Clawdbot结合Pangolinfo API自动生成竞品分析报告的工作流程图

How to Use Clawdbot for Competitor Monitoring with Pangolinfo API Integration

Clawdbot Competitor Analysis: The Right Way to Automate

Clawdbot competitor analysis has become essential for e-commerce sellers, but most people are doing it wrong. This Clawdbot automation tutorial will show you how to use Clawdbot for competitor monitoring effectively by combining it with Pangolinfo API integration.

Clawdbot (Claude Computer Use) has taken the tech world by storm. Everyone’s using it to automate tasks, but I’ve discovered a critical problem with traditional Clawdbot e-commerce integrationit’s incredibly inefficient at fetching competitor data.

Here’s a real scenario: You’re an Amazon seller who wants to receive a daily competitor report at 8 AM covering price changes, inventory status, and review updates. What happens if you let Clawdbot scrape the data itself?

  • ❌ Painfully slow: Opens browser, visits pages one by one, might take until afternoon
  • ❌ Unreliable: E-commerce platforms have strict anti-scraping measures, Clawdbot gets blocked easily
  • ❌ Expensive: Clawdbot charges by usage time, longer scraping = higher costs
  • ❌ Inaccurate data: Incomplete page loads, failed dynamic content, poor data quality

I tested it: letting Clawdbot scrape 10 competitors took 25-30 minutes with only 60% success rate.

But with a different approach—using an API to feed data to Clawdbot—everything changes:

  • ✅ 5 minutes total: API handles data collection, Clawdbot focuses on analysis
  • ✅ 100% success rate: Professional data service, stable and reliable
  • ✅ 70% cost reduction: Less Clawdbot usage time
  • ✅ Accurate data: Real scraping, not estimates

Today, I’ll show you exactly how to combine Clawdbot with Pangolinfo API to build a truly automated competitor monitoring system.

Clawdbot Competitor Analysis Architecture: Automated Competitor Tracking

Before diving into this Clawdbot API integration guide, let’s understand the workflow for effective automated competitor tracking with Clawdbot. The core principle is simple: API handles data collection, Clawdbot handles competitor analysis.

Workflow


Scheduled Task Triggers (Daily at 8 AM)
    ↓
Pangolinfo API Batch Scrapes Competitor Data
(Price, Inventory, Ranking, Reviews, Ads, etc.)
    ↓
Data Passed to Clawdbot
    ↓
Clawdbot Analyzes Data
(Price changes, inventory alerts, rating anomalies, pricing recommendations)
    ↓
Generates Structured Report
    ↓
Auto-sends to Slack/Email
                

Why This Design?

1. API Excels at Data Collection

Pangolinfo API is specifically designed for e-commerce data scraping with these advantages:

  • 98% collection success rate (especially for Amazon SP ad positions)
  • Supports batch collection—10 products in 30 seconds with concurrent requests
  • Global marketplace support (US, UK, Germany, Japan, etc.)
  • Returns structured JSON data, ready to use

2. Clawdbot Excels at Understanding and Expression

Clawdbot’s strengths are:

  • Understanding complex data relationships
  • Discovering anomalies and trends
  • Generating human-readable analysis reports
  • Providing context-based recommendations

Clear division of labor, maximum efficiency. It’s like cooking: the API is the supermarket (providing ingredients), Clawdbot is the chef (cooking the meal). You wouldn’t ask the chef to grow vegetables, nor would you ask the supermarket to cook for you, right?

Clawdbot Automation Tutorial: Step-by-Step Implementation

Step 1: Preparation

1. Register for Pangolinfo and Get API Key

  1. Visit Pangolinfo Scrape API
  2. Register and log in
  3. Create an API Key in the dashboard
  4. Save your API Key (looks like: pk_live_xxxxxxxxxxxxxxxx)

2. Create a New Skill/Workflow in Clawdbot

If you’re using Claude Desktop or API, you’ll need to create a new workflow. We’ll provide complete prompt templates later.

3. Identify Competitors to Monitor

List the ASINs (Amazon product IDs) you want to monitor. Start with 5-10 core competitors:


Competitor List Example:
- B08N5WRWNW (Main Competitor 1)
- B07XYZ1234 (Main Competitor 2)
- B09ABC5678 (Emerging Threat)
- B06DEF9012 (Price Competitor)
- B08GHI3456 (Ranking Benchmark)
                

Step 2: API Data Collection

This is the core of the system. We use Pangolinfo API to batch fetch competitor data.

