Agent skill

Amazon Listing Optimization

by nexscope-ai in nexscope-ai/Amazon-Skills

Amazon listing builder and optimizer for sellers. An agent skill from nexscope-ai/Amazon-Skills.

MITAuto-check passedMarketing & SEO

Install Amazon Listing Optimization

skills CLI
$ npx skills add nexscope-ai/Amazon-Skills --skill amazon-listing-optimization -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install nexscope-ai/Amazon-Skills amazon-listing-optimization --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/nexscope-ai/Amazon-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/amazon-listing-optimization .claude/skills/amazon-listing-optimization && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
amazon-listing-optimization
GitHub stars
735
Used in
2 other repos
Token cost
~4.3k tokens
SKILL.md length
1,206 words
Files
3 (incl. scripts)
Skills in repo
50
Repo updated
First seen
Licence
MIT

At a glance

Amazon listing builder and optimizer for sellers. An agent skill from nexscope-ai/Amazon-Skills.

  • Works in 4 steps: From amazon-keyword-research skill… → From competitor ASINs: User provides 1-3… → From user's keyword list: User pastes… → …
  • Creating a new Amazon listing from keywords
  • SKILL.md covers Installation, Two Modes, Mode A — Three Ways to Start and Capabilities, plus 6 more sections
  • Runs Shell scripts from its folder; calls npx

What it does

Amazon Listing Optimization is an agent skill from nexscope-ai/Amazon-Skills. Amazon listing builder and optimizer for sellers. Two modes: (A) Create — build keyword-optimized listings from scratch using keyword lists + product characteristics + AI copywriting, (B) Optimize — audit existing listings, find keyword gaps, score across 8 dimensions, and rewrite with missing keywords. Integrates with amazon-keyword-research for keyword input. Works on 12 Amazon marketplaces. No API key required. Use when: (1) creating a new Amazon listing from keywords, (2) auditing an existing listing for SEO…

Its SKILL.md is about 4.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files, including scripts (for example `_meta.json` and `scripts/fetch-listing.sh`).

It sits in Marketing & SEO, covering Copywriting, Keyword research and E-commerce operations. The repository describes itself as: Free AI agent skills for Amazon sellers— keyword research, competitor analysis, listing audit & more. Works with OpenClaw, Claude Code, Cursor, Windsurf, Codex and any agent that… The licence is MIT.

When your agent uses it

  • Creating a new Amazon listing from keywords
  • Auditing an existing listing for SEO and conversion
  • Checking keyword coverage in title/bullets/description
  • Generating listing copy with target keywords and tone

Example prompts

  • “/amazon-listing-optimization”

Requirements

  • Node.js
  • A Bash shell

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  1. From amazon-keyword-research skill (recommended): Run keyword research first, then feed results directly. Install: npx skills add…
  2. From competitor ASINs: User provides 1-3 competitor ASINs → run /scripts/fetch-listing.sh on each → extract keywords from their titles…
  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

What it can do on your machine

Read from SKILL.md and the folder at commit 0f3b13f. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Shell), which the agent can run.

    Shell commands in SKILL.md call:

    • npx

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • nexscope.ai

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Amazon Listing Optimization loads about 4.3k tokens when it runs. Until then it costs about 193 tokens; SKILL.md has 1,206 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~193
When it runs · the whole SKILL.md, loaded when a task matches
~4.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from nexscope-ai/Amazon-Skills at commit 0f3b13f, republished under its MIT licence (© nexscope-ai). 1,206 words, ~4,347 tokens.

Download SKILL.mdSave it as .claude/skills/amazon-listing-optimization/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
amazon-listing-optimization
description
Amazon listing builder and optimizer for sellers. Two modes: (A) Create — build keyword-optimized listings from scratch using keyword lists + product characteristics + AI copywriting, (B) Optimize — audit existing listings, find keyword gaps, score across 8 dimensions, and rewrite with missing keywords. Integrates with amazon-keyword-research for keyword input. Works on 12 Amazon marketplaces. No API key required. Use when: (1) creating a new Amazon listing from keywords, (2) auditing an existing listing for SEO and conversion, (3) checking keyword coverage in title/bullets/description, (4) generating listing copy with target keywords and tone, (5) comparing listings against competitors, (6) preparing a listing for launch or relaunch.

