| name | image-ad-clone |
| description | Use when the user wants to reverse-engineer an existing image ad into a reusable, parameterizable prompt template that can be reused with any brand and any product. Triggers on phrases like "clone this ad as a template", "reverse engineer this ad", "turn this ad into a prompt", "extract a template from this image", "make this ad reusable", "add this to my prompt library", "study this ad and make a template". Anchors on input being an EXISTING ad image the user provides — does NOT trigger for fresh ad generation requests (use uni1-image-ad for that). |
image-ad-clone
Take an existing image ad and turn it into a reusable, parameterizable uni-1 prompt template that can be plugged into any brand. Output: a new entry appended to the user's prompt library, ready to be used by uni1-image-ad (or anywhere else) on future generations.
This is the template-creation skill. The companion skill uni1-image-ad is the template-using skill (it generates and uploads ads from filled-in templates). They're meant to be installed together.
Hard rules — never relax
-
Strip platform/screenshot chrome from analysis. When you describe what's in the reference, you're describing the actual ad creative, not the screenshot wrapper. Do not include iOS status bars, "Sponsored"/"Saved" badges, post text/captions surrounding the image, link-card footers, engagement rows, platform tab bars. If the reference is a screenshot of an ad-in-feed, mentally crop the wrapper. The output template must produce a standalone image that would be uploaded as a Meta creative.
-
Always validate by generating. A template that hasn't been round-tripped through uni-1 against the original isn't validated. Run at least one generation with --image-ref <original> and compare. Refine the prompt until the structure matches.
-
Always test the generalized version. Before saving, fill the placeholders with a different brand (use AG1 if the user has no preference — there's a known reference at iterations/ag1-v2/T1-ios-notes/ or the user can supply one) and generate. If the structure breaks, the placeholder set is wrong — fix it.
-
Never write brand-specific text into the final template. Wordmarks, product names, slogans, specific photographs, hex colors specific to the source brand — all become {placeholders}. Only structural content (layout descriptions, photography style, typography family, composition rules) remains literal.
-
Save to the user's library, do not silently overwrite. Default save target is ~/.claude/skills/uni1-image-ad/references/prompt-library.md if it exists; otherwise propose a project-local prompt-library.md and ask. If the target template name (e.g., T8 — Apple Notes listicle) collides with an existing entry, ask the user before overwriting.
Inputs the user must provide
| Input | Notes |
|---|
| Reference ad image | Path to a local file (PNG/JPG/WEBP). The thing being reverse-engineered. |
| Brand to test against (optional) | If they want the test fill to use a specific brand. Default: AG1 if any AG1 assets exist at ag1-assets/, otherwise ask. |
| Save target (optional) | Defaults to ~/.claude/skills/uni1-image-ad/references/prompt-library.md. |
| Template tag (optional) | Short identifier like T8, Lifestyle Hero, Carousel Cover — Claude proposes one based on the analysis if not given. |
Workflow
Phase 1: Preflight
- The reference image path resolves to an existing file. If not, stop and ask.
LUMA_API_KEY is set in the project .env. If not, instruct the user how to get one.- The companion
generate_image.py script is available. Look in this order:
~/.claude/skills/uni1-image-ad/scripts/generate_image.py<repo>/skills/uni1-image-ad/scripts/generate_image.py- If neither: stop and ask the user to install
uni1-image-ad (this skill's hard dependency).
- Read the save target. If the file exists, read its current entries to know what tags are taken. If not, plan to create it.
Phase 2: Visual analysis
This is the most important phase. Read the reference image and describe what you see, structurally separating brand-specific content from format/structure. Document each of these:
- Aspect ratio. Measure or estimate (W:H). Map to the closest Luma-supported ratio:
16:9, 1:1, 1:2, 1:3, 2:1, 2:3, 3:1, 3:2, 9:16. (4:5 and 1.91:1 are NOT supported — use 2:3 and 16:9 respectively.)
- Format type. What this ad pretends to be: editorial article, product flatlay, comparison table, fake search results, story composite, native UI mimic, etc.
- Layout structure. Header / hero / footer / grid — how regions are arranged.
- Typography. Family (geometric sans, condensed sans, serif, handwritten marker, monospace), weight, hierarchy. Do NOT name specific fonts unless they're iconic and necessary; instead describe the feel.
- Color palette. 3-6 hex codes. Identify which are brand-specific (will become
{brand.color_*} variables) vs neutral/structural (white/black/grey backgrounds — stay literal).
- Photography style. Studio product flatlay, lifestyle UGC, editorial portrait, stock-photo-grid, etc. Describe lighting and lens.
- Text content (verbatim). Every visible string in the image. Mark which strings are brand-specific (e.g. "AG1", "Drink AG1") vs structural (e.g. "AS SEEN ON", "VS").
- Decorative / non-text elements. Icons, divider lines, badges, emojis, hand-lettering, sticky-note props.
- Branded vs structural elements. This is the key column. For everything you've described, mark each piece as
[BRAND] (will become a variable) or [STRUCTURE] (stays literal in the template).
- Chrome to strip. Anything you saw that's a screenshot/platform artifact (iOS status bar, Sponsored badge, engagement row). Note it for explicit exclusion in the prompt.
State this analysis to the user as a compact summary. Don't move on until it's complete.
Phase 3: Draft v1 prompt (faithful, brand-specifics intact)
Write a prompt that, paired with the reference image as --image-ref, would reproduce the ad faithfully. At this stage, leave brand-specific content literal — do not placeholder-ize yet.
