| name | main-image-concepts |
| description | Generate 5-10 Amazon main image concepts for an ASIN using Higgsfield AI image generation. Defaults to GPT Image (latest) and Nano Banana Pro — best-quality only. Each concept is scored against the 6-dimension main image rubric (Fidelity, Background, Scroll-Stop, Compliance, Creative, Quality). Use when running `main-image-concepts {ASIN}`, when you need standalone main image ideation without polling, or as a sub-step of `main-image-pipeline`. |
main-image-concepts — AI-Generated Main Image Concepts
Standalone concept generation using Higgsfield. Replaces the early "designer ideation" cycle (5-10 days, $500-$2000) with same-day AI concepts that go to designers for polish.
Methodology — read before rendering
Read reference/02-visual-content/main-image-creative-director.md in full before generating any concept. That file is the source-of-truth for: the 8 enhancement techniques (one per variant, no repeats), the 6-section mandatory prompt structure, the 5 thumbnail rules, the 4-axis scoring rubric (40/30/15/15), and the iteration loop.
Hard rules from that file (non-negotiable):
- Aspect ratio: 1:1, locked in at start AND end of every prompt
- Reference image always: pull live product photo from
brain/products/{asin}-{geo}.json and pass to Higgsfield's image-to-image input
- Brand guidelines respected: read
brain.business.brand_guidelines for colours / fonts / visual style keywords; if empty, fall back to neutral premium aesthetic and flag on the concept card
- Cap at 3 attempts per concept before surfacing failure honestly
Output emits florence-concepts-{asin} per the artifact protocol.
Prerequisites
Higgsfield MCP must be connected at https://mcp.higgsfield.ai/mcp. If not connected:
- Tell user: "Higgsfield MCP isn't connected. Open Claude Settings → Connectors → Add Custom Connector → URL
https://mcp.higgsfield.ai/mcp → sign in with Higgsfield account."
- Pause until connected, then resume.
Invocation
main-image-concepts {ASIN}
main-image-concepts {ASIN} --count=8 # default 8
main-image-concepts {ASIN} --model=gpt-image # or nano-banana-pro (default), or both
main-image-concepts {ASIN} --research={path} # use existing research brief
Output
Writes to /tmp/cro-content/{ASIN}-main-concepts-{date}.md with embedded image URLs and a scoring grid. Saves images to /tmp/cro-content/{ASIN}-main-concepts/ directory.
Default Models (Best-Only Policy)
Per the skill plan: best-quality models only, no multi-model spread by default.
- Primary:
nano-banana-pro — best for product fidelity + clean Amazon look
- Alternate:
gpt-image (latest) — best for creative angles + dramatic lighting
- Fallback:
flux-kontext for product replacement / packaging variations
To explicitly compare across models, use main-image-multi-model instead.
Phase 1 — Source the Brief
Required inputs (in order of preference):
--research={path} — existing brief from asin-deep-research- Existing
/tmp/cro-research/{ASIN}-research-brief-*.md (most recent)
- If neither: trigger
asin-deep-research first, then resume
The brief provides:
- Top 3 purchase drivers (what to lead with visually)
- Top 3 objections (what to preempt)
- Customer demographic (who's in the image, if person)
- Competitor SERP cluster (what visual styles are saturated)
Required: confirm with user the product reference image URL (clean PNG, white BG) before generating. Higgsfield Soul/Nano Banana need this for fidelity.
