| name | aplus-module-generator |
| description | Generate the 5-7 vertical A+ Premium modules for an Amazon listing — Higgsfield image generation + structured layout following the two-half architecture (top half capture & convert, bottom half visual FAQ). Use when running `aplus-module-generator {ASIN}` or when planning A+ Premium content that follows Keplo's methodology. |
aplus-module-generator — A+ Premium 5-7 Module Set
Per 02-visual-content/a-plus-content.md, A+ Premium uses two-half architecture: top 3 modules = capture & convert with photography-forward emotional content, bottom 4 modules = inform & reassure (visual FAQ).
Methodology — read before rendering
Read reference/02-visual-content/aplus-creative-director.md in full before generating any module. (Updated in v0.1.7 — A+ has its own dedicated creative-director ref now, separate from listing-image.) That file is the source of truth for: the 13-step A+ flow, the 4 A+ Critical Rules (Legibility / Single Message / Visual Benefit / Branding consistency), the 5 Pre-Generation Checklist questions, editorial design philosophy, anti-clipart rules, the 6-section mandatory prompt structure (A+ variant), 4-axis scoring rubric, comparison-module exception, A+ module taxonomy, and iteration loop.
Hard rules specific to A+ modules:
- Aspect ratio: 16:9 (widescreen, horizontal layout) — locked in at start AND end of every prompt. NOT 1:1. Wrong-ratio outputs trigger automatic re-generation.
- Reference image always: pull live product photo from
brain/products/{asin}-{geo}.json and pass to Higgsfield
- Brand guidelines respected:
brain.business.brand_guidelines drives palette + visual style keywords + type placement
- No text generation in image: render clean photography; brand designer adds type in post
- No clipart elements — no callout arrows, no dimension lines, no overlay boxes, no flow charts, no annotated diagrams, no burst stickers. A+ modules are editorial photography with type, not infographic clipart.
A+ comparison modules requiring side-by-side spec tables follow the editorial-diagram exception in the listing creative-director reference — clean type, generous white space, photo of product as anchor.
Output emits florence-concepts-{asin} per the artifact protocol.
Prerequisites
- Higgsfield MCP
- Research brief (
asin-deep-research) and Rufus gap analysis (rufus-gap-analysis)
Invocation
aplus-module-generator {ASIN}
aplus-module-generator {ASIN} --modules=7 # 5-7, default 6
aplus-module-generator {ASIN} --premium # use full-width hero + video
Output
/tmp/cro-content/{ASIN}-aplus-{date}.md with 6 modules (1 hero, 2 capture, 3+ FAQ) + image set in /tmp/cro-content/{ASIN}-aplus/.
Phase 1 — Source Research
Pull from existing files:
- Top 3 purchase drivers → modules 1-3 (capture)
- Top 3-4 Rufus FAQ-class queries → modules 4-7 (FAQ)
- Photo review demographics → module 1 hero
Phase 2 — Module Plan
| Module | Type | Source | Visual approach |
|---|
| 1 | Hero — emotional moment | Top driver + demographic | Full-width photography, minimal copy |
| 2 | Benefit deep-dive | Driver #2 | Photography + 1-2 short callouts |
| 3 | Differentiation / brand story | Competitive gap | Photography + short story copy (40-60 words) |
| 4 | Visual FAQ #1 | Top Rufus FAQ | Photo answers + alt-text packed |
| 5 | Visual FAQ #2 | Rufus FAQ #2 | Same |
| 6 | Comparison / why us | Reviews comparison signal | Side-by-side or callout grid |
| 7 (optional) | Final reassurance / guarantee / CTA | Repurchase signal | Trust-mark photography |
Phase 3 — Generate Each Module
For each module:
- Image: Higgsfield call with module-specific prompt. Default Nano Banana Pro. Aspect varies (full-width 970×600 or two-up 487×487).
- Copy: Pull from research brief verbatim where possible. Keep modules scannable in 15-20s total.
- Alt-text: Pack with product name + keywords + benefit + use case + audience (Amazon search + Rufus index this).
Phase 4 — Validate (per 02-visual-content/a-plus-content.md)
- ✅ Top half is photography-forward, not specs
- ✅ Bottom half is visual FAQ (not text-only)
- ✅ No duplication of listing slot 2-7 images
- ✅ Full-width preferred over side-by-side on mobile
- ✅ Total scan time ≤20 seconds
- ✅ Alt-text on every image
Phase 5 — Output
# A+ Premium Module Set — {Title}
**ASIN:** {ASIN} | **Date:** {date} | **Modules:** {N}
## Two-Half Architecture
### Top Half — Capture & Convert
1. **Hero — emotional moment** 
- Copy: {short headline + 1 sentence}
- Alt-text: {keyword-packed string}
2. **Benefit deep-dive** 
- ...
### Bottom Half — Inform & Reassure
4. **Visual FAQ — {Rufus query}** 
- ...
## Designer Brief
- Image specs: 970×600 full-width (modules 1, 6); 487×487 two-up (others)
- Format: PNG, sRGB, ≤500KB each
- Use Higgsfield-generated as composition reference, polish for retail
- Do NOT duplicate any listing image (slots 1-7) — A+ should add, not repeat
## Recommended Next Skills
- Validate scannability: `aplus-comprehension {ASIN}`
- Build the FAQ alt-text pack: `rufus-answer-pack {ASIN}`
- Convert to A+ Premium spec doc: `anthropic-skills:amazon-aplus-brief`
Reference Files
- Vault:
CRO-Knowledge-Base/02-visual-content/a-plus-content.md (two-half architecture)
~/.claude/skills/cro/aplus-best-practices.md- Anthropic skill:
amazon-aplus-brief (companion for client-ready Word doc)
Quality Bar
Auto-Triggers
- User asks "build A+ for {ASIN}"
/cro-content-plan Phase 3 for A+- After
rufus-gap-analysis to act on the gaps
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.