Amazon Listing Optimization 📝

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

Installation

bash
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

Show full SKILL.md (481 more words)Show less
Step B1: Fetch Listing Data

Run the bundled script:

bash
<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.

© nexscope-ai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files (scripts) in amazon-listing-optimization of nexscope-ai/Amazon-Skills.

  • SKILL.md
  • _meta.json
  • scripts/fetch-listing.sh

Open the folder on GitHubat commit 0f3b13f

Used in 2 other repositories

We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 2 other GitHub owners. This page covers the copy in nexscope-ai/Amazon-Skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Amazon Listing Optimization next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Amazon Listing Optimization compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Amazon Listing Optimization this skillnexscope-ai/Amazon-Skills7352 repos~4.3kAutomated safety check: PassMIT
Amazon Listing Competitor Analysisbrowser-act/skills6.1k2 repos~3.2kAutomated safety check: PassMIT
05 Ad Copy Globalminhnv0807/ai-business-skills608—~4.1kAutomated safety check: PassMIT
SEO Planindranilbanerjee/digital-marketing-pro8541 repos~3.9kAutomated safety check: PassMIT
Product Description Generatornexscope-ai/eCommerce-Skills1.1k—~3.3kAutomated safety check: PassMIT
Ecommerce Keyword Researchnexscope-ai/eCommerce-Skills1.1k—~645Automated safety check: PassMIT

Similar skills

  • Analyzes a competitor's Amazon listing by ASIN with BrowserAct data extraction, then reports what it does well, where the market has gaps and opportunity points for your own listing.

    6.1k GitHub starsUsed in 2 repos~3.2k tokens
    Marketing & SEOAuto-check passed
  • 05 Ad Copy Global

    minhnv0807/ai-business-skills

    A skill your agent uses when the user needs PAID ad copy for Meta, Google, or TikTok — six variants across TOFU, MOFU, and BOFU using AIDA, PAS, and BAB, respecting each platform character limit and…

    608 GitHub stars~4.1k tokensUpdated 25 days ago
    Writing & ContentAuto-check passed
  • SEO Plan

    indranilbanerjee/digital-marketing-pro

    Build a 12-month SEO strategy and phased roadmap with industry templates (SaaS, ecommerce, local, publisher, agency).

    854 GitHub starsUsed in 1 repo~3.9k tokens
    Marketing & SEOAuto-check passed
  • Product Description Generator

    nexscope-ai/eCommerce-Skills

    E-commerce product description generator for any platform. An agent skill from nexscope-ai/eCommerce-Skills.

    1.1k GitHub stars~3.3k tokensUpdated 1 mo ago
    Writing & ContentAuto-check passed
  • Ecommerce Keyword Research

    nexscope-ai/eCommerce-Skills

    Cross-platform keyword research for e-commerce. An agent skill from nexscope-ai/eCommerce-Skills.

    1.1k GitHub stars~645 tokensUpdated 1 mo ago
    Marketing & SEOAuto-check passed
  • E-commerce SEO Analysis

    AgriciDaniel/claude-seo

    Analyzes a product page or store for SEO, from title tags and schema to marketplace visibility, with optional live Google Shopping and Amazon data for pricing and keyword gaps.

    18k GitHub stars~3.7k tokensUpdated 2 days ago
    Marketing & SEOAuto-check passed

More from nexscope-ai/Amazon-Skills

All 50 skills in this repo
  • Amazon Keyword Research

    nexscope-ai/Amazon-Skills

    Amazon keyword research and market opportunity analysis for sellers.

    735 GitHub stars~2k tokensUpdated 1 mo ago
    Auto-check passed
  • Amazon Brand Analytics

    nexscope-ai/Amazon-Skills

    Amazon Brand Analytics interpretation and strategic insights for Brand Registry owners.

