CAT
/Skills
SkillsMCPMarketplacesDigestToolsAdvertise

This week in Claude

Every Monday: Claude Code, Agent SDK, MCP, and the Anthropic platform moves worth your time.

Skills by Category
Frontend DevelopmentBackend & APIsTesting & QASecurityDevOps & CI/CDGit & Pull RequestsDocumentationCode Review & QualityAI & Agent BuildingSkill Development
MCP Servers by Category
Sales & MarketingWeb & Browser AutomationDatabasesAI & LLM ToolsCloud & InfrastructureCommunication & MessagingDeveloper ToolsDesign & CreativeDocuments & KnowledgeSearch & Web Crawling
Marketplaces by Category
AI Agents & OrchestrationLLM IntegrationDevelopment ToolsFrontend & UIBackend & APIsDatabasesTesting & Code QualityDevOps & CloudSecurity & ComplianceGit & Version Control

Cross AI Tools

Discover Claude Code plugins, extensions, and tools. Automatically updated directory of Anthropic Claude AI marketplaces with development tools, productivity plugins, and integrations.

Resources

  • Browse Skills
  • Browse MCP Servers
  • Browse Marketplaces
  • Plugins Reference

Community

  • About
  • Tools
  • Feedback
  • Privacy Policy
  • Advertise

Built for the Claude Code community with Claude Code by @mertduzgun

Independent project, not affiliated with Anthropic

Amazon Listing Optimization

nexscope-ai/amazon-skills
353 installs210 stars
Summary

Two modes: create keyword-optimized Amazon listings from scratch, or audit and improve existing ones. The create flow lets you start from a keyword list, competitor ASINs (it extracts their keywords for you), or both. The optimize mode scores your listing across 8 dimensions, finds keyword gaps, and rewrites copy to fill them. Works across 12 Amazon marketplaces with no API key needed. If you're launching a product or reworking underperforming listings, this handles the tedious parts: keyword prioritization, title front-loading, bullet structure, coverage tracking. The competitor extraction is smart,paste 2-3 ASINs and it builds a listing that covers everything they do plus what they missed.

Install to Claude Code

npx -y skills add nexscope-ai/amazon-skills --skill amazon-listing-optimization --agent claude-code

Installs into .claude/skills of the current project.

CodeRabbit
CodeRabbit
AI writes the code. CodeRabbit catches the slop.
Try For Free →
Keep your Mac awake
Keep your Mac awake
Keep your Mac awake while Claude Code and 40+ AI agents run. Sleeps when they're idle.
One time payment $9 →
Context.devContext.dev
Context.dev
Integrate web data into your AI product. One API to scrape website & brand data.
Get API Key Now →
Make your agent a DeFi expert
Make your agent a DeFi expert
Agent, run crypto. Access onchain data & trade routes via 1inch.
Install now →
Make money from your Skills
Make money from your Skills
On Capafy, your Skill runs online 24/7 as an agent product, and you get paid every time someone uses it.
Start earning →
AppSignal
AppSignal
Monitor with ease. Code with confidence.
Start Free Trial →
CodeRabbit
CodeRabbit
AI writes the code. CodeRabbit catches the slop.
Try For Free →
Keep your Mac awake
Keep your Mac awake
Keep your Mac awake while Claude Code and 40+ AI agents run. Sleeps when they're idle.
One time payment $9 →
Context.devContext.dev
Context.dev
Integrate web data into your AI product. One API to scrape website & brand data.
Get API Key Now →
Make your agent a DeFi expert
Make your agent a DeFi expert
Agent, run crypto. Access onchain data & trade routes via 1inch.
Install now →
Make money from your Skills
Make money from your Skills
On Capafy, your Skill runs online 24/7 as an agent product, and you get paid every time someone uses it.
Start earning →
AppSignal
AppSignal
Monitor with ease. Code with confidence.
Start Free Trial →
Files
SKILL.mdView on GitHub

Amazon Listing Optimization 📝

Build keyword-optimized listings from scratch, or audit and optimize existing ones. No API key — works out of the box.

