Skip to main content Accueil Créateurs aaaaqwq agi-super-team ecommerce-competitor-analyzer
ecommerce-competitor-analyzer Multi-platform e-commerce competitor analysis skill that automatically scrapes product data from Amazon, Temu, Shopee and generates comprehensive analysis reports using AI. Use when you need to analyze competitor products, extract product insights, or batch analyze multiple product listings. Supports bulk processing with structured outputs including title, price, rating, reviews, and strategic analysis.
Aller à l'installation Skills Marketplace Découvrez et explorez les compétences IA créées par la communauté.
Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.
Afficher les détails du prompt Une commande directe contourne le prompt de vérification. Examinez la source avant de l'exécuter.
npx skills add https://github.com/aAAaqwq/AGI-Super-Team --skill ecommerce-competitor-analyzerLa commande reste sur une seule ligne. Faites défiler horizontalement pour la vérifier avant de la copier.
Vous préférez une copie locale ? Téléchargez les fichiers actuellement disponibles dans SkillsMP.
Télécharger Zip Téléchargement... Métiers associés SOC
Basé sur la classification professionnelle SOC
Explorateur de fichiers
26 fichiers name ecommerce-competitor-analyzer description Multi-platform e-commerce competitor analysis skill that automatically scrapes product data from Amazon, Temu, Shopee and generates comprehensive analysis reports using AI. Use when you need to analyze competitor products, extract product insights, or batch analyze multiple product listings. Supports bulk processing with structured outputs including title, price, rating, reviews, and strategic analysis. version 1.0.0 author Buluslan@新西楼Newest AI globs ["*.md","platforms.yaml","scripts/*.js","prompts/*.md"]
E-commerce Competitor Analyzer Skill
Quick Start (For AI)
When to use this skill : When user asks to analyze, research, or extract insights from e-commerce products (Amazon, Temu, Shopee).
What you should do :
Extract product identifiers (ASINs or URLs) from user input
Call the scraper script to get product data
Call the AI analysis with the analysis prompt template
Output results in BOTH formats: Google Sheets + Markdown
Input examples :
"Analyze B0C4YT8S6H"
"Analyze these products: B0C4YT8S6H, B08N5WRQ1Y, B0CLFH7CCV"
Google Sheets table with: ASIN, Title, Price, Rating, 4 analysis summaries
Markdown report with detailed 4-dimensional analysis
How AI Should Process Requests
Step 1: Extract Product Identifiers From user input, extract all ASINs and/or URLs:
"Analyze these Amazon products:
B0C4YT8S6H
B08N5WRQ1Y
B0CLFH7CCV"
Extract : ['B0C4YT8S6H', 'B08N5WRQ1Y', 'B0CLFH7CCV']
"Analyze B0C4YT8S6H and https://amazon.com/dp/B08N5WRQ1Y"
Extract : ['B0C4YT8S6H', 'B08N5WRQ1Y'] (extract ASIN from URL)
Step 2: Batch Scrape Product Data For each product identifier:
Detect platform (use scripts/detect-platform.js if available)
Call appropriate scraper (Amazon: scripts/scrape-amazon.js)
Use Olostep API with configured API key from .env
Batch processing pattern :
const products = ['B0C4YT8S6H' , 'B08N5WRQ1Y' , 'B0CLFH7CCV' ];
const results = await Promise .allSettled (
products.map (asin =>scrapeAmazon (asin))
);
const successful = results.filter (r =>status === 'fulfilled' );
const failed = results.filter (r =>status === 'rejected' );
Step 3: Batch AI Analysis For each successfully scraped product:
Read the analysis prompt from prompts/analysis-prompt-base.md
Replace product data placeholders in the prompt
Call Gemini API (model: gemini-3-flash-preview)
Extract structured analysis results
Analysis framework (4 dimensions):
文案构建逻辑与词频分析 (The Brain) - Copywriting strategy & keywords视觉资产设计思路 (The Face) - Visual design methodology评论定量与定性分析 (The Voice) - Review sentiment analysis市场维态与盲区扫描 (The Pulse) - Market positioning & blind spots
Step 4: Generate Dual Format Output Format 1: Google Sheets (Structured Data)
Write to Google Sheets with columns:
| ASIN | 产品标题 | 价格 | 评分 | 文案分析摘要 | 视觉分析摘要 | 评论分析摘要 | 市场分析摘要 |
Sheet selection priority :
User explicitly specified Sheet ID/Name/URL
Default from .env (GOOGLE_SHEETS_ID)
Ask user to provide Sheet ID
Format 2: Markdown Report (Detailed Analysis)
Generate file: 竞品分析-YYYY-MM-DD.md
# Amazon Competitor Analysis Report
## Analysis Overview
- Products analyzed: 3
- Analysis date: 2026-01-29
- Total time: ~5 minutes
---
## Product 1: B0C4YT8S6H
### Basic Information
- Title: [Product title]
- Price: [Price]
- Rating: [Rating]
### Copywriting Strategy & Keyword Analysis
[Full analysis...]
### Visual Asset Design Methodology
[Full analysis...]
### Customer Review Analysis
[Full analysis...]
### Market Positioning & Competitive Intelligence
[Full analysis...]
