Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add JimothySnicket/gemini-image-mcp --skill image-generationgit clone --depth 1 https://github.com/JimothySnicket/gemini-image-mcpWrote 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/jimothysnicket/gemini-image-mcp/image-generation)<a href="https://agentmods.dev/skills/jimothysnicket/gemini-image-mcp/image-generation"><img src="https://agentmods.dev/badge/skills/jimothysnicket/gemini-image-mcp/image-generation/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/jimothysnicket/gemini-image-mcp/image-generation"><img src="https://agentmods.dev/badge/skills/jimothysnicket/gemini-image-mcp/image-generation.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00018 | $0.01806 |
| Opus 5 | $0.00009 | $0.00903 |
| Sonnet 5 | $0.00004 | $0.00361 |
| Haiku 4.5 | $0.00002 | $0.00181 |
Grade A, and why
image-generation 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 9d 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.
How it starts
The opening of the file, as written. The whole thing — 160 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Image Generation Skill
You have access to Google Gemini image generation via the generate_image MCP tool.
When to Use
Generate images when the user:
- Asks for an image, illustration, or visual asset
- Needs a hero image, placeholder, icon, or background for a project
- Asks to edit, modify, or iterate on an existing image
- Mentions creating visual content of any kind
How to Use
Call the generate_image tool. The behaviour depends on the parameters:
Text-to-Image (no input images)
{
"prompt": "A modern dashboard UI with dark theme and blue accent colours",
"aspectRatio": "16:9",
"resolution": "2K",
"filename": "dashboard-hero",
"subfolder": "landing-page"
}
Image Editing (with input images)
{
"prompt": "Change the background to a sunset over water",
"images": ["./src/assets/hero.png"],
"aspectRatio": "16:9"
}
Multi-Turn Refinement
The tool returns a sessionId with every response. Pass it back to continue editing:
{
"prompt": "Make the colours warmer and add more contrast",
"sessionId": "session-1711929600000-a1b2c3"
}
This preserves the conversation history so the model remembers what it generated.
Output Organization
- Use
filenameto give images meaningful names (e.g.hero-bannerinstead ofgemini-1711929600000-a1b2c3) - Use
subfolderto group related assets (e.g.landing-page,blog-posts) - Duplicate filenames are auto-versioned:
hero.png,hero-v2.png,hero-v3.png - When generating assets for a project, save them directly to the project's asset directory using
outputDir
Image Processing (process_image)
Use process_image for local, free operations that don't need AI:
Common Pipelines
Subject on a specific background (canvas approach — recommended):
Create a solid colour canvas with process_image, then feed it to generate_image as input. Gemini places the subject with correct lighting and reflections — no chroma key needed.
// Step 1: generate_image with a canvas image as input
{
"prompt": "Place a yellow rubber duck on this background. Product photography, studio lighting, centered.",
"images": ["./canvas-white.png"],
"filename": "duck-on-white"
}
This is better than chroma key for yellow, green, or glass/reflective subjects.
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.
- 9d ago First seen · 160 lines · 18 tokens per session scan A f3634614d26f
image-generation is a skill published in the GitHub repository JimothySnicket/gemini-image-mcp (1 stars, last pushed 7d ago), licensed MIT. It adds 18 tokens to every session and 1,806 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 2026-08-31.
Other skills, from other repositories
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videoagent-image-studio
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image-generation
Optimizes image generation prompts using Subject-Context-Style structure. Use this skill when generating images, creating illustrations, photos, visual assets, editing images, or crafting prompts for any image generation model.
crossgen-artist
Use CrossGen to plan and execute image generation, editing, inpainting, model selection, media-aware job monitoring, Gallery inspection, and asset export through MCP or its JSON CLI.
model-aware-image-prompt-engineer
Model-aware image prompt engineering for any agent or image generation workflow. Use when writing, improving, translating, debugging, or evaluating prompts for OpenAI image models, Gemini Nano Banana, Midjourney, FLUX, Qwen-Image, Z-Image, Stable Diffusion, SDXL, Pony, Illustrious, NoobAI, Animagine, HunyuanImage…