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Get Started Free →Amazon competitor monitoring and competitive intelligence for sellers. Track pricing changes, inventory levels, new product launches, review velocity, and advertising patterns for competitor ASINs. Set up monitoring alerts for price drops, stock-outs, and market changes. Works on 12 Amazon marketplaces. No API key required. Use when: (1) monitoring competitor pricing strategies, (2) tracking competitor inventory and stock patterns, (3) detecting new product launches in your category, (4) analyzi
.claude/skills/nexscope-ai-amazon-competitor-monitoring/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-13 | ✗→✓ | ▲ Improved | 13% | 0% |
| case-14 | ✗→✓ | ▲ Improved | 19% | 0% |
| case-15 | ✗→✓ | ▲ Improved | 21% | 0% |
| case-12 | ✗→✓ | ▲ Improved | 20% | 0% |
| case-17 | ✗→✓ | ▲ Improved | 53% | 0% |
Track competitor strategies, pricing changes, and market opportunities. No API key — works out of the box.
bashnpx skills add nexscope-ai/Amazon-Skills --skill amazon-competitor-monitoring -g
Users can ask naturally. Examples:
Monitor competitor ASIN B08XYZ123 for pricing changes on Amazon USTrack the top 5 competitors in portable blenders category — alert me for price drops over 10%Analyze competitor pricing strategy for ASIN B09ABC456 over the last 90 daysSet up monitoring for yoga mat category — track new launches and inventory patternsCompare my product B0DEF789 against top 3 competitors: pricing, reviews, and BSR performanceUse web_search to find competitor products:
"[product category]" site:amazon.com — identify top products by relevance"[your main keyword]" amazon best sellers — find category leadersFor each competitor ASIN, use web_fetch to gather current state:
Use web_search to understand competitor patterns:
"[ASIN] price history discount" — find pricing pattern information"[brand] new product launch amazon" — detect launch patterns"[ASIN] reviews complaints issues" — identify competitor weaknessesSynthesize findings into actionable insights following the output format below.
Present the final report in this structure:
## Competitor Analysis Report: [Category]
**Marketplace:** Amazon [US/UK/DE/...] | **Analysis Date:** [current date]
**Competitors Analyzed:** [count] | **Monitoring Period:** [timeframe]
### 1. Pricing Intelligence
| ASIN | Product | Current Price | Price Change | Alert Status |
|------|---------|---------------|--------------|--------------|
| B08ABC123 | [Competitor A] | $19.99 | ↓ 20% (30d) | 🔴 Major Drop |
| B09DEF456 | [Competitor B] | $34.99 | → Stable | 🟢 Normal |
| B0GHI789 | [Competitor C] | $27.50 | ↑ 5% (7d) | 🟡 Minor Rise |
**Price Range:** $19.99 - $34.99 | **Average:** $27.49
### 2. Inventory & Availability
| ASIN | Stock Status | Restock Pattern | Opportunity Window |
|------|-------------|-----------------|-------------------|
| B08ABC123 | In Stock | Weekly | Low |
| B09DEF456 | Low Stock (3 left) | Monthly | High - Stock Out Risk |
| B0GHI789 | Out of Stock | Unknown | High - Market Gap |
### 3. Performance Metrics
| ASIN | BSR | Rating | Reviews | Review Velocity |
|------|-----|--------|---------|----------------|
| B08ABC123 | #156 | 4.3★ | 1,247 | 15/week |
| B09DEF456 | #89 | 4.6★ | 2,891 | 8/week |
| B0GHI789 | #2,456 | 4.1★ | 456 | 3/week |
### 4. Market Opportunities
**Immediate Actions:**
- Price gap at $22-25 range (no strong competitor)
- Inventory opportunity during Competitor C stock-out
- Feature gap: competitors lack [specific feature]
**Strategic Insights:**
- Market leader vulnerable on durability complaints
- Premium segment emerging above $35
- Seasonal demand increase detected
**Recommended Monitoring Alerts:**
- Price drops >10% in 7-day period
- Stock-out events for top 3 competitors
- New product launches in category
- Review velocity spikes (>2x normal)This skill works well when chained with other skills from the Nexscope Amazon-Skills repository.
bashnpx skills add nexscope-ai/Amazon-Skills --skill amazon-sales-estimator -g
Step 1: "Monitor competitors B08ABC123, B09DEF456"
→ amazon-competitor-monitoring provides pricing and BSR data
Step 2: "Estimate monthly sales for those competitors"
→ amazon-sales-estimator calculates revenue benchmarksbashnpx skills add nexscope-ai/Amazon-Skills --skill amazon-keyword-research -g
Step 1: "Find competitor keyword strategies for yoga mat"
→ amazon-keyword-research reveals keyword gaps
Step 2: "Monitor top competitors in that keyword space"
→ amazon-competitor-monitoring tracks their performanceThis skill uses publicly available Amazon data and web search results. It cannot access real-time pricing APIs, historical BSR data, or automated alerts. For live competitor monitoring with automatic notifications and deeper analytics, check out Nexscope — Your AI Assistant for smarter E-commerce decisions.