---
File Structure ecommerce-competitor-analyzer.skill/
├── SKILL.md # This file (AI instructions)
├── platforms.yaml # Platform configurations (URL patterns, regex)
├── .env.example # Configuration template (API keys)
├── prompts/ # AI prompt templates
│ └── analysis-prompt-base.md # Base analysis framework (from n8n)
├── scripts/ # Processing scripts
│ ├── detect-platform.js # Platform detection utility
│ ├── scrape-amazon.js # Amazon scraper (Olostep API)
│ └── batch-processor.js # Batch processing engine
└── references/ # Documentation
└── n8n-workflow-analysis.md # n8n workflow insights
Configuration Files
platforms.yaml Contains platform-specific configurations:
URL patterns for platform detection
ASIN extraction regex patterns
Scraper API endpoints
Data extraction patterns
platforms:
amazon:
url_patterns: ["amazon.com" , "amazon.co.uk" , ... ]
asin_regex:
standard: "/dp/([A-Z0-9]{10})"
scraper:
provider: "olostep"
api_endpoint: "https://api.olostep.com/v2/agent/web-agent"
.env.example Template for required API keys:
OLOSTEP_API_KEY=your_olostep_api_key_here
GEMINI_API_KEY=your_gemini_api_key_here
GOOGLE_SHEETS_ID=YOUR_GOOGLE_SHEETS_ID_HERE
Critical : Always check if .env file exists and contains required keys before processing.
Analysis Prompt Template The AI analysis uses a proven 4-dimensional framework. The exact prompt is stored in:
prompts/analysis-prompt-base.md
Role : 10-year experienced Amazon Operations Director & Brand StrategistGoal : Deep scan of product listing to extract strategic insightsOutput Structure :
Part 1: 文案构建逻辑与词频分析
Part 2: 视觉资产设计思路
Part 3: 评论定量与定性分析
Part 4: 市场维态与盲区扫描
Important : Use the prompt EXACTLY as provided in the template without modifications.
API Services
Olostep API (Web Scraping)
Purpose : Scrape Amazon product pages with rendered JavaScriptEndpoint : https://api.olostep.com/v2/agent/web-agentCost : 1000 free requests/month, then $0.002/requestKey param : comments_to_scrape: 100 (matching n8n config)
Google Gemini API (AI Analysis)
Purpose : Generate comprehensive product analysisModel : gemini-3-flash-preview (cost-effective)Cost : ~$0.001/productAlternative : gemini-2-flash-thinking (for complex analysis)
Google Sheets API (Data Storage)
Purpose : Export structured resultsAuthentication : OAuth2 service accountCost : Free tier
Error Handling
Batch Processing with Error Isolation Critical pattern from n8n workflow :
const items = productIdentifiers;
const results = await Promise .allSettled (
items.map (async (item, index) => {
try {
const data = await scrapeProduct (item);
const analysis = await analyzeWithAI (data);
return { success : true , index, data : analysis };
} catch (error) {
return { success : false , index, error : error.message };
}
})
);
const successful = results.filter (r =>status === 'fulfilled' && r.value .success );
const failed = results.filter (r =>status === 'rejected' || !r.value .success );
console .log (`Processed: ${successful.length} succeeded, ${failed.length} failed` );
Common Errors & Solutions Error Cause Solution OLOSTEP_API_KEY not foundMissing .env file Check .env exists and contains key Invalid ASIN formatMalformed ASIN Validate ASIN: 10 alphanumeric chars Scraping timeoutSlow page load Increase timeout or retry Gemini rate limitToo many requests Add delay between batches
Platform Detection Logic function detectPlatform (urlOrId ) {
if (/^[A-Z0-9]{10}$/ .test (urlOrId)) {
return { platform : 'amazon' , id : urlOrId };
}
if (/amazon\.(com|co\.uk|de|es|fr|it|ca|co\.jp)/i .test (urlOrId)) {
const asinMatch = urlOrId.match (/\/dp\/([A-Z0-9]{10})/i );
if (asinMatch) {
return { platform : 'amazon' , id : asinMatch[1 ] };
}
}
return null ;
}
Implementation Notes
Current Version: Phase 1 MVP Supported Platforms : Amazon (US only)
Input Method : Dialog-based (ASINs or URLs)
Output Format : Google Sheets table + Markdown report
Roadmap
✅ Phase 1: Amazon MVP (current)
🔄 Phase 2: Add Temu & Shopee platforms
🔄 Phase 3: Cross-platform comparison
🔄 Phase 4: Historical tracking & price alerts
Design Philosophy This skill follows the error isolation pattern from the n8n workflow:
Single product failure NEVER stops the entire batch
Always report both successes and failures
Provide detailed error messages for debugging
Performance Benchmarks Operation Time Cost Single product scrape ~30 seconds $0.002 (Olostep) Single product analysis ~45 seconds $0.001 (Gemini) Total per product ~1-2 minutes ~$0.003 Batch of 10 products ~10-15 minutes (parallel) ~$0.03
References
n8n Workflow : Based on v81 workflow logicPlatform Config : See platforms.yaml for URL patterns and extraction rulesAnalysis Prompt : See prompts/analysis-prompt-base.md for exact prompt template
Important Reminders for AI
ALWAYS extract ALL product identifiers from user input before processingALWAYS use batch processing with Promise.allSettled for error isolationALWAYS generate BOTH output formats : Google Sheets + MarkdownNEVER modify the analysis prompt - use it exactly as providedALWAYS validate .env exists before starting processingALWAYS report processing summary : X succeeded, Y failedIf Google Sheets ID is missing , ask user to provide itUse the exact prompt from prompts/analysis-prompt-base.md without any modifications 