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Agent skill

Amazon Listing Competitor Analysis

by browser-act in 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.

MITAuto-check passedMarketing & SEO

Install Amazon Listing Competitor Analysis

skills CLI
$ npx skills add browser-act/skills --skill amazon-listing-competitor-analysis-skill -a claude-code

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

GitHub CLI
$ gh skill install browser-act/skills amazon-listing-competitor-analysis-skill --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/browser-act/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/solutions/ecommerce/amazon-listing-competitor-analysis-skill .claude/skills/amazon-listing-competitor-analysis-skill && 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-competitor-analysis-skill
GitHub stars
6.1k
Used in
2 other repos
Token cost
~3.2k tokens
SKILL.md length
1,241 words
Files
2 (incl. scripts)
Skills in repo
87
Repo updated
First seen
Licence
MIT

At a glance

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.

  • Works in 5 steps: No hallucinations, ensuring stable and… → No CAPTCHA issues: No need to handle… → No IP restrictions or geo-blocking: No… → …
  • Analyzing a top-ranked competitor's Amazon listing by ASIN
  • SKILL.md covers 📖 Brief, ✨ Features, 🔑 API Key Guide and 🛠️ Input Parameters, plus 5 more sections
  • Runs Python scripts from its folder; calls python; reaches amazon.com and amazon.de; needs BROWSERACT_API_KEY

What it does

This skill runs a two-phase workflow on a single competitor Amazon listing identified by its ASIN. Phase one uses the BrowserAct Amazon Listing Extractor for SEO template, driven by scripts/amazon_listing_competitor_analysis.py, to pull visible product data from the listing. Phase two diagnoses what the competitor does well and where the market shows gaps, and closes with strategic opportunity points for your own go-to-market. The output should not read as instructions to rewrite the competitor's listing: the analyzed ASIN is evidence only, and the narrative must be grounded in the extracted data.

Inputs are the ASIN, which is required, and a marketplace URL that defaults to amazon.com, with regional sites such as amazon.de supported. Before running, the agent checks for a BROWSERACT_API_KEY environment variable and, if it is missing, asks you to supply one before doing anything else. Extraction goes through a preset BrowserAct workflow instead of free-form AI browsing. Listed use cases include keyword placement on rival listings, title and bullet strategy, review mining for buyer psychology, and gap analysis before launching a new SKU.

When your agent uses it

  • Analyzing a top-ranked competitor's Amazon listing by ASIN
  • Finding market gaps and unmet buyer needs before launching a new SKU
  • Studying a competitor's keyword placement, bullets and titles
  • Turning competitor review insights into opportunity points for your brand

Example prompts

  • “Analyze the competitor listing for ASIN B0CS62LY6P on amazon.com and give me opportunity points.”
  • “Run a gap analysis on this listing from amazon.de before we launch our own version.”
  • “What keyword placement patterns do this rival's title and bullets use?”

Requirements

  • A BrowserAct API key in BROWSERACT_API_KEY
  • Python to run the analysis script

Workflow steps

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

  1. No hallucinations, ensuring stable and accurate data extraction: Pre-set workflows avoid AI generative hallucinations.
  2. No CAPTCHA issues: No need to handle reCAPTCHA or other verification challenges.
  3. No IP restrictions or geo-blocking: No need to deal with regional IP restrictions or geofencing.
  4. Faster execution: Tasks execute faster compared to purely AI-driven browser automation solutions.
  5. Extremely high cost-efficiency: Significantly reduces data acquisition costs compared to AI solutions that consume massive amounts of…

What it can do on your machine

Read from SKILL.md and the folder at commit 11c057b. 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/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python

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

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • amazon.com
    • amazon.de

    Also links to:

    • browseract.com

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

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • BROWSERACT_API_KEY

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

Context cost

Amazon Listing Competitor Analysis loads about 3.2k tokens when it runs. Until then it costs about 247 tokens; SKILL.md has 1,241 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~247
When it runs · the whole SKILL.md, loaded when a task matches
~3.2k

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 browser-act/skills at commit 11c057b, republished under its MIT licence (© browser-act). 1,241 words, ~3,228 tokens.

Download SKILL.mdSave it as .claude/skills/amazon-listing-competitor-analysis-skill/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
amazon-listing-competitor-analysis-skill
description
This skill helps users analyze Amazon competitor listings by ASIN and produce structured competitive intelligence plus strategic opportunity points for their own go-to-market. The Agent should proactively apply this skill when users want to analyze a competitor Amazon listing by ASIN, understand what a top-ranked product does right in content keywords or visuals, find market gaps and unmet buyer needs, turn competitor research into opportunity maps for their brand, identify keyword placement patterns on rival listings, extract SEO insights from Amazon product pages, reverse-engineer competitor bullet and title strategies, mine competitor reviews for buyer psychology, compare seller and A plus content patterns, run gap analysis before launching a new SKU, research why a listing wins conversion signals, synthesize whitespace you can own versus the diagnosed listing, or say just look at this ASIN with a competitive or optimization angle.