Installation

npx skills add nexscope-ai/Amazon-Skills --skill amazon-listing-optimization -g

Two Modes

ModeWhen to UseInputOutput
A — CreateBuilding a new listingKeywords and/or competitor ASINs + product info + toneFull listing copy + keyword coverage score
B — OptimizeImproving an existing listingYour ASIN or URL (+ optional keywords or competitor ASINs)Optimized listing copy + audit report + gap analysis

Mode A — Three Ways to Start

Input SourceHow it Works
KeywordsUser provides keyword list → skill prioritizes and generates listing
Competitor ASINsUser provides 1-3 competitor ASINs → skill fetches their listings, extracts their keywords, then generates a listing that covers all their keywords and more
BothUser provides keywords + competitor ASINs → skill merges both sources for maximum coverage

Capabilities

  • 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

Usage Examples

Mode A — Create from Keywords

Create a listing for a portable blender. Keywords: portable blender, smoothie maker, USB rechargeable, travel blender, personal blender. Material: BPA-free Tritan. Color: White. Capacity: 380ml. Tone: Friendly.
I have these keywords from my research: [paste keyword list]. Product: silicone kitchen utensil set, 12 pieces, heat resistant to 480°F. Generate a full listing.

Mode A — Create from Competitor ASINs

I want to sell a dog t-shirt on Amazon US. Here are 3 competitors I want to beat: B0D72TSM62, B0ABC12345, B0XYZ67890. My product is 100% cotton, 6 colors, XS-XL, funny print. Analyze their listings and create one that's better. Friendly tone.
Create a listing for my yoga mat. Look at this competitor: B09V3KXJPB. Extract their keywords, find what they're missing, and build a listing that covers more keywords than them. Product: 6mm TPE, non-slip, carrying strap included. Tone: Professional.

Mode A — Create from Keywords + Competitor ASINs

Use amazon-keyword-research to find keywords for "portable blender", also analyze these competitors: B0CPY1GFVZ, B0CXLF3Y19. Combine all keywords and create a listing. Product: 380ml, USB-C, BPA-free Tritan. Tone: Professional.

Mode B — Optimize Existing

Audit the listing for ASIN B0D72TSM62 on Amazon US
Optimize B0D72TSM62 using these keywords: dog shirt, pet clothes, puppy clothing — show me what's missing and rewrite
Optimize my listing B0D72TSM62 by analyzing these competitors: B0ABC12345, B0XYZ67890. Find what keywords they have that I don't, and rewrite my listing to beat them.

Mode A Workflow — Create Listing from Keywords

Step A1: Collect Keywords

Keywords can come from four sources (use one or combine multiple):

  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

When competitor ASINs are provided, always fetch and analyze them first. Extract every meaningful keyword from their titles and bullets, then merge with any user-provided keywords. The goal: cover everything competitors cover, plus keywords they missed.

Step A2: Prioritize Keywords

Organize keywords into tiers:

🔴 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]

Priority rules:

  • 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)

Step A3: Collect Product Characteristics

Ask or extract from user input:

  • 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

Step A4: Select Tone

ToneStyleBest for
ProfessionalAuthoritative, spec-focused, trust-buildingElectronics, tools, B2B
FriendlyConversational, benefit-focused, relatableKitchen, lifestyle, gifts
UrgentScarcity-driven, action words, problem-solvingHealth, safety, seasonal
LuxuryPremium, sensory language, exclusivityBeauty, fashion, premium goods

Default: Professional if not specified.

Step A5: Generate Listing Copy

Generate each component following these rules:

Title (max 200 characters):

  • 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

Bullet Points (5 bullets, max 500 chars each):

  • 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

Description (max 2000 characters):

  • 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

Step A6: Keyword Coverage Score

After generating, produce a coverage map:

## Keyword Coverage Report

| Keyword | Volume | In Title? | In Bullets? | In Description? | Status |
|---------|--------|-----------|-------------|-----------------|--------|
| portable blender | 45,000 | ✅ | ✅ | ✅ | 🟢 Covered |
| smoothie maker | 22,000 | ❌ | ✅ | ✅ | 🟡 Add to title |
| USB rechargeable | 18,000 | ✅ | ✅ | ❌ | 🟢 Covered |
| travel blender | 12,000 | ❌ | ❌ | ✅ | 🟡 Add to bullets |
| mini blender | 8,000 | ❌ | ❌ | ❌ | 🔴 Missing |