Phase 2 — Concept Brief Generation
Translate the research into 8 distinct concept directions per 02-visual-content/main-image.md:
Standard concept slate (pick 5-8 from this menu based on research):
| # | Concept type | Pulls from research | Example |
|---|
| 1 | Hero product, dramatic angle | Top driver + scroll-stop rule | Wide-angle, soft shadow, premium feel |
| 2 | Product + premium packaging | Brand trust + table stakes | Box visible, product foreground |
| 3 | Scale reference (if size matters) | Size objection | Hand holding / next to known object |
| 4 | Dominant color break | Visual SERP cluster analysis | If SERP is white/neutral, use bold color |
| 5 | On-product label callout | Top driver legibility at thumbnail | "100mg" / "30 servings" visible at 150px |
| 6 | Set/bundle visualization | If multipack & not visible | Quantity grouped clearly |
| 7 | Alt angle (top-down or 3/4) | Differentiation | Most competitors use one angle, break |
| 8 | "Open" or "in-use" hint | Scroll-stop curiosity | Lid off, contents partially visible |
Each concept gets a structured prompt that includes:
- Product fidelity instruction (use reference image)
- Background rule: pure white #FFFFFF
- Composition: product fills 85%+
- Compliance: no text/logos/badges (Amazon main image rule)
- Style: photorealistic, e-commerce, soft natural lighting
- Aspect ratio: 1:1 (Amazon main image)
- Resolution: 2000×2000 minimum
Phase 3 — Generate via Higgsfield MCP
For each concept (8 by default):
- Call Higgsfield generate with primary model (
nano-banana-pro)
- Pass: prompt, reference image, aspect 1:1, resolution 2000×2000
Run in batches of 4 (Higgsfield can handle parallel; respect MCP rate limits).
If a generation fails the fidelity check (product visibly different), re-run with gpt-image as fallback.
Phase 4 — Score Against the 6-Dimension Rubric
Per MASTER-CRO-REFERENCE.md §3 (Main Image scoring):
| Dimension | What to check (1-10) |
|---|
| Fidelity | Does it look like the actual product? Color accuracy? Shape? |
| Background | Pure white, no artifacts, no reflections, clean cutout? |
| Scroll-Stop | Does it break from the SERP cluster? Read at 150px thumbnail? |
| Compliance | No text/logos/badges? Product fills 85%+? Min 1000px? |
| Creative | Beyond "catalog shot" — angle, lighting, depth of field? |
| Quality | Resolution sharpness, no AI artifacts, lighting consistency? |
Pass: average ≥7 with no dimension <5.
Auto-fail triggers: product too small, flat catalog shot, dull lighting, cluttered, visible cutout artifacts, product blends into white.
Phase 5 — Output
# Main Image Concepts — {Title}
**ASIN:** {ASIN} | **Date:** {date} | **Models:** {models} | **Reference:** {ref-image-url}
## Brief Summary
- Top driver: {from research}
- Top objection to preempt: {from research}
- SERP visual cluster: {from competitor sweep}
- Differentiation play: {strategy chosen}
## Concept Grid
| # | Concept | Image | Fid | BG | Stop | Compl | Creat | Qual | Avg | Pass |
|---|---------|-------|-----|-----|------|-------|-------|------|-----|------|
| 1 | Hero, dramatic angle |  | 9 | 10 | 8 | 10 | 8 | 9 | 9.0 | ✅ |
| 2 | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
## Top 3 Recommended (for next step)
1. **Concept #X — {name}** (Avg: 9.2) — Why: {1 line on which research signal it lands}
2. **Concept #Y — {name}** (Avg: 8.8) — Why: {...}
3. **Concept #Z — {name}** (Avg: 8.5) — Why: {...}
## Recommended Next Skill
- Validate with shoppers: `main-image-poll {ASIN} --concepts={1,2,3}`
- Or run the full pipeline: `main-image-pipeline {ASIN}` (research → generate → 3-second test → poll → winner)
Reference Files
~/.claude/skills/cro/main-image-best-practices.md — full 6-dim scoring + Amazon compliance~/.claude/skills/hero-image/prompt-engineering.md — proven Higgsfield prompt patterns~/.claude/skills/hero-image/image-scoring.md — scoring rubric details- Vault:
CRO-Knowledge-Base/02-visual-content/main-image.md
- Vault:
CRO-Knowledge-Base/MASTER-CRO-REFERENCE.md §3 (Main Image)
Quality Bar
Auto-Triggers
Runs (without prefix) when:
- User asks "generate main image concepts for {ASIN}"
- User completes
asin-deep-research and asks for visuals
main-image-pipeline calls it as a sub-step
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.
