Amazon Listing Competitor Analysis
browser-act/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.
Amazon listing builder and optimizer for sellers. An agent skill from nexscope-ai/Amazon-Skills.
$ npx skills add nexscope-ai/Amazon-Skills --skill amazon-listing-optimization -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install nexscope-ai/Amazon-Skills amazon-listing-optimization --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ 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-srcUse ~/.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/
Install the "amazon-listing-optimization" agent skill from https://github.com/nexscope-ai/Amazon-Skills/tree/main/amazon-listing-optimization into .claude/skills/amazon-listing-optimization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "amazon-listing-optimization", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/nexscope-ai/Amazon-Skills/tree/main/amazon-listing-optimizationType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add nexscope-ai/Amazon-Skills --skill amazon-listing-optimization -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install nexscope-ai/Amazon-Skills amazon-listing-optimization --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/nexscope-ai/Amazon-Skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/amazon-listing-optimization .agents/skills/amazon-listing-optimization && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "amazon-listing-optimization" agent skill from https://github.com/nexscope-ai/Amazon-Skills/tree/main/amazon-listing-optimization into .agents/skills/amazon-listing-optimization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "amazon-listing-optimization", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add nexscope-ai/Amazon-Skills --skill amazon-listing-optimization -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install nexscope-ai/Amazon-Skills amazon-listing-optimization --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/nexscope-ai/Amazon-Skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/amazon-listing-optimization .cursor/skills/amazon-listing-optimization && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "amazon-listing-optimization" agent skill from https://github.com/nexscope-ai/Amazon-Skills/tree/main/amazon-listing-optimization into .cursor/skills/amazon-listing-optimization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "amazon-listing-optimization", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/nexscope-ai/Amazon-Skills.git --path amazon-listing-optimization--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add nexscope-ai/Amazon-Skills --skill amazon-listing-optimization -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install nexscope-ai/Amazon-Skills amazon-listing-optimization --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/nexscope-ai/Amazon-Skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/amazon-listing-optimization .gemini/skills/amazon-listing-optimization && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "amazon-listing-optimization" agent skill from https://github.com/nexscope-ai/Amazon-Skills/tree/main/amazon-listing-optimization into .gemini/skills/amazon-listing-optimization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "amazon-listing-optimization", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install nexscope-ai/Amazon-Skills amazon-listing-optimizationInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add nexscope-ai/Amazon-Skills --skill amazon-listing-optimization -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/nexscope-ai/Amazon-Skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/amazon-listing-optimization .github/skills/amazon-listing-optimization && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "amazon-listing-optimization" agent skill from https://github.com/nexscope-ai/Amazon-Skills/tree/main/amazon-listing-optimization into .github/skills/amazon-listing-optimization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "amazon-listing-optimization", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add nexscope-ai/Amazon-Skills --skill amazon-listing-optimization -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install nexscope-ai/Amazon-Skills amazon-listing-optimization --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/nexscope-ai/Amazon-Skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/amazon-listing-optimization .opencode/skills/amazon-listing-optimization && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "amazon-listing-optimization" agent skill from https://github.com/nexscope-ai/Amazon-Skills/tree/main/amazon-listing-optimization into .opencode/skills/amazon-listing-optimization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "amazon-listing-optimization", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
amazon-listing-optimizationAmazon 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. 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.
4 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit 0f3b13f. It shows what the files ask for, not the result of running them.
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.
Ships 1 file in scripts/ (Shell), which the agent can run.
Shell commands in SKILL.md call:
npxFrom the folder's file list and the shell code blocks in SKILL.md.
Links to these hosts (documentation or services it may open):
nexscope.aiFrom URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
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.
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.
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.
The full file from nexscope-ai/Amazon-Skills at commit 0f3b13f, republished under its MIT licence (© nexscope-ai). 1,206 words, ~4,347 tokens.
