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Agent skill

Amazon Product Search Extractor

by browser-act in browser-act/skills

Collects structured product data from Amazon search results for a keyword and optional brand, using a BrowserAct script, for market and competitor research.

MITAuto-check passedData & Analytics

Install Amazon Product Search Extractor

skills CLI
$ npx skills add browser-act/skills --skill amazon-product-search-api-skill -a claude-code

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

GitHub CLI
$ gh skill install browser-act/skills amazon-product-search-api-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-product-search-api-skill .claude/skills/amazon-product-search-api-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-product-search-api-skill
GitHub stars
6.1k
Used in
2 other repos
Token cost
~1.6k tokens
SKILL.md length
698 words
Files
2 (incl. scripts)
Skills in repo
87
Repo updated
First seen
Licence
MIT

At a glance

Collects structured product data from Amazon search results for a keyword and optional brand, using a BrowserAct script, for market and competitor research.

  • Works in 5 steps: No Hallucinations: Pre-set workflows… → No Captcha Issues: No need to handle… → No IP Restrictions: No need to handle… → …
  • Pulling Amazon listings for a keyword to size up a product category
  • SKILL.md covers 📖 Introduction, ✨ Features, 🔑 API Key Setup and 🛠️ Input Parameters, plus 4 more sections
  • Runs Python scripts from its folder; calls python; needs BROWSERACT_API_KEY

What it does

The skill runs `scripts/amazon_product_search_api.py`, which calls BrowserAct's Amazon Product Search template and returns product records from Amazon search listings. You give it a keyword, an optional brand filter, a cap on how many products to collect (50 by default) and a language for the Amazon session (English by default).

A `BROWSERACT_API_KEY` environment variable must be set. If it is missing, the agent stops and asks you for the key rather than trying another route. Fields mentioned include titles, URLs, ratings, review counts, Best Seller tags, shipping details and monthly sales estimates. Language options are en, de, fr, it, es, ja, zh-CN and zh-TW. The author contrasts the preset workflow with free-form AI browsing; the excerpt is cut off before the output format and run monitoring.

When your agent uses it

  • Pulling Amazon listings for a keyword to size up a product category
  • Comparing one brand's products, ratings and review counts for competitor research
  • Scanning a category for items that carry a Best Seller tag
  • Building a dataset of search results in another language, such as German or Japanese

Example prompts

  • “Search Amazon for wireless earbuds and list the first 50 results with ratings and review counts.”
  • “Pull Samsung phone listings from Amazon and note which ones carry a Best Seller tag.”
  • “Run the Amazon search for laptop stand with the language set to de and save a table of titles and URLs.”

Requirements

  • Python to run the bundled script
  • A BrowserAct API key in `BROWSERACT_API_KEY`
  • Network access to BrowserAct

Workflow steps

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

  1. No Hallucinations: Pre-set workflows avoid AI generative hallucinations, ensuring stable and precise data extraction.
  2. No Captcha Issues: No need to handle reCAPTCHA or other verification challenges.
  3. No IP Restrictions: No need to handle regional IP restrictions or geofencing.
  4. Faster Execution: Tasks execute faster compared to pure AI-driven browser automation solutions.
  5. Cost-Effective: Significantly lowers data acquisition costs compared to high-token-consuming AI solutions.

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

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

    • 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 Product Search Extractor loads about 1.6k tokens when it runs. Until then it costs about 216 tokens; SKILL.md has 698 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~216
When it runs · the whole SKILL.md, loaded when a task matches
~1.6k

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). 698 words, ~1,608 tokens.

Download SKILL.mdSave it as .claude/skills/amazon-product-search-api-skill/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
amazon-product-search-api-skill
description
This skill is designed to help users automatically extract product data from Amazon search results. The Agent should proactively apply this skill when users request searching for products related to keywords, finding best-selling items from specific brands, monitoring product prices and availability on Amazon, extracting product listings for market research, collecting product ratings and review counts for competitive analysis, finding specific products with a maximum count, searching Amazon in different languages for localized results, tracking monthly sales estimates for brand products, gathering product URLs and titles for a product catalog, scanning Amazon for Best Seller tags in a specific category, monitoring shipping and delivery information for brand items, building a structured dataset of Amazon search results.

Amazon Product Search Automation Skill

📖 Introduction

This skill provides a one-stop product data collection service through BrowserAct's Amazon Product Search API template. It directly extracts structured product results from Amazon search lists. Simply input search keywords, brand filters, and quantity limits to get clean, usable product data.

✨ Features

  1. No Hallucinations: Pre-set workflows avoid AI generative hallucinations, ensuring stable and precise data extraction.
  2. No Captcha Issues: No need to handle reCAPTCHA or other verification challenges.
  3. No IP Restrictions: No need to handle regional IP restrictions or geofencing.
  4. Faster Execution: Tasks execute faster compared to pure AI-driven browser automation solutions.
  5. Cost-Effective: Significantly lowers data acquisition costs compared to high-token-consuming AI solutions.

