git:20260727.51710ca to git:20260823.f8dbcee

3 added, 0 removed. Audit A to A.

---

model_tier: inherit

name: image-generation

description: "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."

domain: product

personas: []

workspaces:

- small-business

packs:

- ai-image

trust:

level: experimental

install:

removable: true

+ scope:

+ write: []

+ verification_reason: "execution declares no handler, so this skill runs nothing of its own — every write is the calling agent's, under the rules that govern it. No command can prove a scope the skill never executes."

execution:

type: manual

---

# 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

1. **Capture the brief** — extract: subject, output format (raster/vector/banner/icon),

style intent, any existing ref images or seed values for consistency.

2. **Route the provider** via [`image-provider-routing`](../image-provider-routing/SKILL.md) —

match job shape (text-in-image → Ideogram, photoreal → Flux, vector → Recraft,

general → Gemini/GPT Image 2).

3. **Author the prompt** via [`prompt-engineering-image`](../prompt-engineering-image/SKILL.md) —

apply provider-specific grammar (text-literal first for Ideogram, noun-phrase for Flux,

`style:` param for Recraft, natural language for Gemini/GPT).

4. **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.

5. **Invoke the adapter (dry-run today)** — run

`node_modules/@event4u/agent-config/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`.

6. **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

1. **Blueprint** — provider choice + routing rationale + prompt string ready to copy.

2. **Provider + prompt** — adapter file reference, key params (aspect ratio, style,

negative prompts), and any seed/ref-image value carried forward.

3. **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.

## See also

- [`image-provider-routing`](../image-provider-routing/SKILL.md) — select the right provider before writing the prompt.

- [`prompt-engineering-image`](../prompt-engineering-image/SKILL.md) — translate the brief into provider-specific prompt grammar.

- [`image-likeness-and-rights`](../../rules/image-likeness-and-rights.md) — rights check before generating real-person likenesses or brand marks.

- [`provider-lifecycle-discipline`](../../rules/provider-lifecycle-discipline.md) — lifecycle tier gates; read before any adapter invocation.