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 notque/vexjoy-agent --skill image-gengit clone --depth 1 https://github.com/notque/vexjoy-agentWrote 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/notque/vexjoy-agent/image-gen)<a href="https://agentmods.dev/skills/notque/vexjoy-agent/image-gen"><img src="https://agentmods.dev/badge/skills/notque/vexjoy-agent/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/notque/vexjoy-agent/image-gen"><img src="https://agentmods.dev/badge/skills/notque/vexjoy-agent/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 | $0.00026 | $0.01332 |
| Opus 5.5 | $0.00010 | $0.00533 |
| Sonnet 5.5 | $0.00005 | $0.00266 |
| Haiku 4.5 | $0.00003 | $0.00133 |
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 25d 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 — 157 lines — stays where its author put it; the contents beside it link to each section on GitHub.
image-gen
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).
Deep References
| 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 |
Phase 1: Detect Backend
python3 skills/content/image-gen/scripts/detect-backend.py
gemini-- loadreferences/backends/gemini.mdask-- no key found; ask the user to setGEMINI_API_KEY
Gate: backend confirmed.
Phase 2: Write Prompt Files
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
For series: write ALL prompt files before calling any generation script.
Gate: all prompt files written and reviewed.
Phase 3: Select Script
| Use case | Script | Subcommand |
|---|---|---|
| Single image, Gemini | scripts/generate_image.py |
--prompt |
| Batch from text file, Gemini | scripts/generate_image.py |
--batch |
| Single with post-processing | scripts/nano-banana-generate.py |
generate |
| Style match from reference | scripts/nano-banana-generate.py |
with-reference |
| Batch from JSON manifest | scripts/nano-banana-generate.py |
batch |
| Series (anchor chain) | scripts/nano-banana-generate.py |
generate then with-reference |
| Post-processing only | scripts/nano-banana-process.py |
crop / remove-bg / pipeline |
What ships with it
6 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.
- 26d ago Changed · -58 lines 100bbb6f258f
- 27d ago Changed · +1 lines a32cf71cba3e
- 1mo ago First seen · 156 lines · 26 tokens per session scan A cc07748ed99e
image-gen is a skill published in the GitHub repository notque/vexjoy-agent (442 stars, last pushed today), licensed MIT. It adds 26 tokens to every session and 1,332 once invoked, about $0.0001 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 2026-09-03.
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