Complete Code Example (JavaScript/Node.js):


// competitor-scraper.js
const fetch = require('node-fetch');

// Configuration
const API_KEY = 'your_pangolinfo_api_key';
const API_BASE_URL = 'https://api.pangolinfo.com/v1';

// Competitor ASIN list
const COMPETITOR_ASINS = [
    'B08N5WRWNW',
    'B07XYZ1234',
    'B09ABC5678',
    'B06DEF9012',
    'B08GHI3456'
];

/**
 * Fetch single product data
 */
async function fetchProductData(asin, marketplace = 'US') {
    const endpoint = `${API_BASE_URL}/scrape/amazon/product`;
    
    const response = await fetch(endpoint, {
        method: 'POST',
        headers: {
            'Authorization': `Bearer ${API_KEY}`,
            'Content-Type': 'application/json'
        },
        body: JSON.stringify({
            asin: asin,
            marketplace: marketplace,
            fields: [
                'price',           // Price info
                'rating',          // Rating
                'reviews_count',   // Review count
                'inventory',       // Stock status
                'bsr',             // Best Sellers Rank
                'images',          // Images
                'title',           // Title
                'sponsored'        // Is sponsored
            ]
        })
    });
    
    if (!response.ok) {
        throw new Error(`API request failed: ${response.statusText}`);
    }
    
    return await response.json();
}

/**
 * Batch fetch all competitors
 */
async function fetchAllCompetitors() {
    console.log(`Starting to scrape ${COMPETITOR_ASINS.length} competitors...`);
    
    const promises = COMPETITOR_ASINS.map(asin => 
        fetchProductData(asin).catch(err => {
            console.error(`Failed to scrape ${asin}:`, err.message);
            return null;
        })
    );
    
    const results = await Promise.all(promises);
    
    // Filter out failed requests
    const validResults = results.filter(r => r !== null);
    
    console.log(`Successfully scraped ${validResults.length}/${COMPETITOR_ASINS.length} competitors`);
    
    return validResults;
}

/**
 * Format data for Clawdbot analysis
 */
function formatForClawdbot(products) {
    return products.map(p => ({
        asin: p.asin,
        title: p.title,
        currentPrice: p.price?.value || 'N/A',
        listPrice: p.list_price?.value || 'N/A',
        discount: p.price?.discount_percentage || 0,
        rating: p.rating || 'N/A',
        reviewCount: p.reviews_count || 0,
        inStock: p.availability?.in_stock || false,
        stockLevel: p.availability?.message || 'Unknown',
        bsr: p.best_sellers_rank?.[0]?.rank || 'N/A',
        isSponsored: p.is_sponsored || false
    }));
}

/**
 * Main function
 */
async function main() {
    try {
        // 1. Fetch data
        const rawData = await fetchAllCompetitors();
        
        // 2. Format data
        const formattedData = formatForClawdbot(rawData);
        
        // 3. Save to file (for Clawdbot to read)
        const fs = require('fs');
        fs.writeFileSync(
            'competitor-data.json',
            JSON.stringify(formattedData, null, 2)
        );
        
        console.log('Data saved to competitor-data.json');
        console.log('Ready to pass to Clawdbot for analysis');
        
        return formattedData;
        
    } catch (error) {
        console.error('Execution failed:', error);
        process.exit(1);
    }
}

// Execute
main();
                

Returned Data Structure Example:


[
  {
    "asin": "B08N5WRWNW",
    "title": "Wireless Bluetooth Earbuds with Charging Case",
    "currentPrice": 29.99,
    "listPrice": 49.99,
    "discount": 40,
    "rating": 4.5,
    "reviewCount": 1234,
    "inStock": true,
    "stockLevel": "In Stock",
    "bsr": 1523,
    "isSponsored": true
  }
]
                

Step 3: Pass Data to Clawdbot

There are two methods to pass data to Clawdbot:

Method 1: File Transfer (Simple)

  1. Run the script above to generate competitor-data.json
  2. Have Clawdbot read this file
  3. Let Clawdbot analyze the data

Method 2: Direct API Call (Recommended, More Automated)

Call Pangolinfo API directly within Clawdbot’s skill to get real-time latest data.

Step 4: Generate Reports with Clawdbot

Now the data is ready. We need to design a good prompt to have Clawdbot generate valuable analysis reports.

Prompt Design Example:


You are a professional e-commerce data analyst. You've received the latest data for 5 competitors, including price, rating, inventory status, BSR ranking, etc.

The data is saved in the competitor-data.json file.

Please perform the following analysis:

1. **Price Analysis**
   - Which competitors lowered prices? By how much?
   - Which are running promotions (discount >30%)?
   - Calculate average price and provide our pricing recommendation

2. **Rating Analysis**
   - Any competitors with abnormal rating changes?
   - Which has the fastest review count growth? (might be pushing volume)
   - Identify competitors with ratings below 4.0 (potential quality issues)

3. **Inventory Alerts**
   - Which competitors have low stock ("Only X left")?
   - Which are out of stock?
   - This is our opportunity to capture market share

4. **Ranking Analysis**
   - Which has the best BSR ranking?
   - Which are running ads (isSponsored=true)?

5. **Comprehensive Recommendations**
   - Based on the above analysis, provide 3 specific operational recommendations
   - Mark priority (High/Medium/Low)

**Output Format Requirements:**
- Use Markdown format
- Display key data in tables
- Use emojis for readability
- Summarize core findings in 3 sentences

**Delivery Method:**
After generating the report, send it to the #competitor-alerts Slack channel
                

Clawdbot Generated Report Example:


# 🎯 Daily Competitor Analysis - 2026-01-27

## 📊 Key Findings

| Metric | Value | Change |
|--------|-------|--------|
| Monitored Competitors | 5 | - |
| Price Anomalies | 2 | ⚠️ |
| Stock Alerts | 1 | 🚨 |
| Running Ads | 3 | 📈 |

---

## 💰 Price Analysis

### Price Drops
1. **B08N5WRWNW** - Wireless Bluetooth Earbuds
   - Current Price: $29.99
   - List Price: $49.99
   - Discount: **40%** ⚠️ Major promotion
   
2. **B07XYZ1234** - Premium Noise Cancelling Earphones
   - Current Price: $39.99
   - List Price: $59.99
   - Discount: 33%

### Pricing Recommendation
- Market Average: $34.50
- Our Recommended Price: **$32.99-$36.99**
- Rationale: Slightly below average to stay competitive while maintaining margins

---

## ⭐ Rating Analysis

| ASIN | Rating | Review Count | Trend |
|------|--------|--------------|-------|
| B07XYZ1234 | 4.7 | 2,156 | 📈 Fastest growth |
| B08N5WRWNW | 4.5 | 1,234 | ➡️ Stable |
| B09ABC5678 | 4.2 | 856 | ⚠️ Below average |

**Key Finding:**
- B07XYZ1234 has fastest review growth, might be pushing volume, monitor closely
- B09ABC5678 rating 4.2, below industry average, possible quality issues

---

## 📦 Inventory Alerts

🚨 **B07XYZ1234** - Low Stock
- Status: "Only 5 left in stock"
- **Opportunity**: Perfect time to capture market share!
- **Recommendation**: Immediately increase ad spend by 30%

---

## 🎯 Operational Recommendations

### High Priority 🔴
1. **Capture B07XYZ1234's Stock-Out Opportunity**
   - Competitor has only 5 units left, increase ad budget by 30% now
   - Estimated additional 50-80 orders

### Medium Priority 🟡
2. **Adjust Pricing Strategy**
   - If current price above $37, recommend lowering to $32.99-$36.99 range
   - Stay competitive with B08N5WRWNW's promotional price

3. **Strengthen Ad Presence**
   - 3 main competitors running ads, we need to maintain visibility
   - Focus on keywords where top BSR competitors rank well

---

## 📝 Three-Sentence Summary

1. **B07XYZ1234 stock critical**, golden opportunity to capture market, recommend immediate ad increase
2. **Intense price competition**, B08N5WRWNW running 40% promotion, need pricing adjustment to stay competitive
3. **Ad war escalating**, 60% of competitors advertising, we need to strengthen ad strategy

---

*Report Generated: 2026-01-27 08:00:00*  
*Data Source: Pangolinfo API*  
*Next Update: 2026-01-28 08:00:00*
                

Step 5: Automation Setup

Final step: make the entire process run automatically.

Method 1: Cron Scheduled Tasks (Linux/Mac)


# Edit crontab
crontab -e

# Add scheduled task (daily at 8 AM)
0 8 * * * cd /path/to/your/project && node competitor-scraper.js && clawdbot run analyze-competitors
                

Method 2: GitHub Actions (Recommended, Cloud-Based)


# .github/workflows/competitor-analysis.yml
name: Daily Competitor Analysis

on:
  schedule:
    # Daily at UTC 00:00 (8 AM Beijing time)
    - cron: '0 0 * * *'
  workflow_dispatch:  # Support manual trigger

jobs:
  analyze:
    runs-on: ubuntu-latest
    
    steps:
      - name: Checkout code
        uses: actions/checkout@v3
      
      - name: Setup Node.js
        uses: actions/setup-node@v3
        with:
          node-version: '18'
      
