/image-ad-clone

name: image-ad-clone
description: Use when the user wants to reverse-engineer an existing image ad into a reusable prompt template. Validates via Arcads — picks gpt-image-2 or Nano Banana at Phase 1. Triggers on "clone this ad as a template", "reverse engineer this ad", "turn this ad into a prompt", "extract a template", "make this ad reusable", "add to my prompt library", "study this ad and make a template". Input is an EXISTING ad image; does NOT trigger for fresh generation (use chatgpt-image-ad or nano-banana-image-ad).

image-ad-clone (Arcads)

Take an existing image ad and turn it into a reusable, parameterizable prompt template that gets appended to the shared **37-template image-ad library**. The template is validated by round-tripping through one of the Arcads image-ad generators — **ChatGPT Image 2** (typography / UI-mimicry templates) or **Nano Banana** (photoreal / lifestyle / multi-reference templates).

This skill replaces the older Uni1-locked `image-ad-clone` (which only worked with Luma uni-1). It's backend-agnostic: at Phase 1 the agent asks you (or auto-detects from the reference) whether to validate against gpt-image-2 or Nano Banana, then routes through the matching generator script in this repo.

Read order

1. **This file** — Arcads-specific generator paths, model-choice decision, what's locked at the per-repo layer. 2. **[shared/skills/image-ad-clone/prompting/guide.md](../../shared/skills/image-ad-clone/prompting/guide.md)** — the full model-agnostic 10-phase workflow (visual analysis → draft prompt → generate-with-reference → iterate → generalize → test → cross-model validate → document → save). 3. **[shared/skills/image-ad-prompting/prompting/template-format.md](../../shared/skills/image-ad-prompting/prompting/template-format.md)** — entry skeleton. 4. **[shared/skills/image-ad-prompting/prompting/prompt-library.md](../../shared/skills/image-ad-prompting/prompting/prompt-library.md)** — destination for the new entry. 37 validated templates already there; new entries go at T40+.

Hard rules

Inherits all 6 hard rules from the shared guide (strip platform chrome, validate by generating, test the generalized version, no brand-specific text in the final template, never silently overwrite, document model notes for both backends). Plus per-repo:

7. **Backend is one of: ChatGPT Image 2 OR Nano Banana on Arcads.** Never uni-1. The script choice happens in Phase 1 once the user picks (or the agent auto-detects).

Picking the right backend in Phase 1

Pick by what the reference ad is showing — most templates fall into one clear bucket.

**Use `chatgpt-image-ad` (gpt-image-2) when the reference is:**

• Typography-heavy / UI mimicry (Apple Notes lists, fake Google search, fake Slack threads, ChatGPT-conversation ads, iMessage screenshots, comparison tables, fake AirDrop dialogs, Hinge-style cards, calendar UI, weather forecast UI, magazine masthead)
• Brutalist / editorial typography heros (huge type makes the joke)
• Dense small text inside UI elements

**Use `nano-banana-image-ad` (Nano Banana family) when the reference is:**

• Photoreal handheld objects (whiteboards, napkins, sticky notes, letter boards, scratch-off tickets)
• Aspirational lifestyle photography (sunset, kitchen at golden hour, OOH / transit)
• Multi-image reference blending (logo + product + style + character all in one)
• Clay / claymation / Pixar-adjacent textures

**If the reference straddles both** (e.g. a UGC-style photo with rendered text overlays), the safer default is to clone twice — once per backend — and ship the template with `Model notes` saying which renders cleaner. The agent will offer this in Phase 8.

If the user explicitly says "clone this with gpt-image-2" or "with Nano Banana", honor that.

Dependencies

This skill uses the matching generator script in the SAME repo:

• For gpt-image-2 validation: `skills/chatgpt-image-ad/scripts/generate_image.py` (locked to `model: gpt-image-2`)
• For Nano Banana validation: `skills/nano-banana-image-ad/scripts/generate_image.py` (`nano-banana-2` default; `--model nano-banana-pro` or `nano-banana-edit` opt-in)

Fail Phase 1 with a fix-it message if neither generator is installed in this repo.

Also required:

• `.env` with `ARCADS_BASIC_AUTH` or `ARCADS_API_KEY`
• (Optional) `PRODUCT_ID` in `.env` — if not set, the generator auto-fetches the first Arcads product
• Python 3.12+

Where this skill's generator lives

When the [shared guide](../../shared/skills/image-ad-clone/prompting/guide.md) Phase 1 tells you to locate the companion generator, look here in order based on the model choice:

For gpt-image-2: 1. `~/.claude/skills/chatgpt-image-ad/scripts/generate_image.py` 2. `<repo>/skills/chatgpt-image-ad/scripts/generate_image.py` 3. If neither: stop and ask the user to install `chatgpt-image-ad` first.

For Nano Banana: 1. `~/.claude/skills/nano-banana-image-ad/scripts/generate_image.py` 2. `<repo>/skills/nano-banana-image-ad/scripts/generate_image.py` 3. If neither: stop and ask the user to install `nano-banana-image-ad` first.

Aspect ratio mapping

The Arcads image endpoint (`/v2/images/generate`) accepts only **`1:1`, `16:9`, `9:16`** — regardless of which model (gpt-image-2 or nano-banana) you're hitting. When measuring the original ad's aspect (Phase 2):

• `4:5`, `2:3`, `5:4` ads → render at `1:1` and post-crop in your downstream ad-builder skill
• `1.91:1` ads → render at `16:9` and post-crop
• `9:16` ads → native, no change

Document the ratio fallback in the template's `Aspect ratio:` field so future users know they're rendering at a mapped ratio, not the original.

(The KIE per-API repo's `image-ad-clone` skill supports a broader native ratio set — `4:5`, `2:3`, `3:2`, etc. — because KIE's `/jobs/createTask` Nano Banana endpoint accepts them. If aspect-ratio fidelity matters more than Arcads-specific control, consider the KIE repo for that template.)

Workflow phases (model-agnostic, see shared guide for fu