    735 GitHub starsUsed in 1 repo~2.3k tokens
    Auto-check passed
  • Amazon Competitor Monitoring

    nexscope-ai/Amazon-Skills

    Amazon competitor monitoring and competitive intelligence for sellers.

    735 GitHub starsUsed in 1 repo~1.6k tokens
    Auto-check passed
  • Amazon Deal Finder

    nexscope-ai/Amazon-Skills

    Amazon deal planning and promotional strategy optimization. An agent skill from nexscope-ai/Amazon-Skills.

    735 GitHub starsUsed in 1 repo~4.4k tokens
    Auto-check passed
  • Amazon Product Research

    nexscope-ai/Amazon-Skills

    Comprehensive product research and opportunity analysis for Amazon sellers.

    735 GitHub starsUsed in 1 repo~3.2k tokens
    Auto-check passed
  • Amazon Backend Keywords

    nexscope-ai/Amazon-Skills

    Amazon backend search term optimization and strategy. An agent skill from nexscope-ai/Amazon-Skills.

    735 GitHub stars~4.9k tokensUpdated 1 mo ago
    Auto-check passed

Questions about Amazon Listing Optimization

What does Amazon Listing Optimization do?

Amazon listing builder and optimizer for sellers. An agent skill from nexscope-ai/Amazon-Skills. Amazon Listing Optimization is an agent skill from nexscope-ai/Amazon-Skills. Amazon listing builder and optimizer for sellers.

When should I use Amazon Listing Optimization?

Amazon Listing Optimization fits situations like: creating a new Amazon listing from keywords; auditing an existing listing for SEO and conversion; checking keyword coverage in title/bullets/description; generating listing copy with target keywords and tone.

How do I install Amazon Listing Optimization in Claude Code?

Run `npx skills add nexscope-ai/Amazon-Skills --skill amazon-listing-optimization -a claude-code`. Or copy the skill folder (amazon-listing-optimization in nexscope-ai/Amazon-Skills) into .claude/skills/amazon-listing-optimization in your project. Claude Code loads it when a task matches its description.

How do I install Amazon Listing Optimization in Codex?

Run `npx skills add nexscope-ai/Amazon-Skills --skill amazon-listing-optimization -a codex`. Or copy the skill folder (amazon-listing-optimization in nexscope-ai/Amazon-Skills) into .agents/skills/amazon-listing-optimization in your project. Codex loads it when a task matches its description.

Can I use Amazon Listing Optimization in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add nexscope-ai/Amazon-Skills --skill amazon-listing-optimization -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/amazon-listing-optimization, .gemini/skills/amazon-listing-optimization, .github/skills/amazon-listing-optimization and .opencode/skills/amazon-listing-optimization in your project.

What does Amazon Listing Optimization need to run?

Going by SKILL.md and its folder, Amazon Listing Optimization needs a shell for the scripts in its folder and the command-line tools its instructions call (npx). Our summary lists: Node.js; A Bash shell.

Does Amazon Listing Optimization access the network?

SKILL.md names 1 domain. As links in the text: nexscope.ai. This is read from the text; nothing was executed.

Is Amazon Listing Optimization safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Amazon Listing Optimization use?

Amazon Listing Optimization is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Amazon Listing Optimization use?

About 4.3k tokens (SKILL.md is roughly 17k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Amazon Listing Optimization?

Skills that share tags, products or a category with Amazon Listing Optimization: Amazon Listing Competitor Analysis (browser-act/skills, 6.1k stars), 05 Ad Copy Global (minhnv0807/ai-business-skills, 608 stars), SEO Plan (indranilbanerjee/digital-marketing-pro, 854 stars) and Product Description Generator (nexscope-ai/eCommerce-Skills, 1.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Amazon Listing Optimization?

nexscope-ai (a GitHub organization) maintains it in nexscope-ai/Amazon-Skills, which has 735 GitHub stars. The repository holds 50 skills in this directory. The repository was last updated on August 26, 2026.

Source: nexscope-ai/Amazon-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.