| name | packaging-mockup |
| description | Generate Amazon packaging mockup variations for split testing — Sunspot Packaging Test™, Open Package Test™, Back Packaging Test, Brand Name Test variations using Higgsfield Flux Kontext / Nano Banana. Use when running `packaging-mockup {ASIN} --variant={test-type}` or when running ProductPinion pre-defined templates that need packaging variations. |
packaging-mockup — Packaging Variation Generator
Several ProductPinion pre-defined templates (Sunspot, Open Package, Back Packaging, Brand Name, Tilted Image) need packaging variations to test. This skill generates them via Higgsfield Flux Kontext or Nano Banana.
Prerequisites
- Higgsfield MCP
- Product reference image (current packaging)
Invocation
packaging-mockup {ASIN} --variant=sunspot # solid color / darker package
packaging-mockup {ASIN} --variant=open # product opened, contents visible
packaging-mockup {ASIN} --variant=back # back of packaging visible
packaging-mockup {ASIN} --variant=brand-name # alternate brand name on label
packaging-mockup {ASIN} --variant=tilted # angled / tilted main image
packaging-mockup {ASIN} --variant=keyphrase # visible keyphrase on label
packaging-mockup {ASIN} --all # generate all 6 variants
Output
/tmp/cro-content/{ASIN}-packaging-{variant}.png — one or more mockup images.
Variant Recipes
Sunspot Packaging Test™
Pull current product, generate variant with:
- Single dominant solid color (different from current)
- Darker overall tone
- Same logo placement
- Hides label complexity
Open Package Test™
- Product unboxed / opened
- Lid removed
- Contents partially visible
- Stops the scroll via curiosity
Back Packaging Test
- Back of packaging facing forward
- Shows ingredient list / spec / story
- Useful when back has differentiation copy
Brand Name Test
- Same packaging, alternate brand name on front
- Test 2-3 brand name candidates
- Use a separate ProductPinion poll to pick
Tilted Image Test
- Same packaging, angled/tilted vs straight-on
- Tests which presentation reads better at thumbnail
Keyphrase Test™
- Add a visible keyphrase to the label/front
- Examples: "0g Sugar" / "100% Natural" / "Made in USA"
- Tests whether claim-on-package improves CTR
Phase 1 — Generate
Higgsfield Flux Kontext for packaging modifications (best at structured edits to existing imagery). Nano Banana Pro fallback for cleaner standalone generation.
Phase 2 — Validate Compliance
Amazon main image rules apply if used as main:
- Pure white background
- 85%+ product fill
- No text added that violates compliance (varies by category — some allow on-package text, some don't)
Phase 3 — Output
# Packaging Mockup — {ASIN}
**Variant:** {type} | **Date:** {date}
## Current vs Variant
| | Current | Variant |
|---|---------|---------|
| Image |  |  |
## Compliance Check
- White BG: ✅
- Product fill: 87%
- No added text: ✅
## Recommended Next Skill
- Test against current: `main-image-poll {ASIN} --concepts=current,variant`
- Or stage in main image pipeline: `main-image-pipeline {ASIN}`
Reference Files
- ProductPinion docs — pre-defined poll templates
~/.claude/skills/cro/main-image-best-practices.md — Amazon compliance rules
Quality Bar
Auto-Triggers
- ProductPinion test plans that reference these template names
- User asks "generate {variant} mockup"
v0.1.12 — Read brain.image_strategy before generating
Before drafting any Higgsfield prompt, read brain.image_strategy from working memory:
- If null → pause and offer
image-strategy first (~10 min, makes every future render bespoke). If user proceeds without, flag concept cards with "No category research used — generic aesthetic."
- If set + fresh (<90d) → inject
prompt_adjustments.scene_keywords into prompt section 1, palette_keywords + mood_keywords into section 4, and do NOT include {anti_patterns_csv} near the end. Cite the strategy on each concept card.
- If stale (>90d) → flag and recommend
image-strategy --refresh.
This brand-level strategy comes from skills/image-strategy.md's top-15-bestsellers analysis. Same adjustments apply across every render for this brand.
v0.1.13 — Visual verification gate + base64 embedding (NON-NEGOTIABLE)
Florence does NOT present an image she hasn't actually looked at, AND she does NOT use raw Higgsfield URLs in artifact HTML. Two blocking rules added in v0.1.13:
Verification gate
After Higgsfield returns each generated image, BEFORE adding to any artifact OR sending to Pinion:
- Load the image into Florence's multimodal context — paste the URL in chat so Cowork's multimodal Claude loads it natively. Narrate: "Looking at #{N} before I include it."
- Visually inspect against: aspect ratio (1:1 main+listing / 16:9 A+), product fidelity vs reference, technique landed (visibly), background appropriate, no clipart leak (anti-clipart rules), no text-on-main-image (TOS), no model faces.
- If anything fails → re-prompt with specific fix + regenerate. Cap at 3 attempts per concept; surface honestly on attempt 4.
- Only after ALL concepts pass → proceed to artifact emission.
The eye trumps the score. Even if the rubric said 100/100, if visual inspection finds a wrong product or off-aspect output, it fails the gate.
Base64 embedding
Higgsfield URLs are temporary AND Cowork's artifact iframe sandbox blocks external image loads in many builds. For every verified concept:
- HTTP GET the Higgsfield URL → fetch image bytes
- Detect MIME type from response headers (typically
image/png)
- Base64-encode the bytes
- Substitute
{{image-src}} (or {{winner-image-src}} for tests) with data:image/png;base64,<encoded> — NOT the raw Higgsfield URL
This makes the artifact self-contained — survives sandbox + URL expiry. ~1-3 MB per image is fine for Cowork.
The live product image ({{product-image-url}} in concepts.html hero strip / dossier head / cockpit product cards) stays as the live Amazon CDN URL — that's permanent and not affected.