      - name: Install dependencies
        run: npm install
      
      - name: Fetch competitor data
        env:
          PANGOLINFO_API_KEY: ${{ secrets.PANGOLINFO_API_KEY }}
        run: node competitor-scraper.js
      
      - name: Run Clawdbot analysis
        env:
          ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
          SLACK_WEBHOOK: ${{ secrets.SLACK_WEBHOOK }}
        run: node clawdbot-analyze.js
      
      - name: Send to Slack
        if: success()
        run: |
          curl -X POST ${{ secrets.SLACK_WEBHOOK }} \
            -H 'Content-Type: application/json' \
            -d @report.json
                

Clawdbot Competitor Analysis ROI: Cost and Efficiency

Many ask: Is building this system worth it? Let’s do the math.

Traditional Approach Costs

TaskTimeFrequencyMonthly Total
Manual competitor checking30 min/dayDaily15 hours
Data organization to Excel15 min/dayDaily7.5 hours
Generate analysis report20 min/dayDaily10 hours
Total32.5 hours/month

Labor Cost: At $20/hour for operations staff, monthly cost = 32.5 hours × $20 = $650

Automated Solution Costs

ItemCostNotes
Initial setup2 hoursOne-time investment
Pangolinfo API$30-50/monthPay-as-you-go
Clawdbot usage$20/monthClaude Pro subscription
Server/GitHub Actions$0-10/monthFree tier sufficient
Manual review5 min/dayJust check reports
Monthly Total$50-80Save $570-600

Return on Investment (ROI)

  • First Month Investment: $50-80 (tool costs) + 2 hours (setup time)
  • Monthly Savings: $570-600 (labor costs) + 27.5 hours (time)
  • Payback PeriodImmediate ROI (profitable from month one)
  • Annual Savings: Approximately $6,840 + 330 hours

Efficiency Improvement Comparison:

MetricTraditionalAutomatedImprovement
Data Collection Time30 minutes5 minutes83% ⬆️
Data Accuracy70%98%40% ⬆️
Report Generation20 minutes2 minutes90% ⬆️
Anomaly Response24 hoursReal-timeInstant ⚡

Conclusion: The automated solution not only saves costs but more importantly improves decision speed and quality. In e-commerce competition, discovering competitor changes 1 hour earlier could mean capturing dozens more orders.

Mastering Clawdbot Competitor Analysis: Key Takeaways

Through this Clawdbot automation tutorial, you’ve learned how to use Clawdbot for competitor monitoring effectively. By combining Clawdbot competitor analysis capabilities with Pangolinfo API, you’ve built an efficient automated competitor tracking system.

Key Takeaways:

  1. ✅ Clear Division of Labor: API handles data collection, Clawdbot handles analysis
  2. ✅ Efficiency Boost: From 30 minutes to 5 minutes (83% improvement)
  3. ✅ Cost Savings: Save $570-600 monthly, $6,840 annually
  4. ✅ Fully Automated: Scheduled execution, anomaly alerts, no manual intervention needed

Who Should Use This

  • 🎯 Amazon/Cross-border e-commerce sellers
  • 🎯 Competitor analysis teams
  • 🎯 Operations staff who don’t want to monitor constantly
  • 🎯 Technical teams wanting efficiency improvements

Next Steps

1. Register for Pangolinfo Trial

Visit Pangolinfo Scrape API, register and get your API Key. New users get free credits to test effectiveness.

2. Download Complete Code Examples

I’ve uploaded complete code to GitHub: https://github.com/Pangolin-spg/clawdbot-competitor-monitor

Includes:

  • Data collection scripts (JavaScript/Python)
  • Clawdbot Skill templates
  • GitHub Actions configuration
  • Database schema
  • Complete documentation

3. Check API Documentation

Detailed API usage instructions: Pangolinfo API Documentation

Final Thoughts

The value of Clawdbot e-commerce integration isn’t just what it can do, but how you make it work for you. This Clawdbot API integration guide has shown you that proper Clawdbot competitor analysis requires the right architecture—feeding it quality data rather than making it do everything.

Through proper architecture design—letting the API handle data collection and Clawdbot handle analysis and decision-making—you can build a truly intelligent, efficient automated system.

Start taking action now! Begin by monitoring 5 competitors, gradually optimize, and you’ll discover this system delivers value far beyond expectations.

Ready to start automated competitor monitoring? Visit Pangolinfo Scrape API to register for a trial, or check the complete API documentation for more technical details.

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