Amazon Listing Competitor Analysis

📖 Brief

This skill runs a two-phase workflow on a single competitor Amazon listing. Phase 1 uses the BrowserAct Amazon Listing Extractor for SEO template to pull visible product data from that listing. Phase 2 diagnoses what that competitor does well and where the market shows gaps, then closes with your strategic opportunity points (how you can win next to them). Do not end with instructions that read like editing or rewriting this competitor's listing; the analyzed ASIN is evidence only. Final narrative output should be grounded in extracted data, not generic claims.

✨ Features

  1. No hallucinations, ensuring stable and accurate data extraction: Pre-set workflows avoid AI generative hallucinations.
  2. No CAPTCHA issues: No need to handle reCAPTCHA or other verification challenges.
  3. No IP restrictions or geo-blocking: No need to deal with regional IP restrictions or geofencing.
  4. Faster execution: Tasks execute faster compared to purely AI-driven browser automation solutions.
  5. Extremely high cost-efficiency: Significantly reduces data acquisition costs compared to AI solutions that consume massive amounts of tokens.

🔑 API Key Guide

Before running, you must check the BROWSERACT_API_KEY environment variable. If it is not set, do not take other actions first; you should ask and wait for the user to provide it. Agent must inform the user:

"Since you haven't configured the BrowserAct API Key yet, please go to the BrowserAct Console to get your Key."

🛠️ Input Parameters

When calling the script, the Agent should flexibly configure the following parameters based on user needs:

  1. ASIN

    • Type: string
    • Description: The ASIN (Amazon Standard Identification Number) of the Amazon product to analyze.
    • Example: B0CS62LY6P
    • Required: Yes

  2. Marketplace_url

    • Type: string
    • Description: The base URL of the Amazon marketplace. Use the correct regional site for the listing.
    • Example: https://www.amazon.com/, https://www.amazon.de/
    • Default: https://www.amazon.com/

🚀 Invocation Method

Run Phase 1 extraction with the script below. After structured data is returned, the Agent performs Phase 2 analysis using the framework in Competitive Analysis Framework (Phase 2). The closing section must synthesize opportunity points for the user's business, not a checklist of edits applied to the competitor page under review.

bash
python -u ./scripts/amazon_listing_competitor_analysis.py "B0CS62LY6P" "https://www.amazon.com/"

When only the ASIN is needed, the marketplace argument may be omitted; the script defaults to https://www.amazon.com/.

⏳ Running Status Monitoring

Since this task involves automated browser operations, it may take a long time (several minutes). The script will continuously output status logs with timestamps while running (e.g., [14:30:05] Task Status: running). Agent guidelines:

  • While waiting for the script to return results, please keep an eye on the terminal output.
  • As long as the terminal continues to output new status logs, it means the task is running normally. Do not misjudge it as a deadlock or unresponsiveness.
  • If the status remains unchanged for a long time or the script stops outputting without returning a result, only then consider triggering the retry mechanism.

📊 Data Output

Upon successful execution, the script prints the API result string (or full task JSON if no string field is present). Typical fields include:

  • asin, title, product_url, brand, price, coupon_text, rating, review_count, best_sellers_rank, availability, prime_eligible
  • description, short_description, category, key_features, bullet_points
  • main_image_url, additional_image_urls, seller_name, ships_from, sold_by
  • specifications, product_details, attributes, and review-related blocks (reviewer, content, date, helpful votes, etc.)

Use this payload as the single source of truth for Phase 2; do not invent listing facts.

⚠️ Error Handling & Retry

During script execution, if errors occur (such as network fluctuations or task failure), the Agent should follow this logic:

  1. Check the output content:

    • If the output contains "Invalid authorization", it means the API Key is invalid or expired. At this point, do not retry, but guide the user to recheck and provide the correct API Key.
    • If the output contains "concurrent" or "too many running tasks" or similar concurrency limit messages, it means the concurrent task limit for the current subscription plan has been reached. Do not retry; guide the user to upgrade their plan. Agent must inform the user:

      "The current task cannot be executed because your BrowserAct account has reached the limit of concurrent tasks. Please go to the BrowserAct Plan Upgrade Page to upgrade your subscription plan and enjoy more concurrent task benefits."

    • If the output does not contain the above error keywords but the task fails (e.g., output starts with Error: or returns empty results), the Agent should automatically try to run the script once more.

