• Keyword-driven listing generation: Import keywords (from amazon-keyword-research, manual list, or extracted from competitor ASINs), rank by priority, generate copy that maximizes keyword coverage
• Competitor keyword extraction: Fetch competitor listings and automatically extract their title/bullet keywords as your baseline
• 8-dimension audit & scoring: Title, bullets, description, images, A+ content, pricing, reviews, SEO coverage
• Keyword coverage tracking: Visual map showing which keywords appear in title / bullets / description / missing
• Tone selection: Professional, Friendly, Urgent, Luxury — affects AI copywriting style
• Competitive benchmarking: Compare your listing against competitors
• Multi-marketplace: US, UK, DE, FR, IT, ES, JP, CA, AU, IN, MX, BR

1. From amazon-keyword-research skill (recommended): Run keyword research first, then feed results directly. Install:

   npx skills add nexscope-ai/Amazon-Skills --skill amazon-keyword-research -g

2. From competitor ASINs: User provides 1-3 competitor ASINs → run

   <skill>/scripts/fetch-listing.sh

   on each → extract keywords from their titles, bullets, and descriptions → use as your keyword baseline. This is the fastest way to start — you inherit what's already working for competitors, then add more.
3. From user's keyword list: User pastes their own keyword list (e.g. from Helium 10 Cerebro, Jungle Scout, or manual research)
4. Auto-discover: Use

   web_search

   to find top keywords for the product category

🔴 Primary (must appear in Title):
  - [keyword] — [search volume if known]
  - [keyword] — [search volume if known]

🟡 Secondary (must appear in Bullets):
  - [keyword]
  - [keyword]

🟢 Tertiary (should appear in Description or Backend):
  - [keyword]
  - [keyword]

⚪ Long-tail (use where natural):
  - [keyword phrase]
  - [keyword phrase]

• Highest search volume → Title (front-loaded)
• Medium volume + high relevance → Bullets (one primary keyword per bullet)
• Lower volume / long-tail → Description
• Remaining → Backend search terms (advise seller to add in Seller Central)

• Product name / type
• Brand name
• Key attributes: Material, color, size, weight, capacity, quantity
• Key features: What makes it different (3-5 features)
• Target audience: Who buys this?
• Use cases: Top 3 scenarios
• What's in the box: Everything included

• Format:

  [Brand] + [Primary Keyword] + [Key Attribute 1] + [Key Attribute 2] + [Secondary Keyword] + [Differentiator]

• Primary keyword as close to the front as possible (after brand)
• No ALL CAPS except brand name
• No promotional claims ("best", "#1", "top rated")
• Include size/color/quantity if relevant to search

• Each bullet:

  [BENEFIT HEADER IN CAPS] — [Benefit explanation with keyword naturally embedded]

• Bullet 1: Primary feature + primary keyword
• Bullet 2: Key use case + secondary keyword
• Bullet 3: Quality/material + trust signal
• Bullet 4: What's included / compatibility
• Bullet 5: Guarantee / differentiator / social proof hint
• Each bullet should contain at least 1 target keyword

• Opening: Problem/pain point the product solves
• Middle: Features → benefits (expand on bullets, don't repeat verbatim)
• Close: Call to action + what's in the box
• Embed remaining keywords not used in title/bullets
• Use line breaks for readability

<skill>/scripts/fetch-listing.sh "<ASIN>" [marketplace]

• ASIN

  (required): e.g. B09V3KXJPB

• marketplace

  (optional):

  us

  (default),

  uk

  ,

  de

  ,

  fr

  ,

  it

  ,

  es

  ,

  jp

  ,

  ca

  ,

  au

  ,

  in

  ,

  mx

  ,

  br

web_fetch

1. Extract apparent keywords from current title and bullets
2. Run

   web_search

   for

   site:amazon.com "[product type]"

   to find competitors
3. Extract keywords from top 3 competitor titles and bullets
4. (Optional) Chain with

   amazon-keyword-research

   skill for deeper analysis
5. Compile a combined keyword list with estimated priority

• Show before vs after for each component
• Highlight which keywords were added and where
• Maintain the brand's existing tone unless a different tone is requested

1. [BENEFIT HEADER] — [text with keyword]
2. [BENEFIT HEADER] — [text with keyword]
3. [BENEFIT HEADER] — [text with keyword]
4. [BENEFIT HEADER] — [text with keyword]
5. [BENEFIT HEADER] — [text with keyword]

1. [BENEFIT HEADER] — [optimized text]
2. [BENEFIT HEADER] — [optimized text]
3. [BENEFIT HEADER] — [optimized text]
4. [BENEFIT HEADER] — [optimized text]
5. [BENEFIT HEADER] — [optimized text]

❌ [original] ✅ [optimized]

❌ 1. [original] ✅ 1. [optimized — added: +[kw1], +[kw2]]

1. [what was wrong → how we fixed it]

1. [image improvements, A+ content, pricing — things the skill can't rewrite]

1. [positive aspects preserved]

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1. The seller's workflow is: copy the listing → paste into Seller Central → done. The diagnostic section explains WHY those specific words were chosen, but the listing itself must stand alone as a complete, ready-to-use deliverable. Never output only a report without the actual listing copy.
2. Output language must match the target marketplace. Amazon US/UK/AU/CA/IN → English. Amazon DE → German. Amazon FR → French. Amazon JP → Japanese. Amazon ES/MX → Spanish. Amazon IT → Italian. Amazon BR → Portuguese. The entire output (listing copy AND diagnostic section) must be in the marketplace language, regardless of what language the user is speaking in the conversation.