
Amazon Product Search Automation
FreeEffortlessly extract product data from Amazon searches.
Free · Opens the source repo
What Amazon Product Search Automation does
The Amazon Product Search Automation skill is designed to streamline the process of gathering structured product data from Amazon's search results. By simply providing keywords, brand filters, and quantity limits, users can obtain clean, usable data without the hassle of navigating through Amazon's interface manually. This skill is particularly beneficial for market researchers, competitive analysts, and e-commerce professionals who need reliable product information quickly and efficiently.
This skill operates by leveraging BrowserAct's Amazon Product Search API, which ensures that data extraction is both accurate and fast. Users can avoid common pitfalls associated with traditional scraping methods, such as CAPTCHA challenges and IP restrictions. The automation allows for rapid execution, making it a cost-effective solution compared to other AI-driven approaches that may consume significant resources.
The provided script is straightforward to use, requiring only a few input parameters to customize the search according to user needs. It outputs detailed product information, including titles, URLs, ratings, review counts, and pricing details. This structured output is ideal for users looking to compile comprehensive datasets for analysis or cataloging. Moreover, the skill supports multiple languages, enabling users to conduct localized searches effectively.
Overall, this skill is a valuable tool for anyone needing to extract and analyze product data from Amazon efficiently. It simplifies the data collection process, allowing users to focus on analysis rather than data gathering.
When to use it
Use this skill when you need to gather product information from Amazon quickly for market research, competitive analysis, or cataloging purposes.
When not to use it
This skill may not be suitable for one-off searches or when you require highly customized scraping beyond the provided parameters.
What you can build with it
Market Research
Search for 'wireless earbuds' from 'Sony' to analyze the current market.
Competitive Monitoring
Track 'Samsung' phone prices and availability on Amazon.
Catalog Discovery
Gather product titles and URLs for a new product catalog in the 'laptop stand' category.
How to install Amazon Product Search Automation
View source1. Install with the skills CLI
npx skills add browser-act/skills/amazon-product-search-api-skill --agent claude-code2. Or install it manually
Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.
Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs
Inside SKILL.md
Written by browser-actAmazon 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
- No Hallucinations: Pre-set workflows avoid AI generative hallucinations, ensuring stable and precise data extraction.
- No Captcha Issues: No need to handle reCAPTCHA or other verification challenges.
- No IP Restrictions: No need to handle regional IP restrictions or geofencing.
- Faster Execution: Tasks execute faster compared to pure AI-driven browser automation solutions.
- 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:
-
KeyWords (Search Keywords)
- Type:
string - Description: The keywords the user wants to search for on Amazon.
- Example:
phone,wireless earbuds,laptop stand
- Type:
-
Brand (Brand Filter)
- Type:
string - Description: Filter products by brand name shown in the listing.
- Example:
Apple,Samsung,Sony
- Type:
-
Maximum_date (Maximum Products)
- Type:
number - Description: The maximum number of products to extract across paginated search results.
- Default:
50
- Type:
-
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
- Type:
🚀 Usage
The Agent should execute the following independent script to achieve "one-line command result":
# 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 nameproduct_url: Detail page URLrating_score: Average star ratingreview_count: Total number of reviewsmonthly_sales: Estimated monthly sales (if available)current_price: Current selling pricelist_price: Original list price (if available)delivery_info: Delivery or fulfillment informationshipping_location: Shipping origin or locationis_best_seller: Whether marked as Best Selleris_available: Whether available for purchase
⚠️ Error Handling & Retry
If an error occurs during script execution (e.g., network fluctuations or task failure), the Agent should follow this logic:
-
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 withError:or returns empty results), the Agent should automatically try to re-execute the script once.
- If the output contains
-
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
- Market Research: Search for "wireless earbuds" from "Sony" to analyze the current market.
- Competitive Monitoring: Track "Samsung" phone prices and availability on Amazon.
- Catalog Discovery: Gather product titles and URLs for a new product catalog in the "laptop stand" category.
- Localized Analysis: Search Amazon in "ja" (Japanese) to understand products available in the Japan region.
- Best Seller Tracking: Identify products marked as "Best Seller" for a specific brand.
- Pricing Intelligence: Compare
current_priceandlist_priceto monitor discounts. - Sales Trend Estimation: Use
monthly_salesdata to estimate market demand for certain items. - Shipping Efficiency Study: Analyze
delivery_infoandshipping_locationfor various brands. - Large-scale Data Extraction: Collect up to 100 products for a comprehensive dataset.
- Product Availability Check: Verify if specific brand products are currently
is_availablefor purchase.
Frequently asked questions about Amazon Product Search Automation
Similar skills
Power BI Semantic Modeling
Optimize your Power BI data models with best practices.
Data Context Extractor
Tailor data analysis skills to your company's needs.
Power BI Performance Troubleshooting
Systematic guidance for optimizing Power BI performance.
Power BI Model Design Review
Optimize your Power BI data models with expert reviews.
Power BI DAX Formula Optimizer
Optimize your DAX formulas for better performance and clarity.
Fabric Lakehouse
Optimize your data solutions with Lakehouse best practices.