  2. Retry limit:

    • Automatic retry is limited to once. If the second attempt still fails, stop retrying and report the specific error message to the user.
Show full SKILL.md (497 more words)Show less

🌟 Typical Use Cases

  1. Competitor listing teardown: Analyze one ASIN to see title formula, bullets, and differentiation language.
  2. Keyword placement audit: Map where primary and long-tail terms appear across title, bullets, and description or A+ content.
  3. Visual strategy review: Infer image narrative, infographic highlights, and video approach from extracted media data.
  4. Buyer-validated selling points: Use high-helpful positive reviews to confirm what buyers value versus what the listing emphasizes.
  5. Unmet needs mining: Use three-star and mixed reviews to find feature and expectation gaps.
  6. Pre-launch gap analysis: Compare a planned positioning against a top competitor's listing structure.
  7. Cross-marketplace research: Run the same ASIN on different regional Amazon URLs for localized copy signals.
  8. Opportunity backlog from a rival listing: Turn extracted facts and gaps into a prioritized map of positioning, search, creative, and offer opportunities for your side of the market.
  9. SEO and conversion benchmarking: Relate BSR, rating volume, and copy patterns without guessing unavailable metrics.
  10. Review-driven objection handling: Surface recurring complaints to address in copy or images.

🧠 Competitive Analysis Framework (Phase 2)

After extraction succeeds, work through each dimension below. Every insight must be grounded in the actual extracted data.

Layer 1 — What the Competitor Did Right

1. Content Strategy

  • Title formula: Information order, primary keyword placement, brand-first vs feature-first vs use-case-first.
  • Bullet priority: What Bullet 1 leads with; selling point order across bullets (signal of tested conversion order).
  • Differentiation language: How generic category features are phrased to sound distinct.
  • A+ content: Modules implied by extracted content (comparison table, brand story, lifestyle, spec callouts).

2. Keyword Placement Strategy

Map where terms appear (not only which terms exist):

  • Title (first 80 chars) → primary ranking bets
  • Bullets 1–2 → secondary high-weight terms
  • Bullets 3–5 → long-tail and use-case terms
  • Description / A+ → supplementary terms and synonyms

3. Visual Content Strategy

  • Image narrative arc: Sequence story (hero, lifestyle, pain point, specs, size comparison, social proof, guarantee).
  • Infographic data: Numbers or attributes highlighted and how they are presented.
  • Video (if present in data): Hook length, demo vs lifestyle, subtitles.
  • Overall style: Premium, approachable, technical, lifestyle-focused.

4. Buyer-Validated Selling Points

From four- to five-star reviews with high helpful votes:

  • What reviewers praise that the listing underplays
  • Unexpected benefits buyers mention
Layer 2 — What the Market Lacks

5. Unmet Buyer Needs

From three-star reviews and recurring themes in low stars (non-defect noise):

  • "I wish it had…", "Would be five stars if…", "Good but not great because…"

6. Keyword Gaps

  • Natural search terms buyers would use that the listing does not cover
  • High-traffic angles the data suggests but copy does not foreground

7. Visual Content Gaps

  • Weak or missing context in existing images
  • Absent image types (use-case, comparison, real-world scale)
Required Output Format (Phase 2)

Produce the analysis using this structure. Be specific and quote or paraphrase extracted fields and reviews where useful. The final block is your opportunity synthesis; avoid imperatives that sound like "change this competitor's bullet five" or any direct edit list for the ASIN being studied.

Competitor ASIN: [ASIN] | Brand: [brand] | BSR: [rank] | Rating: [x.x] ([N] reviews)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

✅ WHAT THIS COMPETITOR DOES RIGHT

Content Strategy:
  - Title formula: [describe the pattern and keyword placement]
  - Bullet priority: [what each bullet leads with and the logic behind the order]
  - Standout phrasing: [specific language worth noting or borrowing]
  - A+ modules: [which are used and what they emphasize]

Keyword Placement:
  - Primary (title, first 80 chars): [keywords]
  - High-weight (Bullets 1–2): [terms]
  - Long-tail (Bullets 3–5): [terms]
  - Supplementary (description/A+): [terms]

Visual Strategy:
  - Image sequence: [describe the narrative arc across images]
  - Infographic highlights: [what data/specs are called out]
  - Video: [approach if present, or "none"]

Buyer-Validated Selling Points:
  - "[specific insight from high-helpful reviews]"
  - "[another insight]"

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

🕳️ MARKET GAPS (OBSERVED ON THIS COMPETITOR LISTING)

Content gap: [selling points or use cases their copy under-serves, as seen in extracted text]
Keyword gap: [search intents or terms weakly covered on their page — note buyer language from reviews where possible]
Visual gap: [image or video proof types missing or weak on their gallery or A+]
Unmet buyer needs: [recurring themes from 3-star and mixed reviews, quoted or paraphrased]

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

🎯 YOUR STRATEGIC OPPORTUNITY POINTS (FOR YOUR BRAND OR ROADMAP — NOT EDITS TO THIS LISTING)

The ASIN above is the competitor under diagnosis. Below, translate gaps into **where you can win**; do not phrase outcomes as rewriting their bullets or their title.