Built by Nexscope — research, validate, and act on e-commerce opportunities with AI.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-13 | fail→pass | 16,545 | 7,983 | -52% | 1 | 1 | 0% | 2,431 | 2,735 | +13% | 0 | 0 | — |
case-14 | fail→pass | 14,335 | 7,063 | -51% | 1 | 1 | 0% | 2,281 | 2,706 | +19% | 0 | 0 | — |
case-15 | fail→pass | 17,201 | 9,143 | -47% | 1 | 1 | 0% | 2,448 | 2,953 | +21% | 0 | 0 | — |
case-01 | fail→fail | 44,268 | 8,297 | -81% | 1 | 1 | 0% | 3,980 | 1,947 | -51% | 0 | 0 | — |
case-02 | fail→fail | 17,752 | 7,211 | -59% | 1 | 1 | 0% | 2,755 | 1,757 | -36% | 0 | 0 | — |
case-03 | fail→fail | 24,181 | 7,517 | -69% | 1 | 1 | 0% | 3,711 | 1,838 | -50% | 0 | 0 | — |
case-04 | pass→pass | 13,900 | 9,285 | -33% | 1 | 1 | 0% | 2,162 | 2,972 | +37% | 0 | 0 | — |
case-05 | fail→fail | 16,806 | 9,257 | -45% | 1 | 1 | 0% | 2,600 | 2,909 | +12% | 0 | 0 | — |
case-06 | pass→pass | 18,134 | 13,808 | -24% | 1 | 1 | 0% | 3,140 | 3,733 | +19% | 0 | 0 | — |
case-07 | fail→fail | 17,518 | 7,503 | -57% | 1 | 1 | 0% | 2,816 | 2,021 | -28% | 0 | 0 | — |
case-08 | pass→fail | 21,076 | 5,207 | -75% | 1 | 1 | 0% | 3,133 | 1,677 | -46% | 0 | 0 | — |
case-09 | fail→fail | 20,413 | 7,710 | -62% | 1 | 1 | 0% | 3,022 | 1,896 | -37% | 0 | 0 | — |
case-10 | fail→fail | 13,780 | 11,935 | -13% | 1 | 1 | 0% | 2,193 | 1,912 | -13% | 0 | 0 | — |
case-11 | fail→fail | 22,598 | 8,734 | -61% | 1 | 1 | 0% | 2,971 | 1,826 | -39% | 0 | 0 | — |
case-12 | fail→pass | 13,519 | 5,186 | -62% | 1 | 1 | 0% | 2,011 | 2,405 | +20% | 0 | 0 | — |
case-16 | fail→fail | 20,592 | 7,256 | -65% | 1 | 1 | 0% | 3,278 | 1,867 | -43% | 0 | 0 | — |
case-17 | fail→pass | 18,294 | 18,669 | +2% | 1 | 1 | 0% | 2,524 | 3,852 | +53% | 0 | 0 | — |
case-18 | pass→fail | 23,255 | 6,879 | -70% | 1 | 1 | 0% | 3,643 | 1,912 | -48% | 0 | 0 | — |
case-19 | fail→fail | 19,649 | 5,995 | -69% | 1 | 1 | 0% | 3,050 | 1,748 | -43% | 0 | 0 | — |
case-20 | fail→fail | 14,611 | 14,883 | +2% | 1 | 1 | 0% | 2,725 | 4,326 | +59% | 0 | 0 | — |
case-21 | fail→fail | 9,290 | 8,553 | -8% | 1 | 1 | 0% | 1,752 | 2,163 | +23% | 0 | 0 | — |
case-22 | pass→fail | 8,417 | 16,198 | +92% | 1 | 1 | 0% | 1,402 | 3,513 | +151% | 0 | 0 | — |
case-23 | fail→fail | 19,665 | 25,907 | +32% | 1 | 1 | 0% | 3,190 | 6,751 | +112% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 23 cases were attempted, and 11 counted toward the lift figure. The other 12 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +9 percentage points is the difference between those two pass rates over the 11 comparable cases. 8 cases got worse with the skill loaded, and they are included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.