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 gabrielmoreira/agent-skills-mirror --skill image-gengit clone --depth 1 https://github.com/gabrielmoreira/agent-skills-mirrorWrote 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/gabrielmoreira/agent-skills-mirror/image-gen)<a href="https://agentmods.dev/skills/gabrielmoreira/agent-skills-mirror/image-gen"><img src="https://agentmods.dev/badge/skills/gabrielmoreira/agent-skills-mirror/image-gen/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/gabrielmoreira/agent-skills-mirror/image-gen"><img src="https://agentmods.dev/badge/skills/gabrielmoreira/agent-skills-mirror/image-gen.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00049 | $0.01386 |
| Opus 5.5 | $0.00020 | $0.00554 |
| Sonnet 5.5 | $0.00010 | $0.00277 |
| Haiku 4.5 | $0.00005 | $0.00139 |
Grade A, and why
image-gen 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 1mo 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 — 122 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Image Gen
Generate AI images via submit_image (configured provider keys only). Prefer one clear still per request unless the user asked for variants.
Model Selection
| Model | Reference | Strengths | Max refs |
|---|---|---|---|
fal + falModel |
references/fal.md | Explicit Fal catalog; see tool schema for per-model limits | Model-specific |
gpt-image-2 |
references/gpt-image-2.md | Best text rendering, strongest prompt adherence | 16 |
nano-banana |
references/nano-banana.md | Strongest reference-image fidelity | 14 |
image-01 |
references/image-01.md | MiniMax stills / live style; one subject reference via R2 | 1 |
grok-imagine |
references/grok-imagine.md | xAI Grok Imagine; text-to-image, ≤4 outputs, 1K/2K | 0 |
- If Fal.ai is selected or requested, use
model: "fal"and the requestedfalModelor saved Fal default from capabilities. Ask if none is selected. Native-provider defaults and controls below do not apply to Fal. - Default:
gpt-image-2when that key is on. - Reference-heavy →
nano-banana. - User named MiniMax / only MiniMax image key on →
image-01. - Respect capabilities: do not call a model whose vendor is not configured.
IMPORTANT: Before generating, READ the chosen model's reference.
Tool Params
| Param | Values | Default |
|---|---|---|
aspectRatio |
1:1, 16:9, 9:16, 4:3, 3:4, 3:2, 2:3, 4:5, 5:4, 21:9 |
16:9 |
imageSize |
512px, 1K, 2K, 4K (model-specific) |
1K |
width / height |
GPT Image: 512–3840, /16; MiniMax: 512–2048, /8 | — |
quality |
low, medium, high, auto (gpt-image-2 only) |
high |
referenceAssetIds |
Array of project asset ids — backend resolves bytes server-side | — |
name |
Short descriptive asset name shown in the library | — |
count |
Number of images to generate (1–10; image-01 max 9) | 1 |
promptOptimizer |
MiniMax image-01 only — prompt_optimizer |
false |
seed |
MiniMax image-01 only |
— |
maskAssetId, background, moderation, inputFidelity |
GPT Image edit/output controls | — |
outputFormat, outputCompression |
GPT Image PNG/JPEG/WebP controls | PNG |
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.
- 1mo ago Changed · +2 lines · +3 tokens per session 4931cf15ce42
- 1mo ago First seen · 120 lines · 46 tokens per session scan A 06d46c9c38fb
image-gen is a skill published in the GitHub repository gabrielmoreira/agent-skills-mirror (19 stars, last pushed yesterday), licensed MIT. It adds 49 tokens to every session and 1,386 once invoked, about $0.0002 per session on Opus 5.5. 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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