Positioning and messaging whitespace:
  - [Claim, use case, or audience angle they under-own; why it is an opening for you]

Search and intent capture:
  - [Queries or intents implied by reviews or category that their listing weakly serves; how you could own a different slice of demand]

Trust, proof, and creative differentiation:
  - [Proof points, demos, or gallery angles they lack that you could credibly own]

Product, offer, or bundle opportunity:
  - [Unmet needs from reviews that map to a SKU, variant, bundle, warranty, or service on your side — stay factual to extracted complaints and wishes]

Competitive strengths to respect or neutralize:
  - [What this competitor does so well in copy, visuals, or social proof that you should assume as the bar before claiming superiority]

© browser-act, 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 1 other file (scripts) in solutions/ecommerce/amazon-listing-competitor-analysis-skill of browser-act/skills.

  • SKILL.md
  • scripts/amazon_listing_competitor_analysis.py

Open the folder on GitHubat commit 11c057b

Used in 2 other repositories

We found 3 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 2 other GitHub owners. This page covers the copy in browser-act/skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Amazon Listing Competitor Analysis 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.

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SEO Content Brief GeneratorAgriciDaniel/claude-seo18k2 repos~2.6kAutomated safety check: PassMIT
Amazon Listing Optimizationnexscope-ai/Amazon-Skills7362 repos~4.3kAutomated safety check: PassMIT
SEO DataforseoAgriciDaniel/codex-seo7912 repos~4.6kAutomated safety check: PassMIT

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Categories

Questions about Amazon Listing Competitor Analysis

What does Amazon Listing Competitor Analysis do?

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. This skill runs a two-phase workflow on a single competitor Amazon listing identified by its ASIN.py, to pull visible product data from the listing.

When should I use Amazon Listing Competitor Analysis?

Amazon Listing Competitor Analysis fits situations like: analyzing a top-ranked competitor's Amazon listing by ASIN; finding market gaps and unmet buyer needs before launching a new SKU; studying a competitor's keyword placement, bullets and titles; turning competitor review insights into opportunity points for your brand.

How do I install Amazon Listing Competitor Analysis in Claude Code?

Run `npx skills add browser-act/skills --skill amazon-listing-competitor-analysis-skill -a claude-code`. Or copy the skill folder (solutions/ecommerce/amazon-listing-competitor-analysis-skill in browser-act/skills) into .claude/skills/amazon-listing-competitor-analysis-skill in your project. Claude Code loads it when a task matches its description.

How do I install Amazon Listing Competitor Analysis in Codex?

Run `npx skills add browser-act/skills --skill amazon-listing-competitor-analysis-skill -a codex`. Or copy the skill folder (solutions/ecommerce/amazon-listing-competitor-analysis-skill in browser-act/skills) into .agents/skills/amazon-listing-competitor-analysis-skill in your project. Codex loads it when a task matches its description.

Can I use Amazon Listing Competitor Analysis 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 browser-act/skills --skill amazon-listing-competitor-analysis-skill -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-competitor-analysis-skill, .gemini/skills/amazon-listing-competitor-analysis-skill, .github/skills/amazon-listing-competitor-analysis-skill and .opencode/skills/amazon-listing-competitor-analysis-skill in your project.

What does Amazon Listing Competitor Analysis need to run?

Going by SKILL.md and its folder, Amazon Listing Competitor Analysis needs Python for the scripts in its folder, the command-line tools its instructions call (python) and credentials named BROWSERACT_API_KEY. Our summary lists: A BrowserAct API key in BROWSERACT_API_KEY; Python to run the analysis script.

Does Amazon Listing Competitor Analysis access the network?

SKILL.md names 3 domains. In commands or code: amazon.com and amazon.de; the agent is likely to contact these when it follows the instructions. As links in the text: browseract.com. This is read from the text; nothing was executed.

Is Amazon Listing Competitor Analysis 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 Competitor Analysis use?

Amazon Listing Competitor Analysis 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 Competitor Analysis use?

About 3.2k tokens (SKILL.md is roughly 13k 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 Competitor Analysis?

Skills that share tags, products or a category with Amazon Listing Competitor Analysis: Sif Amazon Research (liangdabiao/amazon-sorftime-research-MCP-skill, 946 stars), Ecommerce Keyword Research (nexscope-ai/eCommerce-Skills, 1.1k stars), SEO Content Brief Generator (AgriciDaniel/claude-seo, 18k stars) and Amazon Listing Optimization (nexscope-ai/Amazon-Skills, 736 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Amazon Listing Competitor Analysis?

browser-act (a GitHub organization) maintains it in browser-act/skills, which has 6,114 GitHub stars. The repository holds 87 skills in this directory. The repository was last updated on August 24, 2026.

Source: browser-act/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.