Image generation

Skill event4u-app/agent-config/src/skills/image-generation

Universal AI Agent OS — audited skills, governance rules, replayable state. One contract, every host agent.

Install

npx -y skills add event4u-app/agent-config --skill image-generation

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One thing to look at

7 stars7 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Generate an image from a brief — provider-agnostic blueprint then provider-specific translation, with ref-image/seed reuse for consistency. Use when generating/creating an image.

SKILL.md

5.0 KB, as published. Nobody here has run it

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 src/scripts/ai-image/adapters/<provider>.sh with the assembled params. Validate the returned artifact path or dry-run confirmation. All adapters are experimental (scaffold-tier); no live generation occurs until a maintainer promotes the adapter via provider-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 per media-governance-routing before delivering the output.

Output format

Blueprint — provider choice + routing rationale + prompt string ready to copy.
Provider + prompt — adapter file reference, key params (aspect ratio, style, negative prompts), and any seed/ref-image value carried forward.
Artifact path / dry-run note — the path returned by the adapter, or an explicit note: "adapter is experimental (scaffold-tier) — dry-run plan only; no rendered asset until promotion per provider-lifecycle-discipline."

Gotcha

Scaffold-tier adapters produce plans, not pixels — all four adapters (ideogram.sh, flux.sh, recraft.sh, gemini-image.sh) are scaffold-tier (dry-run only). This skill produces the blueprint + dry-run confirmation; actual renders require a maintainer to capture a smoke trace and promote the adapter to stable. Claiming a rendered asset exists when no adapter is stable misleads the caller.
Seed reuse is the consistency lever — re-describing the subject more precisely in each prompt does not lock character or style; carrying the seed value or ref-image path forward does. Drop the seed and character drift is inevitable across a series.

Good example: Brief for a product-shot series → route to Flux (photoreal), author noun-phrase prompt, carry seed: 42 across all five renders, invoke adapter, note "experimental — dry-run plan only."

Bad example: Invoking the adapter and telling the user "here is your rendered image" while all adapters are still scaffold-tier.

Do NOT

Do NOT claim a rendered asset is produced while adapters are scaffold-tier — surface the dry-run caveat explicitly every time.
Do NOT skip image-likeness-and-rights when the brief names a real person, a brand mark, or a named living artist's style.
Do NOT bypass image-provider-routing — selecting the wrong provider for the job shape (e.g. Flux for a text-in-image logo) produces garbled output.
Do NOT ignore seed/ref-image values when consistency across a series is required — seed reuse is the only reliable consistency mechanism.