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Find product ideas and blue-ocean product opportunities. Triggers: what should I sell, product ideas, product discovery, untapped products. Use for discovering opportunities, not validating a specific ASIN or broad market overview.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
| name | ecommerce.product-opportunity-finder |
| version | 2.2.0 |
| description | Find product ideas and blue-ocean product opportunities. Triggers: what should I sell, product ideas, product discovery, untapped products. Use for discovering opportunities, not validating a specific ASIN or broad market overview. |
| allowed-tools | ["Bash","Read","Write"] |
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
cdinto 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.
Before running this skill, if the user provides a product, image, ASIN, listing, product ID, or product idea, first identify:
Use this product understanding to choose parameters, filters, competitors, regions, keywords, or analysis dimensions before executing the script.
What products should I sell?
Find blue ocean products with multi-dimensional filtering and opportunity scoring.
— What products should I sell? — What product opportunities are there?
| Source | Endpoint | Data |
|---|---|---|
| Amazon Search | /api/v1/tools/research/amazon/search | Products, sales, reviews |
| eBay | /api/v1/tools/research/ebay/search | Cross-platform demand validation |
| Walmart | /api/v1/tools/research/walmart/search | Multi-channel opportunity |
| Google Trends | /api/v1/tools/research/googleTrend/getTrendByKeys | Trend validation |
| Keepa | /api/v1/tools/research/keepa/productRequest | Product details, monthly sales |
| Keepa | /api/v1/tools/research/keepa/productSeries | BSR/price history |
Multi-dimensional score based on:
| Dimension | Weight | Metrics |
|---|---|---|
| 📈 Demand | 25% | Sales velocity, search volume |
| ⚔️ Competition | 25% | Review barriers, brand presence |
| 💰 Profit | 25% | Margins, price stability |
| 🎯 Opportunity | 25% | Gaps, differentiation |
| Type | Description |
|---|---|
| 🏖️ Low Competition | High demand, few sellers |
| ⭐ Quality Gap | Low ratings, room for improvement |
| 💵 Price Gap | Underserved price segment |
| 📈 Rising Star | Growing demand trend |
| 🔄 Channel Arbitrage | Price difference across platforms |
| 📦 Bundle Opportunity | FBT (frequently bought together) |
| 🎯 Niche Segment | Underserved sub-category |
| Preset | Description |
|---|---|
| Conservative | Low risk, proven demand |
| Balanced | Mix of risk/reward |
| Aggressive | Higher risk, higher potential |
| Premium | High-price products |
| Budget | Low-price, high-volume |
| Trending | Focus on rising trends |
| Category | Reason |
|---|---|
| ⚠️ Hazmat | Shipping restrictions |
| ⚠️ Fragile | High damage rates |
| ⚠️ Patent/IP Risk | Legal issues |
| ⚠️ Oversized | FBA fee impact |
| ⚠️ Gated Category | Approval required |
--chart with an output directory unless the user asks for a quick text-only result or chart data is unavailable.ecommerce_chart_helpers.py which reads from chart_style.json (derived from references/display-rules.md). Do NOT hardcode colors in scripts.# Find opportunities in a category
python3 scripts/product_opportunity_finder.py '{"keyword": "yoga accessories"}'
# With specific preset
python3 scripts/product_opportunity_finder.py '{"keyword": "kitchen gadgets", "preset": "conservative"}'
# