设计与创意 · SKILL.md

amazon-competitor-analyzer

amazon-competitor-analyzer

【设计与创意】Scrapes Amazon product data from ASINs using browseract.com automation API and performs surgical competitive analysis. Compares specifications, pricing, review quality, and visual strategies to identify competitor moats and vulnerabilities.

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Kkernel8901 / ai-agent-skills-classificationMIT

技能说明

GitHub

name: amazon-competitor-analyzer description: Scrapes Amazon product data from ASINs using browseract.com automation API and performs surgical competitive analysis. Compares specifications, pricing, review quality, and visual strategies to identify competitor moats and vulnerabilities.

Amazon Competitor Analyzer

This skill scrapes Amazon product data from user-provided ASINs using browseract.com's browser automation API and performs deep competitive analysis. It compares specifications, pricing, review quality, and visual strategies to identify competitor moats and vulnerabilities.

✨ Platform Compatibility

✅ Works Powerfully & Reliably On All Major AI Assistants

Platform	Status	How to Install
OpenCode	✅ Fully Supported	Copy skill folder to `~/.opencode/skills/`
Claude Code	✅ Fully Supported	Native skill support
Cursor	✅ Fully Supported	Copy to `~/.cursor/skills/`
OpenClaw	✅ Fully Supported	Compatible

Why Choose BrowserAct Skills?

🚀 Stable & crash-free execution
⚡ Fast response times
🔧 No configuration headaches
📦 Plug & play installation
💬 Professional support

When to Use This Skill

Competitive research: Input multiple ASINs to understand market landscape
Pricing strategy analysis: Compare price bands across similar products
Specification benchmarking: Deep dive into technical specs and feature differences
Review insights: Analyze review quality, quantity, and sentiment patterns
Visual strategy research: Evaluate main images, A+ content, and brand visuals
Market opportunity discovery: Identify gaps and potential threats
Product optimization: Develop optimization strategies based on competitor analysis
New product research: Support new product development with market data

What This Skill Does

ASIN Data Collection: Automatically extract product title, price, rating, review count, images, and core data using BrowserAct workflow templates
Specification Extraction: Deep extraction of technical specs, features, and materials
Review Quality Analysis: Analyze review patterns, keywords, and sentiment
Visual Strategy Assessment: Evaluate main images, A+ page design, and brand consistency
Multi-Dimensional Comparison: Side-by-side comparison of key metrics across products
Moat Identification: Identify core competitive advantages and barriers
Vulnerability Discovery: Find competitor weaknesses and market opportunities
Structured Output: Generate JSON and Markdown analysis reports

Prerequisites

1. BrowserAct.com Account Setup

You need a BrowserAct.com account and API key:

Visit browseract.com
Sign up for an account
Navigate to API settings
Generate an API key
Store your API key securely (environment variables recommended)

2. Environment Configuration

Set your API key as an environment variable:

export BROWSERACT_API_KEY="your-api-key-here"

Or create a .env file:

BROWSERACT_API_KEY=your-api-key-here

How to Use

Basic Competitor Analysis

Analyze the following Amazon ASIN: B09XYZ12345

Compare these three products: B07ABC11111, B07DEF22222, B07GHI33333

Deep Specification Comparison

Analyze the technical specification differences: B09XYZ12345, B09ABC11111

Review Quality Analysis

Analyze review quality and feedback: B09XYZ12345, B07DEF22222

Visual Strategy Research

Research main image and visual presentation strategies: B09XYZ12345, B09ABC11111

Complete Competitive Analysis

Analyze competitor landscape: B09XYZ12345, B07DEF22222, B07GHI33333, B09JKL44444

Instructions

When a user requests Amazon competitor analysis:

1. ASIN Identification and Validation

Identify ASINs from user input:

ASIN Format: 10-character alphanumeric (e.g., B09XYZ12345)
Validation: Check format compliance with Amazon ASIN standards
URL Parsing: Extract ASIN from Amazon product URLs
Error Handling: Prompt user to correct invalid ASINs

2. BrowserAct API Implementation

"""
BrowserAct API - Run Template Task and Wait for Completion
Scenarios for beginners - Synchronous task execution with official templates
"""
import os
import time
import traceback
import json
import requests

# ============ Configuration Area ============
# API Key - Get from: https://www.browseract.com/reception/integrations
API_KEY = os.getenv("BROWSERACT_API_KEY", "your-api-key-here")

# Workflow Template ID for Amazon product scraping
# You can get it from:
# - Run: python Workflow-Python/11.list_official_workflow_templates.py
# - Or visit: https://www.browseract.com/template?platformType=0
WORKFLOW_TEMPLATE_ID = "77814333389670716"

# Polling configuration
POLL_INTERVAL = 5  # Check task status every 5 seconds
MAX_WAIT_TIME = 1800  # Maximum wait time: 30 minutes (1800 seconds)

API_BASE_URL = "https://api.browseract.com/v2/workflow"


def create_input_parameters(asins):
    """Create input parameters for the workflow template"""
    return [
        {
            "name": "ASIN",
            "value": asin.strip()
        }
        for asin in asins if asin.strip()
    ]


def run_task_by_template(workflow_template_id, input_parameters):
    """Start a task using template"""
    headers = {
        "Authorization": f"Bearer {API_KEY}"
    }
    
    data = {
        "workflow_template_id": workflow_template_id,
        "input_parameters": input_parameters,
    }
    
    api_url = f"{API_BASE_URL}/run-task-by-template"
    response = requests.post(api_url, json=data, headers=headers)
    
    if response.status_code == 200:
        result = response.json()
        task_id = result["id"]
        print(f"Task started successfully, Task ID: {task_id}")
        if "profileId" in result:
            print(f"   Profile ID: {result['profileId']}")
        return task_id
    else:
        print(f"Failed to start task: {response.json()}")
        return None


def get_task_status(task_id):
    """Get task status"""
    headers = {
        "Authorization": f"Bearer {API_KEY}"
    }
    
    api_url = f"{API_BASE_URL}/get-task-status?task_id={task_id}"
    try:
        response = requests.get(api_url, headers=headers, timeout=30)
        
        if response.status_code == 200:
            return response.json().get("status")
        else:
            print(f"Failed to get task status: {response.json()}")
            return None
    except (requests.exceptions.SSLError, requests.exceptions.ConnectionError, 
            requests.exceptions.Timeout, requests.exceptions.RequestException) as e:
        # Network error, will retry in next polling cycle
        return None


def get_task(task_id):
    """Get detailed task information and results"""
    headers = {
        "Authorization": f"Bearer {API_KEY}"
    }
    
    api_url = f"{API_BASE_URL}/get-task?task_id={task_id}"
    try:
        response = requests.get(api_url, headers=headers, timeout=30)
        
        if response.status_code == 200:
            return response.json()
        else:
            print(f"Failed to get task details: {response.json()}")
            return None
    except (requests.exceptions.SSLError, requests.exceptions.ConnectionError, 
            requests.exceptions.Timeout, requests.exceptions.RequestException) as e:
        print(f"Network error while getting task details: {type(e).__name__}")
        return None


def wait_for_task_completion(task_id):
    """Wait for task completion with progress updates"""
    start_time = time.time()
    previous_status = None
    
    print(f"Waiting for task completion (max wait time: {MAX_WAIT_TIME // 60} minutes)...")
    
    while True:
        # Check if timeout
        elapsed_time = time.time() - start_time
        if elapsed_time > MAX_WAIT_TIME:
            print(f"Wait timeout (waited {elapsed_time:.0f} seconds)")
            return None
        
        # Get task status
        status = get_task_status(task_id)
        
        if status is None:
            # Network error or API error, continue waiting
            elapsed = int(elapsed_time)
            print(f"   Network error, retrying... (waited {elapsed} seconds)", end="\r")
        elif status == "finished":
            print(f"Task completed successfully!")
            return "finished"
        elif status == "failed":
            print(f"Task execution failed")
            return "failed"
        elif status == "canceled":
            print(f"Task canceled")
            return "canceled"
        else:
            # running, created, paused, etc.
            elapsed = int(elapsed_time)
            if status != previous_status:
                print(f"   Status: {status} (waited {elapsed} seconds)", end="\r")
                previous_status = status
            else:
                print(f"   Status: {status} (waited {elapsed} seconds)", end="\r")
        
        # Wait before checking again
        time.sleep(POLL_INTERVAL)


def scrape_amazon_products(asins):
    """
    Main function to scrape Amazon product data
    
    Args:
        asins: List of Amazon ASINs to scrape
        
    Returns:
        dict: Task result containing product data
    """
    if not asins:
        raise ValueError("No ASINs provided for scraping")
    
    # Create input parameters
    input_parameters = create_input_parameters(asins)
    
    print(f"Starting Amazon product scraping for {len(asins)} ASIN(s)...")
    print(f"ASINs: {[p['value'] for p in input_parameters]}")
    
    # Step 1: Start task using template
    task_id = run_task_by_template(WORKFLOW_TEMPLATE_ID, input_parameters)
    
    if task_id is None:
        raise Exception("Unable to start scraping task")
    
    # Step 2: Wait for task completion
    final_status = wait_for_task_completion(task_id)
    
    if final_status != "finished":
        raise Exception(f"Task did not complete successfully. Status: {final_status}")
    
    # Step 3: Get task results
    task_info = get_task(task_id)
    
    if task_info is None:
        raise Exception("Unable to retrieve task results")
    
    return task_info


def main():
    """Main execution function for testing"""
    print("=" * 60)
    print("Amazon Product Scraper - BrowserAct Integration")
    print("=" * 60)
    
    try:
        # Example: Scrape multiple ASINs
        test_asins = ["B09XYZ12345", "B07ABC11111"]
        
        print(f"\nScraping Amazon products: {test_asins}")
        results = scrape_amazon_products(test_asins)
        
        print("\n" + "=" * 60)
        print("Scraping Results (JSON):")
        print("=" * 60)
        print(json.dumps(results, indent=2, ensure_ascii=False))
        
        return results
        
    except Exception as e:
        error = traceback.format_exc()
        print(f"Error occurred: {error}")
        raise


# Example usage
if __name__ == "__main__":
    main()

3. Task Output Data Structure

The BrowserAct API returns structured data in the following format:

{
  "id": "task_id_12345",
  "status": "finished",
  "created_at": "2026-02-06T10:00:00Z",
  "completed_at": "2026-02-06T10:05:00Z",
  "results": {
    "products": [
      {
        "asin": "B09XYZ12345",
        "url": "https://www.amazon.com/dp/B09XYZ12345",
        "product_info": {
          "title": "Complete product title",
          "brand": "Brand name",
          "manufacturer": "Manufacturer",
          "model": "Model"
        },
        "pricing": {
          "current_price": 29.99,
          "original_price": 39.99,
          "discount_percent": 25,
          "currency": "USD"
        },
        "reviews": {
          "average_rating": 4.5,
          "total_count": 1234,
          "rating_distribution": {
            "5_star": 65,
            "4_star": 20,
            "3_star": 10,
            "2_star": 3,
            "1_star": 2
          }
        },
        "specifications": {
          "weight": "1.5 lbs",
          "dimensions": "10 x 5 x 3 inches",
          "material": "Plastic/Metal",
          "features": ["Feature 1", "Feature 2"]
        },
        "media": {
          "main_image": "https://example.com/image.jpg",
          "thumbnails": ["url1", "url2"],
          "has_video": true,
          "has_a_plus": true
        },
        "seller": {
          "type": "Amazon",
          "fulfillment": "FBA",
          "availability": "InStock"
        }
      }
    ]
  }
}

4. Workflow Template Parameters

Parameter	Type	Required	Description
ASIN	string	Yes	Amazon Standard Identification Number (10 characters)
output_format	string	No	Output format: "json" or "markdown" (default: "json")
include_reviews	boolean	No	Include review analysis (default: true)
include_images	boolean	No	Include image URLs (default: true)
include_specs	boolean	No	Include specifications (default: true)

5. Error Handling

import requests

def safe_api_call(api_func, max_retries=3, delay=5):
    """Execute API call with retry logic"""
    for attempt in range(max_retries):
        try:
            result = api_func()
            if result is not None:
                return result
        except requests.exceptions.RequestException as e:
            print(f"Attempt {attempt + 1} failed: {e}")
        
        if attempt < max_retries - 1:
            time.sleep(delay * (attempt + 1))  # Exponential backoff
    
    raise Exception(f"API call failed after {max_retries} attempts")


def handle_api_error(response):
    """Handle API error responses"""
    error_messages = {
        400: "Invalid request parameters",
        401: "Authentication failed - check API key",
        403: "Access denied - insufficient permissions",
        404: "Resource not found",
        429: "Rate limit exceeded - slow down",
        500: "Internal server error",
        503: "Service temporarily unavailable"
    }
    
    status_code = response.status_code
    message = error_messages.get(status_code, f"Unknown error (status: {status_code})")
    
    return {
        "error": message,
        "status_code": status_code,
        "details": response.json() if response.content else None
    }

6. Rate Limiting Best Practices

import time
from datetime import datetime, timedelta


class RateLimiter:
    """Rate limiter for API calls"""
    
    def __init__(self, max_requests=10, time_window=60):
        self.max_requests = max_requests
        self.time_window = time_window
        self.requests = []
    
    def wait_if_needed(self):
        """Wait if rate limit would be exceeded"""
        now = datetime.now()
        
        # Remove requests outside the time window
        self.requests = [req_time for req_time in self.requests 
                        if now - req_time < timedelta(seconds=self.time_window)]
        
        if len(self.requests) >= self.max_requests:
            # Wait until oldest request expires
            oldest = min(self.requests)
            wait_time = self.time_window - (now - oldest).seconds
            if wait_time > 0:
                print(f"Rate limit reached, waiting {wait_time} seconds...")
                time.sleep(wait_time)
        
        self.requests.append(now)


# Usage
rate_limiter = RateLimiter(max_requests=10, time_window=60)

def scrape_with_rate_limiting(asins):
    """Scrape products with rate limiting"""
    results = []
    
    for asin in asins:
        rate_limiter.wait_if_needed()
        result = scrape_single_asin(asin)
        results.append(result)
    
    return results

Data Extraction and Structuring

## Product Information

### ASIN: B09XYZ12345

**Product Title**: [Complete product title]
**Status**: Success / Failed
**Scraped At**: [Timestamp]

---

#### Pricing Information

| Item | Value |
|------|-------|
| Current Price | $XX.XX |
| Original Price | $XX.XX |
| Discount | XX% OFF |
| Price Range | $XX.XX - $XX.XX (variants) |

---

#### Rating and Reviews

| Item | Value |
|------|-------|
| Average Rating | X.X / 5.0 |
| Total Reviews | X,XXX |
| Rating Distribution | 5-star XX% / 4-star XX% / 3-star XX% / 2-star XX% / 1-star XX% |

---

#### Visual Assets

| Item | Count/Status |
|------|--------------|
| Main Image | [URL / N/A] |
| Thumbnail Count | X images |
| Video Count | X videos |
| 360° View | Available / N/A |
| A+ Page | Available / N/A |
| Brand Story | Available / N/A |

---

#### Specification Parameters

| Parameter | Value |
|-----------|-------|
| Brand | [Brand name] |
| Model | [Model info] |
| Weight | [Weight] |
| Dimensions | [Dimensions] |
| Material | [Material] |
| Country of Origin | [Country] |

---

#### Seller Information

| Item | Information |
|------|-------------|
| Seller Type | Amazon / Third-Party |
| Seller Name | [Name] |
| Fulfillment | FBA / Self |
| Availability | In Stock / Out of Stock / Pre-order |

### JSON Output Format

```json
{
  "asin": "B09XYZ12345",
  "url": "https://www.amazon.com/dp/B09XYZ12345",
  "scraped_at": "2026-02-06T10:00:00Z",
  "status": "success",
  "product_info": {
    "title": "[Complete product title]",
    "brand": "[Brand name]",
    "manufacturer": "[Manufacturer]",
    "model": "[Model]"
  },
  "pricing": {
    "current_price": XX.XX,
    "original_price": XX.XX,
    "discount_percent": XX,
    "currency": "USD",
    "price_range": {
      "min": XX.XX,
      "max": XX.XX
    }
  },
  "reviews": {
    "average_rating": X.X,
    "total_count": XXXX,
    "rating_distribution": {
      "5_star": XX,
      "4_star": XX,
      "3_star": XX,
      "2_star": XX,
      "1_star": XX
    }
  },
  "specifications": {
    "weight": "[Weight]",
    "dimensions": "[Dimensions]",
    "material": "[Material]",
    "country_of_origin": "[Country]",
    "features": ["[Feature1]", "[Feature2]"]
  },
  "media": {
    "main_image": "[URL]",
    "thumbnails": ["[URL1]", "[URL2]"],
    "has_video": true,
    "has_a_plus": true
  },
  "seller": {
    "type": "[Amazon/Third-Party]",
    "name": "[Seller name]",
    "fulfillment": "[FBA/Self]",
    "availability": "[InStock/OutOfStock]"
  },
  "bestseller_rank": [
    {
      "category": "[Category]",
      "rank": XXX
    }
  ]
}

LLM Deep Analysis

After collecting data from multiple products, perform surgical competitive analysis:

## Competitive Deep Analysis

### 1. Price Dimension Analysis

#### Price Band Positioning

| Product | ASIN | Price | Position | Price Advantage |
|---------|------|-------|----------|----------------|
| [Product A] | B09XXX | $XX.XX | Premium/Mid-range/Economy | High/Medium/Low |
| [Product B] | B09YYY | $XX.XX | Premium/Mid-range/Economy | High/Medium/Low |
| [Product C] | B09ZZZ | $XX.XX | Premium/Mid-range/Economy | High/Medium/Low |

**Price Range Analysis**:
- Lowest Price: $XX.XX ([Product Name])
- Highest Price: $XX.XX ([Product Name])
- Median Price: $XX.XX
- Price Spread: XX%

**Pricing Strategy Insights**:
- [Analyze pricing strategies across products]
- [Identify price gaps]
- [Discover pricing opportunities]

#### Value Assessment

| Product | Feature Count | Cost per Feature | Feature Density | Value Index |
|---------|---------------|------------------|-----------------|-------------|
| [A] | XX | $X.XX | High/Medium/Low | X.X |
| [B] | XX | $X.XX | High/Medium/Low | X.X |
| [C] | XX | $X.XX | High/Medium/Low | X.X |

### 2. Specification Comparison

#### Core Parameter Matrix

| Parameter | [Product A] | [Product B] | [Product C] | Industry Benchmark |
|-----------|-------------|-------------|-------------|-------------------|
| [Param 1] | [Value] | [Value] | [Value] | [Value] |
| [Param 2] | [Value] | [Value] | [Value] | [Value] |
| [Param 3] | [Value] | [Value] | [Value] | [Value] |
| [Param 4] | [Value] | [Value] | [Value] | [Value] |
| [Param 5] | [Value] | [Value] | [Value] | [Value] |

#### Differentiation Analysis

**Exclusive Advantages**:
- [Product A Exclusive]: Description + User Value + Market Rarity
- [Product B Exclusive]: Description + User Value + Market Rarity
- [Product C Exclusive]: Description + User Value + Market Rarity

**Weakness Comparison**:
- [Product A Weakness]: Missing Feature + User Impact + Alternative
- [Product B Weakness]: Missing Feature + User Impact + Alternative
- [Product C Weakness]: Missing Feature + User Impact + Alternative

### 3. Review Weight Analysis

#### Review Quality Assessment

| Product | Total Reviews | Avg Rating | Positive % | Negative % | Quality Index |
|---------|---------------|------------|------------|------------|---------------|
| [A] | X,XXX | X.X | XX% | XX% | X.X |
| [B] | X,XXX | X.X | XX% | XX% | X.X |
| [C] | X,XXX | X.X | XX% | XX% | X.X |

#### Review Keyword Analysis

**Positive Keywords**:
- [Keyword 1] (XX occurrences)
- [Keyword 2] (XX occurrences)
- [Keyword 3] (XX occurrences)

**Negative Keywords**:
- [Keyword 1] (XX occurrences)
- [Keyword 2] (XX occurrences)
- [Keyword 3] (XX occurrences)

#### Sentiment Analysis

| Sentiment Dimension | [Product A] | [Product B] | [Product C] |
|--------------------|-------------|-------------|-------------|
| Positive Sentiment | XX% | XX% | XX% |
| Neutral Sentiment | XX% | XX% | XX% |
| Negative Sentiment | XX% | XX% | XX% |

**Review Insights**:
- [Product A Key Praise Points]
- [Product A Main Complaints]
- [Product B Key Praise Points]
- [Product B Main Complaints]

#### Credibility Analysis

| Product | Verified Purchase Rate | Avg Review Length | Image/Video Review % | Credibility Score |
|---------|----------------------|-------------------|---------------------|-------------------|
| [A] | XX% | XX words | XX% | X.X |
| [B] | XX% | XX words | XX% | X.X |
| [C] | XX% | XX words | XX% | X.X |

### 4. Visual Strategy Analysis

#### Main Image Assessment

| Product | Style | Background | Information Density | Visual Appeal | Conversion Potential |
|---------|-------|------------|---------------------|---------------|---------------------|
| [A] | [Description] | Solid/Scene/Transparent | High/Medium/Low | Score | High/Medium/Low |
| [B] | [Description] | Solid/Scene/Transparent | High/Medium/Low | Score | High/Medium/Low |
| [C] | [Description] | Solid/Scene/Transparent | High/Medium/Low | Score | High/Medium/Low |

**Image Strategy Insights**:
- [Analyze visual strategies across products]
- [Identify effective visual elements]
- [Discover visual optimization opportunities]

#### A+ Page Analysis

| Product | A+ Type | Module Count | Information Architecture | Brand Presentation | UX Score |
|---------|---------|-------------|-------------------------|-------------------|----------|
| [A] | Standard/Premium | XX | Excellent/Good/Poor | Strong/Medium/Weak | Score |
| [B] | Standard/Premium | XX | Excellent/Good/Poor | Strong/Medium/Weak | Score |
| [C] | Standard/Premium | XX | Excellent/Good/Poor | Strong/Medium/Weak | Score |

**A+ Page Highlights**:
- [Product A A+ Highlights]
- [Product B A+ Highlights]
- [Product C A+ Highlights]

### 5. Competitive Landscape Summary

#### Market Position Matrix

| Dimension | Leader | Challenger | Follower | Niche |
|----------|--------|------------|----------|-------|
| Volume | [Product] | [Product] | [Product] | [Product] |
| Price | [Product] | [Product] | [Product] | [Product] |
| Rating | [Product] | [Product] | [Product] | [Product] |
| Features | [Product] | [Product] | [Product] | [Product] |

#### Competitive Differentiation Map

                Feature Richness
                      ↑
                      |
     [Product B]      |          [Product A]
     (High Feature)    |         (High Brand)
                      |

◄──────────────────────┼──────────────────────► | [Product C] | [Product D] (Low Feature) | (Low Brand) | ↓ Simplified Features

Price Band: Left Low Right High | Brand: Bottom Low Top High


### 6. Moat Identification

#### Competitor Core Moats

**Patent/Technology Barriers**:
- [Specific patent or tech advantage]
- [Imitation Difficulty]: High/Medium/Low
- [Defense Duration]: Long-term/Medium-term/Short-term

**Economies of Scale**:
- [Cost advantage source]
- [Pricing flexibility]
- [Competitor catch-up difficulty]

**Brand Equity**:
- [Brand awareness level]
- [Customer loyalty metrics]
- [Brand premium capability]

**Channel Advantages**:
- [Amazon ranking strength]
- [Buy Box share]
- [Review accumulation advantage]

**Supply Chain Barriers**:
- [Exclusive supplier relationships]
- [Cost structure advantage]
- [Capacity constraints]

#### Moat Strength Assessment

| Moat Type | [Product A] | [Product B] | [Product C] |
|-----------|-------------|-------------|-------------|
| Patents/Tech | ⭐⭐⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐ |
| Economies of Scale | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐ |
| Brand Assets | ⭐⭐⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐ |
| Channel Advantage | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐ |
| Customer Lock-in | ⭐⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐ |
| **Overall Score** | **X.X/5.0** | **X.X/5.0** | **X.X/5.0** |

### 7. Vulnerabilities and Opportunities

#### Strategic Weaknesses

**Product-Level**:
- [Weakness 1]: Description + User Impact + Market Share Effect
- [Weakness 2]: Description + User Impact + Market Share Effect

**Pricing-Level**:
- [Price Disadvantage]: Analysis + Cause + Adjustment Recommendation

**Review-Level**:
- [Negative Review Pattern]: Issue + Difficulty + Exploitation Value

**Visual-Level**:
- [Visual Defect]: Issue + User Impact + Optimization Space

**Operational-Level**:
- [Operational Shortcoming]: Performance + Impact + Improvement Direction

#### Opportunity Matrix

| Opportunity Type | Description | Size | Difficulty | Priority |
|-----------------|-------------|------|------------|----------|
| [Opportunity 1] | [Description] | Large/Medium/Small | High/Medium/Low | Immediate/Planned/Watch |
| [Opportunity 2] | [Description] | Large/Medium/Small | High/Medium/Low | Immediate/Planned/Watch |
| [Opportunity 3] | [Description] | Large/Medium/Small | High/Medium/Low | Immediate/Planned/Watch |

#### Differentiation Entry Points

**Best Differentiation Directions**:
1. [Direction 1]: Recommendation + Expected Effect + Implementation Difficulty
2. [Direction 2]: Recommendation + Expected Effect + Implementation Difficulty
3. [Direction 3]: Recommendation + Expected Effect + Implementation Difficulty

**Competitive Traps to Avoid**:
- [Trap 1]: Cause + Alternative Approach
- [Trap 2]: Cause + Alternative Approach

### 8. Recommendations

#### Strategic Positioning

**Competition Strategy for [Target Product]**:
- [Core Strategy]: Actionable recommendations
- [Differentiation Focus]: Key differentiators
- [Pricing Strategy]: Pricing recommendations
- [Marketing Focus]: Promotion direction

#### Product Optimization Priority

| Optimization Item | Urgency | Impact | Difficulty | Recommendation |
|------------------|---------|--------|------------|---------------|
| [Item 1] | High/Medium/Low | High/Medium/Low | High/Medium/Low | Implement Now |
| [Item 2] | High/Medium/Low | High/Medium/Low | High/Medium/Low | Plan for Later |
| [Item 3] | High/Medium/Low | High/Medium/Low | High/Medium/Low | Long-term |

#### Risk Warnings

| Risk Type | Description | Likelihood | Impact | Mitigation |
|-----------|-------------|------------|--------|-----------|
| [Risk 1] | [Description] | High/Medium/Low | High/Medium/Low | [Measure] |
| [Risk 2] | [Description] | High/Medium/Low | High/Medium/Low | [Measure] |

### 9. Executive Summary

#### Key Findings

1. **[Finding 1]**: One-sentence summary
2. **[Finding 2]**: One-sentence summary
3. **[Finding 3]**: One-sentence summary

#### Critical Actions

1. **[Action 1]**: Specific recommendation
2. **[Action 2]**: Specific recommendation
3. **[Action 3]**: Specific recommendation

#### Competitive Position

- **Your Product vs [Competitor A]**: Advantage/Disadvantage/Tie
- **Your Product vs [Competitor B]**: Advantage/Disadvantage/Tie
- **Your Product vs [Competitor C]**: Advantage/Disadvantage/Tie

---

**Analysis Completion Time**: [Timestamp]
**Products Analyzed**: X
**Data Source**: Amazon.com
**Analysis Method**: LLM Deep Semantic Analysis

Best Practices

1. Data Collection Optimization

Batch Processing: Maximum 10 ASINs per analysis to avoid rate limits
Delay Strategy: 3-5 second delay between ASIN requests
Retry Logic: Automatic retry, 30-second delay on failure
Caching: No duplicate collection within 24 hours for same ASIN

2. Analysis Quality Assurance

Cross-Validation: Multi-dimensional data corroboration
Outlier Detection: Identify and handle anomalous data points
Time Dimension: Consider product age effects
Market Trends: Combine with category trend analysis

3. Strategy Implementation

Priority Ranking: Rank recommendations by impact and feasibility
Resource Assessment: Define required resources for implementation
ROI Estimation: Quantify expected benefits and costs
Risk Assessment: Identify potential implementation risks

4. Compliance and Ethics

Data Use Compliance: Only for legitimate business analysis purposes
Platform Rules: Respect Amazon's Terms of Service
Data Security: Protect collected data appropriately
Information Discretion: Do not disclose sensitive business intelligence

Example Usage

User Request:

Analyze Amazon competitors, compare market landscape:
B09XYZ12345, B07ABC11111, B07DEF22222, B09JKL44444

Output:

## Competitive Analysis Report

### Analysis Overview

| Product | ASIN | Price | Rating | Reviews | Brand |
|---------|------|-------|--------|---------|-------|
| [Product A] | B09XYZ12345 | $XX.XX | X.X | X,XXX | [Brand] |
| [Product B] | B07ABC11111 | $XX.XX | X.X | X,XXX | [Brand] |
| [Product C] | B07DEF22222 | $XX.XX | X.X | X,XXX | [Brand] |
| [Product D] | B09JKL44444 | $XX.XX | X.X | X,XXX | [Brand] |

[... Complete deep analysis report ...]

User Request:

Analyze technical specification differences: B09XYZ12345, B09ABC11111

Output:

## Technical Specification Deep Comparison

### Core Parameter Matrix

| Parameter | [Product A] | [Product B] | [Product C] | Assessment |
|-----------|-------------|-------------|-------------|------------|
| [Param 1] | [Value] | [Value] | [Value] | [Analysis] |
| [Param 2] | [Value] | [Value] | [Value] | [Analysis] |

[... More analysis ...]

User Request:

Research visual presentation strategies: B09XYZ12345, B09ABC11111

Output:

## Visual Strategy Comparison

### Main Image Assessment

| Dimension | [Product A] | [Product B] |
|-----------|-------------|-------------|
| Main Image Style | [Description] | [Description] |
| Background Type | Solid/Scene | Solid/Scene |
| Visual Appeal | XX/10 | XX/10 |

[... Complete visual analysis ...]

Limitations

Data Timeliness: Amazon page data changes in real-time; analysis results have limited shelf life
Anti-Scraping: May trigger Amazon's anti-bot measures
Page Structure Changes: Amazon layout updates may invalidate selectors
Regional Limitations: Data based on specific Amazon site (e.g., US)
Review Sampling: Review analysis based on samples; may have bias
A+ Page Access: Some A+ content may require special permissions
Buy Box Data: Real-time Buy Box information may be restricted
Historical Data: Cannot directly access historical price and ranking data

Troubleshooting

Issue: API Call Failed

Solution:

Verify API key is correctly configured
Confirm account has sufficient API quota
Validate ASIN format is correct
Check network connectivity

Issue: Task Timeout

Solution:

Check MAX_WAIT_TIME setting (default 30 minutes)
Verify BrowserAct service status
Reduce number of ASINs per batch
Check template availability

Issue: Incomplete Data Extraction

Solution:

Increase wait time for page load
Check if template needs updating
Verify target page is accessible
Review API response for errors

Issue: Abnormal Analysis Results

Solution:

Confirm data collection completeness
Check for anomalous data points
Validate comparison dimensions are reasonable
Re-run analysis process

Issue: Amazon Anti-Scraping Triggered

Solution:

Reduce request frequency
Increase delay between requests
Use smaller batches
Contact BrowserAct support for rate limit adjustment

Related Skills

browseract-integration - BrowserAct API integration
web-scraper - General web scraping
data-analyzer - Data analysis tools
competitor-research - Competitor research
market-researcher - Market research tools
amazon-rank-tracker - Amazon ranking tracking

Resources

Skill Version: 1.0.0 Last Updated: 2026-02-06 Compatibility: BrowserAct API v2+ Supported Site: Amazon.com (US)

MAKE IT PART OF YOUR WORKFLOW

安装技能

TERMINAL

npx skills add kernel8901/ai-agent-skills-classification --skill amazon-competitor-analyzer

复制命令到本地终端执行；安装前请先审查源代码和许可证。

ZIP · 完整技能目录，含资源与许可证

SKILL.md · 仅技能说明原文

仓库 Star: 未同步
许可证: MIT
版本 / 分支: HEAD · c826e03e
仓库更新时间: 未同步

安装前，请检查技能脚本与权限。

源代码

amazon-competitor-analyzer

技能说明

name: amazon-competitor-analyzer description: Scrapes Amazon product data from ASINs using browseract.com automation API and performs surgical competitive analysis. Compares specifications, pricing, review quality, and visual strategies to identify competitor moats and vulnerabilities.

Amazon Competitor Analyzer

✨ Platform Compatibility

When to Use This Skill

What This Skill Does

Prerequisites

1. BrowserAct.com Account Setup

2. Environment Configuration

How to Use

Basic Competitor Analysis

Deep Specification Comparison

Review Quality Analysis

Visual Strategy Research

Complete Competitive Analysis

Instructions

1. ASIN Identification and Validation

2. BrowserAct API Implementation

3. Task Output Data Structure

4. Workflow Template Parameters

5. Error Handling

6. Rate Limiting Best Practices

Data Extraction and Structuring

LLM Deep Analysis

Best Practices

1. Data Collection Optimization

2. Analysis Quality Assurance

3. Strategy Implementation

4. Compliance and Ethics

Example Usage

Limitations

Troubleshooting

Issue: API Call Failed

Issue: Task Timeout

Issue: Incomplete Data Extraction

Issue: Abnormal Analysis Results

Issue: Amazon Anti-Scraping Triggered

Related Skills

Resources

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前端界面设计

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