| name | amazon-product-research-skill |
| description | Amazon Best Sellers category analysis for e-commerce product research and selection decisions. Activates when users analyze Amazon categories, find black horse products, research competitors, or evaluate market entry opportunities. Triggers on phrases like analyze Amazon category, find high-potential products, Amazon Best Sellers research, product selection analysis, competitor analysis, market opportunity scan. Supports macro category analysis (price tiers, brand concentration, monopoly risk), micro product analysis (potential score calculation), AI-powered product labeling (category type, form factor, material, key tags), and strategic opportunity recommendations. Uses Apify actors for data collection and AI models for intelligent analysis. Calculates potential score using (sales / (reviews + 1)) * (365 / (days_online + 1)) formula to identify high-growth, low-competition products. |
| license | MIT |
| metadata | {"author":"Agent Skill Creator","version":"1.0.0","created":"2026-03-12T00:00:00.000Z","last_reviewed":"2026-03-12T00:00:00.000Z","review_interval_days":90,"dependencies":[{"url":"https://apify.com/junglee/amazon-bestsellers","name":"Apify Amazon Bestsellers Scraper","type":"api"},{"url":"https://apify.com/axesso_data/amazon-product-details-scraper","name":"Apify Amazon Product Details Scraper","type":"api"},{"url":"https://platform.openai.com","name":"OpenAI API (for AI analysis)","type":"api"}]} |
/amazon-product-research — Amazon Best Sellers Analysis
You are an expert e-commerce product research analyst specializing in Amazon
marketplace analysis. Your job is to help sellers make data-driven product
selection decisions by analyzing Amazon Best Sellers categories.
Trigger
User invokes /amazon-product-research followed by their input:
/amazon-product-research Analyze https://www.amazon.com/Best-Sellers-Portable-Changing-Pads
/amazon-product-research Find black horse products in electronics category
/amazon-product-research Research competitor strategy for ASIN B09B8V1LZ3
/amazon-product-research Generate market entry report for baby products
/amazon-product-research Weekly scan of kitchen gadgets category
When to Use This Skill
- Category Analysis: Evaluate an entire Amazon Best Sellers category for
market entry decisions
- Black Horse Discovery: Find high-potential, low-competition products
- Competitor Research: Analyze competitor product strategies and positioning
- Investment Decisions: Get AI-powered strategic recommendations
- Weekly Monitoring: Track category changes and emerging products
Data Sources
This skill uses Apify Actors for data collection:
- junglee/amazon-bestsellers: Scrapes Best Seller rankings (ASINs, rankings,
basic info) from category pages
- axesso_data/amazon-product-details-scraper: Scrapes detailed product
information for each ASIN (price, reviews, ratings, features, seller info)
Prerequisites:
- Apify API token (get free tier at https://apify.com)
- OpenAI API key for AI analysis (optional, for enhanced recommendations)
Workflows
Workflow 1: Complete Category Analysis
Analyze an entire Amazon Best Sellers category end-to-end:
export APIFY_API_TOKEN="your_apify_token"
export OPENAI_API_KEY="your_openai_key"
python3 scripts/analyze_category.py \
--category-url "https://www.amazon.com/Best-Sellers-Portable-Changing-Pads" \
--output-format markdown \
--include-ai-analysis
What it does:
- Scrapes Best Sellers list (up to 100 products)
- Collects detailed product information
- Performs macro analysis (price tiers, brand share, monopoly risk)
- Calculates potential scores for each product
- Identifies black horse candidates
- Generates comprehensive report
Workflow 2: Black Horse Discovery
Find high-potential products with low competition:
python3 scripts/find_black_horses.py \
--category-url "https://www.amazon.com/Best-Sellers-Electronics/zgbs/electronics" \
--min-potential-score 1.0 \
--max-reviews 100 \
--output black_horses.json
Black Horse Criteria (configurable):
- Potential Score >= 1.0
- Reviews <= 100
- Days online < 180 (new products)
Workflow 3: AI-Powered Opportunity Analysis
Get strategic recommendations from AI:
python3 scripts/ai_opportunity_analysis.py \
--input-file category_data.json \
--analysis-type comprehensive \
--output report.md
Analysis Types:
label-extraction: Extract category_type, form_factor, material_core, key_tagsbasic-analysis: Category macro report + product audit by tagsopportunity-analysis: Strategic recommendations with investment advicecomprehensive: All analyses combined
Workflow 4: Quick Category Scan
Fast overview without detailed scraping:
python3 scripts/quick_scan.py \
--category-url "https://www.amazon.com/Best-Sellers-Cell-Phones/zgbs/wireless" \
--limit 50
Available Scripts
| Script | Purpose | Inputs | Outputs |
|---|
analyze_category.py | Complete end-to-end analysis | Category URL | Full report (JSON/Markdown) |
find_black_horses.py | Find high-potential products | Category URL | Black horse list |
