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Agent Skill · 研究检索

amazon-listing-optimization(亚马逊 listing 优化)

amazon-listing-optimization 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

amazon-listing-optimization 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为接触敏感数据;许可证信息还不明确,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 27,707。

总安装 3,345周安装 134GitHub Stars 140下载 1,083核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:amazon-listing-optimization(亚马逊 listing 优化)
来源仓库:https://github.com/nexscope-ai/amazon-skills
仓库路径:skills/amazon-listing-optimization
安装命令:
npx skills add https://github.com/nexscope-ai/amazon-skills --skill amazon-listing-optimization
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/nexscope-ai/amazon-skills --skill amazon-listing-optimization

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

用于查找、检索和筛选与亚马逊 listing 优化相关的信息,支持关键词定位和场景匹配。适合在 Codex、Claude、Cursor、Gemini CLI 中快速获取候选结果,辅助商品页面优化。通过 GitHub 安装并使用 npx 命令添加技能,具体用法需结合原始 README 进一步验证。

使用前应确认权限范围和维护状态,避免触发联网、命令执行或文件读写等敏感操作。建议核对来源仓库和实际功能边界,确保符合当前环境和数据安全要求。

来源仓库内路径:skills/amazon-listing-optimization

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/nexscope-ai/amazon-skills --skill amazon-listi…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

amazon-listing-optimization 怎么安装?

在终端执行 npx skills add https://github.com/nexscope-ai/amazon-skills --skill amazon-listing-optimiz…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

amazon-listing-optimization 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

amazon-listing-optimization 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

amazon-listing-optimization 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

amazon-listing-optimization 用的人多吗?

公开数据是累计安装 3,345 次,近一周安装 134 次,下载 1,083 次,GitHub 140 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

amazon-listing-optimization 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
amazon-listing-optimization

亚马逊 listing 优化 · 本页

3,345140接触敏感数据未明确有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

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.

平台分布

Codex

35.57%
按下载量换算385

Claude

31.43%
按下载量换算340

Cursor

16.92%
按下载量换算183

Gemini CLI

8.78%
按下载量换算95

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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