Coverage: 18/22 keywords (82%)
Title keywords: 6/8 slots used
Bullet keywords: 12/15 target keywords covered
Uncovered → recommend for Backend Search Terms

Scoring:

  • 🟢 90%+ coverage = Excellent
  • 🟡 70-89% = Good, minor gaps
  • 🔴 <70% = Needs work, significant keywords missing

Mode B Workflow — Optimize Existing Listing

Step B1: Fetch Listing Data

Run the bundled script:

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

Parameters:

  • ASIN (required): e.g. B09V3KXJPB
  • marketplace (optional): us (default), uk, de, fr, it, es, jp, ca, au, in, mx, br

Extracts: Title, brand, price, bullet points, description, image count, A+ content presence, rating, review count, BSR, categories, date first available.

If script returns incomplete data, fall back to web_fetch on the product URL.

Step B2: Discover Target Keywords

If user provides keywords, use those. Otherwise, auto-discover:

  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

Step B3: Keyword Gap Analysis

Compare current listing against target keywords:

## Keyword Gap Analysis: [ASIN]

### ✅ Keywords Found in Listing
| Keyword | In Title | In Bullets | In Description |
|---------|----------|------------|----------------|
| [kw] | ✅ | ✅ | ❌ |

### ❌ Missing Keywords (Competitors Have, You Don't)
| Keyword | Competitor 1 | Competitor 2 | Competitor 3 | Priority |
|---------|-------------|-------------|-------------|----------|
| [kw] | ✅ Title | ✅ Bullet | ❌ | 🔴 High |

### Coverage: X/Y keywords (Z%)

Step B4: 8-Dimension Audit

Score each on the scale shown, with keyword integration factored in:

DimensionMax ScoreKey Criteria
Title/15Primary keyword near front? Brand? Attributes? Under 200 chars? Not truncated on mobile?
Bullet Points/15All 5 used? Benefit-first? Keywords embedded naturally? Under 500 chars each?
Images/157+ images? White bg main? Infographic? Lifestyle? Size ref? Video?
A+ Content/10Present? Brand story? Comparison chart? Lifestyle imagery?
Description/10Keywords not in title/bullets? Readable? Problem→solution flow?
Pricing/10Competitive? Coupon/deal present?
Reviews/154.0+ stars? 100+ reviews? Recent reviews positive?
SEO Coverage/10Primary kw in title+bullets+desc? Long-tail present? No wasted repeats? Keyword coverage %

Step B5: Generate Optimized Copy

Rewrite the listing incorporating missing keywords:

  • 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

Output Formats

The primary deliverable is always a ready-to-use listing that the seller can copy-paste directly into Seller Central. Diagnostic data (scores, keyword analysis) comes after as supporting evidence.

Mode A Output — New Listing

# ✅ Your Listing — Ready to Use

## Title
[title text — copy this directly into Seller Central]

## Bullet Points
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]

## Description
[description text — copy this directly into Seller Central]

## Backend Search Terms
[comma-separated keywords to paste into Seller Central → Keywords → Search Terms]

---

# 📊 How We Built This Listing (Diagnostic)

**Marketplace:** Amazon [XX] | **Tone:** [tone] | **Keywords imported:** [count]
**Title characters:** [X]/200 | **Description characters:** [X]/2000

## Keyword Coverage: [X]%

| Keyword | Volume | In Title | In Bullets | In Description | Status |
|---------|--------|----------|------------|----------------|--------|
| [kw] | [vol] | ✅/❌ | ✅/❌ | ✅/❌ | 🟢🟡🔴 |

## Keyword Priority Breakdown
🔴 Primary (Title): [list]
🟡 Secondary (Bullets): [list]
🟢 Tertiary (Description): [list]
⚪ Backend: [list]

Mode B Output — Audit + Optimized Listing

# ✅ Optimized Listing — Ready to Use

## Title
[optimized title — copy this directly into Seller Central]

## Bullet Points
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]

## Description
[optimized description — copy this directly into Seller Central]

## Backend Search Terms
[comma-separated keywords to paste into Seller Central → Keywords → Search Terms]

---

# 📊 Audit Report: [ASIN]

**Product:** [title] | **Brand:** [brand]
**Price:** [price] | **Rating:** [stars] ([count] reviews)

## Score: [X/100] → [Y/100] (after optimization)

| Dimension | Before | After | Key Change |
|-----------|--------|-------|-----------|
| Title | /15 | /15 | [what changed] |
| Bullet Points | /15 | /15 | [what changed] |
| Images | /15 | — | [recommendation only] |
| A+ Content | /10 | — | [recommendation only] |
| Description | /10 | /10 | [what changed] |
| Pricing | /10 | — | [observation] |
| Reviews | /15 | — | [observation] |
| SEO Coverage | /10 | /10 | [what changed] |