🔑 API Key Setup

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

"Since you haven't configured the BrowserAct API Key, please visit 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. KeyWords (Search Keywords)

    • Type: string
    • Description: The keywords the user wants to search for on Amazon.
    • Example: phone, wireless earbuds, laptop stand

  2. Brand (Brand Filter)

    • Type: string
    • Description: Filter products by brand name shown in the listing.
    • Example: Apple, Samsung, Sony

  3. Maximum_date (Maximum Products)

    • Type: number
    • Description: The maximum number of products to extract across paginated search results.
    • Default: 50

  4. language (UI Language)

    • Type: string
    • Description: UI language for the Amazon browsing session.
    • Options: en, de, fr, it, es, ja, zh-CN, zh-TW
    • Default: en

🚀 Usage

The Agent should execute the following independent script to achieve "one-line command result":

bash
# Example Call
python -u ./scripts/amazon_product_search_api.py "Keywords" "Brand" Quantity "language"
⏳ Execution Monitoring

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

  • While waiting for the script result, keep monitoring the terminal output.
  • As long as the terminal is outputting new status logs, the task is running normally; do not mistake it for a deadlock or unresponsiveness.
  • Only if the status remains unchanged for a long time or the script stops outputting without returning a result should you consider triggering the retry mechanism.

📊 Data Output

After successful execution, the script will parse and print results directly from the API response. Results include:

  • product_title: Product name
  • product_url: Detail page URL
  • rating_score: Average star rating
  • review_count: Total number of reviews
  • monthly_sales: Estimated monthly sales (if available)
  • current_price: Current selling price
  • list_price: Original list price (if available)
  • delivery_info: Delivery or fulfillment information
  • shipping_location: Shipping origin or location
  • is_best_seller: Whether marked as Best Seller
  • is_available: Whether available for purchase
Show full SKILL.md (250 more words)Show less

⚠️ Error Handling & Retry

If an error occurs during script execution (e.g., network fluctuations or task failure), the Agent should follow this logic:

  1. Check Output Content:

    • If the output contains "Invalid authorization", it means the API Key is invalid or expired. Do not retry; guide the user to re-check and provide the correct API Key.
    • If the output does not contain "Invalid authorization" but the task failed (e.g., output starts with Error: or returns empty results), the Agent should automatically try to re-execute the script once.

  2. Retry Limit:

    • Automatic retry is limited to one time. If the second attempt fails, stop retrying and report the specific error information to the user.

🌟 Typical Use Cases

  1. Market Research: Search for "wireless earbuds" from "Sony" to analyze the current market.
  2. Competitive Monitoring: Track "Samsung" phone prices and availability on Amazon.
  3. Catalog Discovery: Gather product titles and URLs for a new product catalog in the "laptop stand" category.
  4. Localized Analysis: Search Amazon in "ja" (Japanese) to understand products available in the Japan region.
  5. Best Seller Tracking: Identify products marked as "Best Seller" for a specific brand.
  6. Pricing Intelligence: Compare current_price and list_price to monitor discounts.
  7. Sales Trend Estimation: Use monthly_sales data to estimate market demand for certain items.
  8. Shipping Efficiency Study: Analyze delivery_info and shipping_location for various brands.
  9. Large-scale Data Extraction: Collect up to 100 products for a comprehensive dataset.
  10. Product Availability Check: Verify if specific brand products are currently is_available for purchase.

© 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-product-search-api-skill of browser-act/skills.

  • SKILL.md
  • scripts/amazon_product_search_api.py

Open the folder on GitHubat commit 11c057b

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 browser-act/skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Amazon Product Search Extractor 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 Product Search Extractor compared with similar skills
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Amazon Product Search Extractor this skillbrowser-act/skills6.1k2 repos~1.6kAutomated safety check: PassMIT
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LinkfoxagentLeoYeAI/openclaw-master-skills2.2k—~3.8kAutomated safety check: PassMIT
Competitor Analysissocial-media-skills/skills125—~1.3kAutomated safety check: PassMIT
Extractactionbook/actionbook1.6k2 repos~3.4kAutomated safety check: PassApache-2.0
Vendor Pricing Trackerfirecrawl/web-agent1.2k—~1.2kAutomated safety check: PassMIT

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Categories

Questions about Amazon Product Search Extractor

What does Amazon Product Search Extractor do?

Collects structured product data from Amazon search results for a keyword and optional brand, using a BrowserAct script, for market and competitor research. py`, which calls BrowserAct's Amazon Product Search template and returns product records from Amazon search listings. You give it a keyword, an optional brand filter, a cap on how many products to collect (50 by default) and a language for the Amazon session (English by default).

When should I use Amazon Product Search Extractor?

Amazon Product Search Extractor fits situations like: pulling Amazon listings for a keyword to size up a product category; comparing one brand's products, ratings and review counts for competitor research; scanning a category for items that carry a Best Seller tag; building a dataset of search results in another language, such as German or Japanese.

How do I install Amazon Product Search Extractor in Claude Code?

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

How do I install Amazon Product Search Extractor in Codex?

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

Can I use Amazon Product Search Extractor 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-product-search-api-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-product-search-api-skill, .gemini/skills/amazon-product-search-api-skill, .github/skills/amazon-product-search-api-skill and .opencode/skills/amazon-product-search-api-skill in your project.

What does Amazon Product Search Extractor need to run?

Going by SKILL.md and its folder, Amazon Product Search Extractor 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: Python to run the bundled script; A BrowserAct API key in `BROWSERACT_API_KEY`; Network access to BrowserAct.

Does Amazon Product Search Extractor access the network?

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

Is Amazon Product Search Extractor 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 Product Search Extractor use?

Amazon Product Search Extractor 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 Product Search Extractor use?

About 1.6k tokens (SKILL.md is roughly 6.4k 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 Product Search Extractor?

Skills that share tags, products or a category with Amazon Product Search Extractor: Competitor Comparison Matrix (firecrawl/web-agent, 1.2k stars), Linkfoxagent (LeoYeAI/openclaw-master-skills, 2.2k stars), Competitor Analysis (social-media-skills/skills, 125 stars) and Extract (actionbook/actionbook, 1.6k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Amazon Product Search Extractor?

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.