Structure the prompt with these sections (omit any that don't apply):
- Aspect ratio + canvas (e.g. "1:1 static ad creative, 1080x1080, edge-to-edge")
- Background description
- Header section (top X% of the image)
- Main content / hero section
- Decorative elements (badges, dividers)
- Bottom section / footer band
- Typography note (weight, family-feel, hierarchy)
- Composition / spacing rules
- Explicit chrome exclusion — name what NOT to render (the script's no-chrome suffix is a safety net; the prompt should also explicitly exclude)
Show the v1 prompt to the user.
Phase 4: Generate with reference
Use the discovered generate_image.py to fire one generation. Pass the original reference as --image-ref and the matched aspect ratio.
<path-to>/generate_image.py \
--prompt "$(cat /tmp/v1.prompt)" \
--aspect-ratio <matched_ratio> \
--image-ref <reference_path> \
--out iterations/clone-tmp \
--env-file .env
(Write the prompt to a temp file to avoid shell-quoting hell.) Wait for completion (~50s with reference). Read the generated image.
Phase 5: Compare and iterate
Show user the original side-by-side description with the generated. Identify deltas:
- Layout regions misplaced or missing
- Typography weight wrong
- Wrong aspect ratio interpretation
- Brand color drifted
- Decorative elements (icons, badges) wrong or missing
Refine the prompt based on the deltas. Regenerate. Repeat until the structure is faithful enough to call it "good." Cap at 4 iterations — beyond that, the prompt has a structural problem and needs more dramatic editing rather than tweaking.
Phase 6: Generalize into placeholders
This is where the template becomes reusable. Walk back through the v1 prompt and replace every [BRAND]-marked element from Phase 2 with a {placeholder} variable. Use the standard placeholder vocabulary:
Standard variables (use these names where they fit):
{brand.name} — wordmark text{brand.color_primary} — primary brand color hex (e.g. #1A4731){brand.color_accent} — secondary accent color hex (if used){brand.product_image_description} — one-line description of the product visible in the ad{brand.tagline} — short brand promise{brand.competitor_category} — for comparison templates: what's being compared against{ad.headline} — top-line headline copy{ad.subcopy} — sub-headline / supporting copy{ad.body} — primary text block{ad.cta_phrase} — CTA button text
Template-specific variables — name them clearly when needed:
{checklist_items[]} (Notes-style){tweet_body} (story templates){rows[]} (comparison templates){publication} (editorial templates){ugc_subject} (UGC photo composite templates)
For each variable: write a 1-line description of what it represents and what kind of value goes in it.
Phase 7: Test the generalized template
Pick a different brand (default AG1 if assets exist; otherwise ask). Substitute test values into every placeholder. Generate again with --image-ref set to the test brand's product photo (NOT the original ad). The output should:
- Have the same layout/composition as the original
- Show the test brand instead of the source brand
- Read as a coherent ad, not a frankenstein
If the test fails the structure breaks under different brand assumptions, return to Phase 6 and refine the placeholder set. Often the fix is making a placeholder that was missed (e.g. you hardcoded a font feel that's specific to one brand).
Phase 8: Document the template
Compose the library entry. Use the format in references/template-format.md — it has the markdown skeleton.
The entry must include:
- Tag and one-line title (e.g.
T8 — Lifestyle hero with overlay text)
- When to use — 1-2 sentences on positioning fit
- Aspect ratio recommendation
- Reference image guidance — what kind of
--image-ref to pass when reusing this template (product hero? logo? lifestyle portrait?)
- Variable schema — every
{placeholder} with a 1-line description
- Template prompt in a fenced code block, ready to copy-paste-fill
- Example fill — the test fill from Phase 7, showing the variables substituted
- Validated example path — pointer to the iteration dir (e.g.
iterations/clone-2026-05-04/T8/)
Phase 9: Save and confirm
- Append the entry to the configured library file. If overwriting an existing tag, ask first.
- Print the entry's path so the user can review.
- Move the validated PNGs from
iterations/clone-tmp/ to a permanent dir keyed by the template tag (e.g. iterations/clone-2026-05-04/T8/).
- Tell the user the template is now available for use by
uni1-image-ad. Suggest a follow-up: "Want me to make a real ad with this template right now?"
Naming convention for new templates
If the save target already has T1–T7 (the seeded templates), continue with T8, T9, … Use semantic suffixes if helpful: T8 — Lifestyle hero, T9 — Carousel cover. Keep the T<n> part for cross-skill referencing.
Out of scope
- Generating real ads / uploading to Meta. That's
uni1-image-ad. This skill produces templates, period.
- Reverse-engineering video ads. Image only. Refuse with: "This skill is for static image ads. Video reverse-engineering isn't supported in this version."
- Multi-template extraction in one run. One reference → one template per skill invocation. If the user has 5 references, do 5 sequential runs (or batch via the same flow once you've done one).
- Modifying existing templates in the library. If the user wants to revise T3, treat it as a new run pointed at the same library entry — show the diff and ask before overwriting.
Files this skill owns
~/.claude/skills/image-ad-clone/SKILL.md — this file~/.claude/skills/image-ad-clone/references/template-format.md — load this in Phase 8 before composing the library entry<cwd>/iterations/clone-<date>/<tag>/ — per-run iteration outputs (gitignored PNGs, kept prompts)
Files this skill writes to (in user space)
- The configured prompt library (default:
~/.claude/skills/uni1-image-ad/references/prompt-library.md) — appended, never silently overwritten
<cwd>/iterations/clone-<date>/<tag>/prompt.txt — the final validated prompt<cwd>/iterations/clone-<date>/<tag>/v1.png, v2.png, … — each iteration's output
Dependencies
- The
uni1-image-ad skill must be installed (this skill uses its generate_image.py for iteration). If not installed, fail Phase 1 with a clear instruction.
LUMA_API_KEY in .env.- Python 3.12+ (for the helper script).