## Keyword Coverage: [X]% → [Y]%

| Keyword | Before | After | Where Added |
|---------|--------|-------|-------------|
| [kw] | ❌ | ✅ | Title + Bullet 2 |
| [kw] | ✅ Title only | ✅ Title + Bullets | Bullet 4 |

## What Changed (Before → After)

**Title:**
> ❌ [original]
> ✅ [optimized]

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

## 🔴 Issues Fixed
1. [what was wrong → how we fixed it]

## 🟡 Recommendations (requires seller action)
1. [image improvements, A+ content, pricing — things the skill can't rewrite]

## 🟢 What Was Already Working
1. [positive aspects preserved]

Competitive Comparison (if requested)

| Dimension | Your Listing | Competitor 1 | Competitor 2 | Competitor 3 |
|-----------|-------------|-------------|-------------|-------------|
| Title score | /15 | /15 | /15 | /15 |
| Bullets score | /15 | /15 | /15 | /15 |
| Images | [count] | [count] | [count] | [count] |
| A+ Content | Yes/No | Yes/No | Yes/No | Yes/No |
| Keyword coverage | X% | X% | X% | X% |
| Price | — | — | — | — |
| Rating | — | — | — | — |
| **Total** | **/100** | **/100** | **/100** | **/100** |

Key principles

  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.

Integration with amazon-keyword-research

This skill works best when chained with amazon-keyword-research:

Step 1: "Research keywords for portable blender on Amazon US"
   → amazon-keyword-research returns keyword list with volumes

Step 2: "Now create a listing using those keywords. Product: 380ml BPA-free blender, USB-C rechargeable. Tone: Friendly."
   → amazon-listing-optimization Mode A uses the keywords to generate optimized copy

Limitations

This skill uses publicly available data from Amazon product pages. It cannot access backend search terms, exact search volumes, or PPC/conversion data. For deeper analytics, check out Nexscope — Your AI Assistant for smarter E-commerce decisions.


Built by Nexscope — research, validate, and act on e-commerce opportunities with AI.

Featured
CodeRabbit
CodeRabbit
AI writes the code. CodeRabbit catches the slop.
Try For Free →
Keep your Mac awake
Keep your Mac awake
Keep your Mac awake while Claude Code and 40+ AI agents run. Sleeps when they're idle.
One time payment $9 →
Context.devContext.dev
Context.dev
Integrate web data into your AI product. One API to scrape website & brand data.
Get API Key Now →
Make your agent a DeFi expert
Make your agent a DeFi expert
Agent, run crypto. Access onchain data & trade routes via 1inch.
Install now →
Make money from your Skills
Make money from your Skills
On Capafy, your Skill runs online 24/7 as an agent product, and you get paid every time someone uses it.
Start earning →
AppSignal
AppSignal
Monitor with ease. Code with confidence.
Start Free Trial →
First SeenJun 3, 2026
View on GitHub

Recommended

caveman

juliusbrussee/caveman

Ultra-compressed communication mode cutting token usage ~75% while preserving technical accuracy.
203.4k
67.8k
grill-me

mattpocock/skills

Relentless interviewing skill that stress-tests plans and designs through systematic questioning.
250.9k
114.5k
improve

shadcn/improve

Survey any codebase as a senior advisor and produce prioritized, self-contained implementation plans for other models/agents to execute.
10
205
systematic-debugging

obra/superpowers

Structured debugging methodology that mandates root cause investigation before attempting any fixes.
124.6k
215.9k
karpathy-guidelines

forrestchang/andrej-karpathy-skills

Behavioral guidelines to reduce common LLM coding mistakes through explicit assumptions, simplicity, and verifiable success criteria.
13.9k
165.4k
find-skills

vercel-labs/skills

Discover and install specialized agent skills from the open ecosystem when users need extended capabilities.
1.8M
21.1k