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 event4u-app/agent-config --skill image-generationgit clone --depth 1 https://github.com/event4u-app/agent-configWrote 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/event4u-app/agent-config/image-generation)<a href="https://agentmods.dev/skills/event4u-app/agent-config/image-generation"><img src="https://agentmods.dev/badge/skills/event4u-app/agent-config/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/event4u-app/agent-config/image-generation"><img src="https://agentmods.dev/badge/skills/event4u-app/agent-config/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 | $0.00037 | $0.01203 |
| Opus 5 | $0.00018 | $0.00602 |
| Sonnet 5 | $0.00007 | $0.00241 |
| Haiku 4.5 | $0.00004 | $0.00120 |
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 — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
image-generation
Generate an image end-to-end: capture the brief, route to the right provider, author
a provider-specific prompt, reuse seeds for consistency, invoke the adapter, and apply
governance. All adapters are scaffold-tier (dry-run) until promoted to stable.
When to use
- User asks to generate, create, or produce an image from a brief.
- End-to-end image production (routing + prompting + adapter invocation + governance).
- When a consistent series of images needs seed/ref-image reuse across renders.
Procedure
- Capture the brief — extract: subject, output format (raster/vector/banner/icon), style intent, any existing ref images or seed values for consistency.
- Route the provider via
image-provider-routing— match job shape (text-in-image → Ideogram, photoreal → Flux, vector → Recraft, general → Gemini/GPT Image 2). - Author the prompt via
prompt-engineering-image— apply provider-specific grammar (text-literal first for Ideogram, noun-phrase for Flux,style:param for Recraft, natural language for Gemini/GPT). - Reuse ref-image / seed for consistency — if the brief is part of a series, carry the seed value or ref-image path forward. Seed reuse is the primary consistency lever; re-describing the subject each time is not.
- Invoke the adapter (dry-run today) — run
node_modules/@event4u/agent-config/src/scripts/ai-image/adapters/<provider>.shwith the assembled params. Validate the returned artifact path or dry-run confirmation. All adapters areexperimental(scaffold-tier); no live generation occurs until a maintainer promotes the adapter viaprovider-lifecycle-discipline. - Apply governance — run the rights check (
image-likeness-and-rights) when the brief names a real person, brand mark, or living artist's style. Attach the AI-disclosure footer permedia-governance-routingbefore delivering the output.
What ships with it
1 file 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.
- 9d ago First seen · 100 lines · 37 tokens per session scan A b0a2d0b4ff0d
image-generation is a skill published in the GitHub repository event4u-app/agent-config (10 stars, last pushed today), licensed MIT. It adds 37 tokens to every session and 1,203 once invoked, about $0.0002 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 2026-09-03.
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