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/event4u-app/agent-confignpx agentmods add skills/event4u-app/agent-config/image-creatorWrote 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-creator)<a href="https://agentmods.dev/skills/event4u-app/agent-config/image-creator"><img src="https://agentmods.dev/badge/skills/event4u-app/agent-config/image-creator/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-creator"><img src="https://agentmods.dev/badge/skills/event4u-app/agent-config/image-creator.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 | $0.00046 | $0.01415 |
| Opus 5 | $0.00023 | $0.00707 |
| Sonnet 5 | $0.00009 | $0.00283 |
| Haiku 4.5 | $0.00005 | $0.00142 |
Grade A, and why
image-creator 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 — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.
image-creator
Turn a Canon Spec + a scene into a maximally-detailed, reproducible generation prompt that renders a character to spec — then hand the result to
image-analyserto verify. The loop partner. Schema + rubric + loop:canon-spec.md.
When to use
- "Generate / render / create this character", "render Veikko in scene X to spec", "make the image match the canon".
- Inside the fidelity loop, fed by
image-analyser's correction directives.
NOT for: scene blocking / motion (→ video-director, motion-choreographer,
which take a verified still from here), non-character art (→ canvas-design).
Input
- Character id / Canon Spec (
agents/reference/ai-video/<project>/characters/<id>.json). - Scene brief (setting + pose). Optional: prior
image-analyserdiff (loop mode).
Procedure
- Governance gate FIRST (per
media-governance-routing): if the character is a real-person likeness, consultagents/settings/policies/media/likeness.mdpublic-figures.md+disclosure.mdbefore emitting anything. Fictional characters (odins-beard trio) are exempt; the routing decision is in-session.
- Provider gate (per
provider-lifecycle-discipline): read the resolved provider's tier; if non-stable (experimental/deprecated/community), surface the tier and ask before running. Never default to a non-stable provider silently. Name the provider + tier in the run summary. - Assemble the prompt from the spec — anchors first. Order matters: the
hard-to-render
identity_anchorsgo at the TOP (the canon's lesson — heterochromia + hair-split get forgotten if buried). Then physique, face (+ marks), per-location tattoos (incl. exacttext), outfit, jewelry. - Asymmetry block — for split / heterochromatic characters, an explicit left/right section ("LEFT half black / RIGHT half blond", "blue LEFT eye / green RIGHT eye") with concrete comparison refs for engines that drop it.
- Negative block + engine settings — reuse the canon's proven structure
(negatives that kill "single hair colour", "both eyes same colour", etc.;
per-engine
--ar/--style/CFG/steps). Do not reinvent; the character book's prompt format is the template. - Generate through the existing provider/adapter layer
(
scripts/ai-video/adapters/, the/video|imagesurface). Do not add a new provider path where one exists. - Verify — hand the output to
image-analyser; in loop mode, fold its correction directives into the next prompt (see the loop incanon-spec.md).
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 · 118 lines · 46 tokens per session scan A d0ab52a88a91
image-creator is a skill published in the GitHub repository event4u-app/agent-config (10 stars, last pushed today), licensed MIT. It adds 46 tokens to every session and 1,415 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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