ai_opportunity_analysis.py | AI-powered recommendations | Product data | Strategic report |
quick_scan.py | Fast category overview | Category URL | Summary stats |
fetch_bestsellers.py | Raw data collection | Category URL | Raw JSON data |
calculate_metrics.py | Metric calculations | Product data | Scored products |
Available Analyses
Macro Analysis (Category Overview)
Metrics Calculated:
- Price Tiers: Average prices for Top 20, 21-50, 51-100 rankings
- Brand Analysis: Brand occurrence counts, market share calculations
- Seller Structure: Amazon Direct vs FBA vs FBM distribution
- Monopoly Risk: Top 5 brand concentration percentage (CR5)
- Amazon Competition: Amazon Direct products percentage
Interpretation:
- Monopoly Risk > 60%: HIGH - Difficult for new entrants
- Monopoly Risk 40-60%: MODERATE - Possible with differentiation
- Monopoly Risk < 40%: LOW - Favorable for new entrants
- Amazon Direct > 20%: SEVERE competition from Amazon
Micro Analysis (Product-Level)
Potential Score Formula:
Potential Score = (monthly_sales / (review_count + 1)) * (365 / (days_online + 1))
Logic:
- Higher sales = higher score (market demand)
- Lower reviews = higher score (less competition)
- Fewer days online = higher score (new product opportunity)
Black Horse Thresholds:
- Potential Score >= 1.0
- Reviews <= 100
- Days online < 180
AI Label Extraction
Extracts standardized product attributes:
- category_type: Product type (e.g., "Diaper Pad", "Earbuds")
- form_factor: Physical structure (e.g., "Foldable Mat", "Cylindrical Box")
- material_core: Main material (e.g., "PU Leather", "Silicone")
- key_tags: 3 core selling points (e.g., ["Waterproof", "Wireless", "Non-slip"])
AI Opportunity Analysis
Strategic recommendations including:
- Premium Ceiling Analysis: What drives higher pricing
- Winning DNA Formula: Recommended product characteristics
- Giant Blind Spots: Opportunities big brands miss
- Investment Recommendation: Price range and competitive advantage strategy
Error Handling
| Error | Cause | Solution |
|---|
ApifyAuthError | Invalid API token | Check APIFY_API_TOKEN env var |
RateLimitError | Too many requests | Wait and retry; check Apify dashboard |
NoProductsError | Empty category or blocking | Try different category or proxy |
AIAnalysisError | OpenAI API failure | Check OPENAI_API_KEY; retry without AI |
DataValidationError | Missing required fields | Check input data format |
Keywords for Automatic Detection
Entities: Amazon, Best Sellers, ASIN, category, product, brand, seller, FBA, FBM
Metrics: potential score, monopoly risk, market share, price tier, BSR (Best Seller Rank), reviews, rating, sales volume
Actions: analyze, research, scan, discover, find, compare, evaluate, monitor
Geography: amazon.com, amazon.co.uk, amazon.de, amazon.co.jp, amazon.ca
Activation examples:
- "Analyze this Amazon category for me"
- "Find black horse products in electronics"
- "Research Amazon Best Sellers for baby products"
- "What's the monopoly risk in this category?"
- "Generate a product selection report"
Does NOT activate for:
- General e-commerce questions (not Amazon-specific)
- Product listing optimization
- Amazon advertising analysis
- Inventory management
Usage Examples
Example 1: Category Entry Decision
User: "Should I enter the portable changing pads market?"
Flow:
- Skill scrapes https://www.amazon.com/Best-Sellers-Portable-Changing-Pads
- Calculates monopoly risk (e.g., 45% - MODERATE)
- Identifies 5 black horse candidates
- AI recommends: "Enter at $25-35 price point with waterproof silicone material"
Output: Comprehensive report with go/no-go recommendation
Example 2: Black Horse Discovery
User: "Find me high-potential products with low competition"
Flow:
- User provides category URL
- Skill calculates potential scores for all products
- Filters for score >= 1.0 and reviews <= 100
- Returns ranked list with ASINs and analysis
Output: JSON/Markdown list of black horse products
Example 3: Competitor Analysis
User: "Analyze the top 10 products in this category"
Flow:
- Scrapes top 10 Best Sellers
- Extracts brand distribution
- Analyzes price positioning
- Identifies feature gaps
Output: Competitor positioning map
Example 4: Weekly Monitoring
User: "Weekly scan of kitchen gadgets category"
Flow:
- Scheduled execution (via cron or similar)
- Compares with previous week's data
- Identifies new entrants and ranking changes
- Sends alert if new black horses appear
Output: Change report with new opportunities
References
| File | Content |
|---|
references/api-guide.md | Apify API setup, authentication, rate limits |
references/analysis-methods.md | Detailed formulas and methodologies |
references/troubleshooting.md | Common issues and solutions |
assets/config.json | Default configuration and thresholds |
Configuration
Edit assets/config.json to customize:
- Scraping limits: max products per category
- Black horse thresholds: potential score, review count
- AI models: which model to use for analysis
- Output formats: default report format
- Rate limiting: request delays and concurrency
