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 — 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 — entry skeleton.
4. 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 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 full detail)

1. Phase 1: Preflight + model choice. Reference image file resolves; .env has Arcads creds; both generators detected. Ask the user which model to validate against (or auto-detect from the reference's typography-vs-photo balance).
2. Phase 2: Visual analysis. Describe the reference structurally — aspect ratio, format type, layout, typography, color palette, photography style, every text string verbatim, decorative elements, chrome to strip.
3. Phase 3: Draft v1 prompt (brand-specifics intact).
4. Phase 4: Generate with reference using the matching generator script. Pass --image-ref <reference_path> and the matched aspect ratio.
5. Phase 5: Compare and iterate. Refine prompt based on deltas. Cap 4 iterations.
6. Phase 6: Generalize into placeholders ({brand.name}, {brand.color_primary}, etc.).
7. Phase 7: Test the generalized template against a DIFFERENT brand. Regenerate. If structure breaks, refine placeholder set.
8. Phase 8: Cross-model validation (recommended). Run the same template through the OTHER backend in this repo. Document deltas in the Model notes block.
9. Phase 9: Document the template. Use the format in template-format.md. Required fields: tag, when-to-use, aspect ratio, reference image guidance, variable schema, template prompt (full validated), example fill, Model notes for both backends, validated example path.
10. Phase 10: Save and confirm. Append to shared/skills/image-ad-prompting/prompting/prompt-library.md. Print path. Move PNGs to permanent iteration dir.

Iteration directory layout

<cwd>/iterations/clone-2026-05-26/
  T40-lifestyle-hero/
    prompt.txt
    v1.png, v2.png, …                # against the source ref (chosen backend)
    test-fill-v1.png, …              # Phase 7 generalization test against a different brand
    cross-{other-backend}/v1.png     # Phase 8 cross-model validation (optional)
    notes.md

Common Model notes patterns to write in Phase 9

**Model notes:**
- **gpt-image-2:** {observed behavior — e.g. "clean — strong on UI mimicry and table text", "tends to add a 4th Slack message — keep prompt explicit about exactly N", "small chart axis labels blur — bump font size feel"}
- **nano-banana:** {observed behavior — e.g. "strong — preferred backend for handheld board photos", "character identity drifts across variants on -2; use -pro to lock", "weak on dense table text — keep rows to 4 max"}

If you only validated against one model in Phase 8, say so explicitly:

**Model notes:**
- **gpt-image-2:** validated clean (see iteration path)
- **nano-banana:** untested — validate before using on nano-banana-image-ad backend

The diff between a uni-1-era library and this one is this Model notes block. Don't skip it — it's the difference between a portable template and one nobody knows how to use.

See also

• shared/skills/image-ad-clone/prompting/guide.md — full 10-phase workflow
• shared/skills/image-ad-prompting/OVERVIEW.md — ecosystem context
• chatgpt-image-ad skill — gpt-image-2 generator (used for validation when you pick that backend)
• nano-banana-image-ad skill — Nano Banana generator (used for validation when you pick that backend)
• arcads-external-api skill — Arcads conventions (session folder, credit cost confirmation, presigned-upload flow)