https://agentmods.dev/badge/skills/mkdev-me/agent-skills/gemini-image-generator.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00013 | $0.00356 |
| Opus 5 | $0.00006 | $0.00178 |
| Sonnet 5 | $0.00003 | $0.00071 |
| Haiku 4.5 | $0.00001 | $0.00036 |
Grade A, and why
gemini-image-generator scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
gemini-image-generator
Instructions
Use this skill to generate images using Google Gemini's image generation model. The skill supports:
- Text-to-image generation from prompts
- Image-to-image generation with a reference image
- Multiple output sizes (1K, 2K, 4K)
- Custom output paths
The API key must be set via the GEMINI_API_KEY environment variable.
Parameters
--prompt(required): The text prompt describing the image to generate--output(required): Output file path for the generated image--reference: Optional reference image for style/content guidance--size: Image size - "1K", "2K", or "4K" (default: 4K)
Examples
Basic text-to-image generation
./scripts/generate.py --prompt "A serene mountain landscape at sunset" --output images/landscape.png
With reference image for style guidance
./scripts/generate.py --prompt "Same character but wearing a party hat" --reference images/character.png --output images/party.png
Different output size
./scripts/generate.py --prompt "Abstract art" --output art.png --size 2K
Setup
Before first use, set up the virtual environment:
cd scripts && python3 -m venv venv && ./venv/bin/pip install -r requirements.txt
Set your API key:
export GEMINI_API_KEY="your-api-key-here"
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 53 lines · 13 tokens per session scan A fb3f27c1d0ea
gemini-image-generator is a skill published in the GitHub repository mkdev-me/agent-skills (37 stars, last pushed 4mo ago), licensed MIT. It adds 13 tokens to every session and 356 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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