IMPORTANT — Working Directory: All scripts in this skill MUST be executed with the working directory set to the absolute path of the directory containing this SKILL.md file (the skill root directory). Always cd into this directory before running any command. Do NOT use relative paths or shell shortcuts like ~ in paths, as they may not be resolved by the execution environment.
Product Validator v1.4.0
Product Understanding Step
Before running this skill, if the user provides a product, image, ASIN, listing, product ID, or product idea, first identify:
- product type/category
- main use case
- key physical, design, or functional features
- target marketplace, country, or platform
- user's analysis goal
Use this product understanding to choose parameters, filters, competitors, regions, keywords, or analysis dimensions before executing the script.
Is this product data reliable? Should I trust these numbers?
Validate product data reliability with 10-step analysis including profitability calculation.
Core Question
— Is this product data reliable?
— Can I make money on this?
When to Use
- Validating a product before sourcing
- Checking if BSR/sales estimates are realistic
- Calculating profitability and ROI
- Identifying fake reviews or manipulated data
Differs From / Not Applicable
- Use product-opportunity-finder to discover products.
- Use competitor-analyzer to compare competing ASINs.
- Use price-history-analyzer for deep price history.
- Use this skill for validating a known product or ASIN before sourcing.
Data Sources
| Source |
Endpoint |
Data |
| Keepa |
/api/v1/tools/linkfox/keepa/productRequest |
Product details, title, price, reviews, BSR |
| Keepa |
/api/v1/tools/linkfox/keepa/productSeries |
BSR history, price history (180-day) |
| Jungle Scout |
/api/v1/tools/jungle-scout/keywords/by-keyword |
Category benchmarks (avg search volume, difficulty) |
10-Step Validation Flow
| Step |
Analysis |
Output |
| 1️⃣ |
Data Extraction |
Raw product data |
| 2️⃣ |
BSR History |
Stability, seasonality |
| 3️⃣ |
Price History |
Volatility, trends |
| 4️⃣ |
Buy Box Analysis |
Stability, competition |
| 5️⃣ |
Seller History |
Seller count, types |
| 6️⃣ |
Review Scoring |
Authenticity check |
| 7️⃣ |
Stock Status |
Availability patterns |
| 8️⃣ |
Seasonality |
Peak/trough timing |
| 9️⃣ |
Score Calculation |
Composite score |
| 🔟 |
Risk Assessment |
Final recommendation |
Validation Score (0-100)
| Score |
Level |
Action |
| ≥80 |
✅ VALID |
Proceed with confidence |
| 60-79 |
⚠️ CAUTION |
Verify specific concerns |
| <60 |
🔴 AVOID |
High risk, do not proceed |
⚠️ MANDATORY: Charts & Display Rules
- Generate charts by default for full analytical reports — use
--chart with an output directory unless the user asks for a quick text-only result or chart data is unavailable.
- Send generated charts to the user — when charts are created, share all chart PNGs immediately.
- All chart styling is driven by
ecommerce_chart_helpers.py which reads from chart_style.json (derived from references/display-rules.md). Do NOT hardcode colors in scripts.
Usage
# Validate a product
python3 scripts/product_validator.py '{"asin": "B07RL88DD2"}'
# With profitability calculation
python3 scripts/product_validator.py '{"asin": "B07RL88DD2", "cost_price": 19.99}'
# Batch validation
python3 scripts/product_validator.py '{"asins": ["B0XXX", "B0YYY"]}'
# With chart output
python3 scripts/product_validator.py '{"asin": "B07RL88DD2"}' --chart /tmp/output
Parameters
| Parameter |
Type |
Default |
Description |
asin |
string |
- |
Single ASIN to validate |
asins |
array |
- |
Multiple ASINs to validate |
market |
string |
|
Target marketplace |
cost |
float |
- |
Product cost for profitability |
price |
float |
- |
Selling price (or use current) |
category |
string |
- |
Category for FBA fees |
Output Structure
{
"asin": "B07RL88DD2",
"market": "place": "US",
"title": "Product Name",
"validation_score": 85,
"risk_level": "VALID",
"recommendation": "Data trustworthy, proceed with standard due diligence",
"scores": {
"bsr": {"score": 25, "max": 25},
"price": {"score": 18, "max": 20},
"buybox": {"score": 17, "max": 20},
"sellers": {"score": 12, "max": 15},
"reviews": {"score": 12, "max": 15},
"stock": {"score": 4, "max": 5}
},
"red_flags": [
{"type": "SEASONALITY", "severity": "low", "detail": "Q4 spike expected"}
],
"seasonality": {
"pattern": "MILD",
"volatility": "LOW",
"peak_months": [11, 12],
"current_position": "OFF_PEAK",
"bsr_trend": "STABLE",
"modifier": 2,
"interpretation": "Mild seasonal pattern"
},
"product_data": {
"price": 19.99,
"reviews": 1200,
"rating": 4.5,
"bsr": 5000,
"sellers": 3,
"monthly_sales": 250,
"is_variant": false
},
"bsr_history": [4800, 5100, 4900, 5000],
"price_history": [19.99],
"series_data": {
"available": true,
"bsr_data_points": 180,
"bsr_category": "Kitchen & Dining",
"analysis": {"manipulation_flags": []}
},
"profitability": {
"cost": 5.00,
"price": 19.99,
"fba_fee": 6.50,
"referral_fee": 3.00,
"profit": 5.49,
"margin": 27.5,
"roi": 109.8,
"break_even_price": 14.50,
"ad_buffer": 2.00
},
"category": "_benchmarks": {
"avg_monthly_search_volume": 45000,
"avg_difficulty": 55
}
}
Profitability Calculator
| Component |
Description |
| Referral Fee |
Category-based (typically 15%) |
| FBA Fee |
Size/weight-based fulfillment |
| Profit |
Price - Cost - Fees |
| Margin |
Profit / Price × 100 |
| ROI |
Profit / Cost × 100 |
| Break-even |
Cost + Fees (minimum viable price) |
| Ad Buffer |
Recommended PPC budget headroom |
Charts Generated
| Chart |
Description |
File |
| BSR Trend |
Historical BSR line |
bsr_trend.png |
| Price Trend |
Historical price line |
price_trend.png |
| Score Breakdown |
Component bar |
score_breakdown.png |
| Profitability |
Waterfall chart |
profitability.png |
Risk Flags
| Flag |
Trigger |
| ⚠️ BSR Volatile |
>50% variance in 90 days |
| ⚠️ Price War |
>20% price drops |
| ⚠️ Review Spike |
Unnatural review growth |
| ⚠️ Stock Issues |
Frequent out-of-stock |
| ⚠️ Seasonal |
>2x BSR variance by season |
Modular Components
product_validator.py (main entry)
├── profitability_module.py (standalone FBA calculator)
└── seasonality_module.py (standalone seasonality detector)
Workflow Integration
✅ Validation Phase
├── demand-validator → Validate demand is real
├── price-history-analyzer → Check for price wars
└── product-validator → Validate product data ← YOU ARE HERE
Environment Variables
| Variable |
Required |
Description |
NEXSCOPE_PROXY_BASE |
Yes |
NexScope proxy base URL |
NEXSCOPE_API_KEY |
Yes |
NexScope proxy API key |
Limitations
- BSR history requires Keepa data
- FBA fee estimates are approximate
- Review authenticity is heuristic
- Seasonality detection needs 12+ months data
- Does not account for advertising costs in base profit
References
references/fba-fee-guide.md — Fee calculation detailsreferences/display-rules.md — Chart styling guidelines