.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.Build keyword-optimized listings from scratch, or audit and optimize existing ones. No API key — works out of the box.
npx skills add nexscope-ai/Amazon-Skills --skill amazon-listing-optimization -g| Mode | When to Use | Input | Output |
|---|---|---|---|
| A — Create | Building a new listing | Keywords and/or competitor ASINs + product info + tone | Full listing copy + keyword coverage score |
| B — Optimize | Improving an existing listing | Your ASIN or URL (+ optional keywords or competitor ASINs) | Optimized listing copy + audit report + gap analysis |
| Input Source | How it Works |
|---|---|
| Keywords | User provides keyword list → skill prioritizes and generates listing |
| Competitor ASINs | User provides 1-3 competitor ASINs → skill fetches their listings, extracts their keywords, then generates a listing that covers all their keywords and more |
| Both | User provides keywords + competitor ASINs → skill merges both sources for maximum coverage |
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.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.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.Audit the listing for ASIN B0D72TSM62 on Amazon USOptimize B0D72TSM62 using these keywords: dog shirt, pet clothes, puppy clothing — show me what's missing and rewriteOptimize 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.Keywords can come from four sources (use one or combine multiple):
npx skills add nexscope-ai/Amazon-Skills --skill amazon-keyword-research -g<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.web_search to find top keywords for the product categoryWhen 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.
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:
Ask or extract from user input:
| Tone | Style | Best for |
|---|---|---|
| Professional | Authoritative, spec-focused, trust-building | Electronics, tools, B2B |
| Friendly | Conversational, benefit-focused, relatable | Kitchen, lifestyle, gifts |
| Urgent | Scarcity-driven, action words, problem-solving | Health, safety, seasonal |
| Luxury | Premium, sensory language, exclusivity | Beauty, fashion, premium goods |
Default: Professional if not specified.
Generate each component following these rules:
Title (max 200 characters):
[Brand] + [Primary Keyword] + [Key Attribute 1] + [Key Attribute 2] + [Secondary Keyword] + [Differentiator]Bullet Points (5 bullets, max 500 chars each):
[BENEFIT HEADER IN CAPS] — [Benefit explanation with keyword naturally embedded]Description (max 2000 characters):
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 TermsScoring:
Run the bundled script:
<skill>/scripts/fetch-listing.sh "<ASIN>" [marketplace]Parameters:
ASIN (required): e.g. B09V3KXJPBmarketplace (optional): us (default), uk, de, fr, it, es, jp, ca, au, in, mx, brExtracts: 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.
If user provides keywords, use those. Otherwise, auto-discover:
web_search for site:amazon.com "[product type]" to find competitorsamazon-keyword-research skill for deeper analysisCompare 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%)Score each on the scale shown, with keyword integration factored in:
| Dimension | Max Score | Key Criteria |
|---|---|---|
| Title | /15 | Primary keyword near front? Brand? Attributes? Under 200 chars? Not truncated on mobile? |
| Bullet Points | /15 | All 5 used? Benefit-first? Keywords embedded naturally? Under 500 chars each? |
| Images | /15 | 7+ images? White bg main? Infographic? Lifestyle? Size ref? Video? |
| A+ Content | /10 | Present? Brand story? Comparison chart? Lifestyle imagery? |
| Description | /10 | Keywords not in title/bullets? Readable? Problem→solution flow? |
| Pricing | /10 | Competitive? Coupon/deal present? |
| Reviews | /15 | 4.0+ stars? 100+ reviews? Recent reviews positive? |
| SEO Coverage | /10 | Primary kw in title+bullets+desc? Long-tail present? No wasted repeats? Keyword coverage % |
Rewrite the listing incorporating missing keywords:
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.
# ✅ 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]# ✅ 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]| 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** |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.
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.
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 copyThis 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
SKILL.md and 2 other files (scripts) in amazon-listing-optimization of nexscope-ai/Amazon-Skills.
Open the folder on GitHubat commit 0f3b13f
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.
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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Amazon Listing Optimization this skillnexscope-ai/Amazon-Skills | 735 | 2 repos | ~4.3k | Automated safety check: Pass | MIT | |
| Amazon Listing Competitor Analysisbrowser-act/skills | 6.1k | 2 repos | ~3.2k | Automated safety check: Pass | MIT | |
| 05 Ad Copy Globalminhnv0807/ai-business-skills | 608 | — | ~4.1k | Automated safety check: Pass | MIT | |
| SEO Planindranilbanerjee/digital-marketing-pro | 854 | 1 repos | ~3.9k | Automated safety check: Pass | MIT | |
| Product Description Generatornexscope-ai/eCommerce-Skills | 1.1k | — | ~3.3k | Automated safety check: Pass | MIT | |
| Ecommerce Keyword Researchnexscope-ai/eCommerce-Skills | 1.1k | — | ~645 | Automated safety check: Pass | MIT |
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Categories
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.
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.
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.
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.
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.
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.
SKILL.md names 1 domain. As links in the text: nexscope.ai. This is read from the text; nothing was executed.
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.
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.
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.
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.
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.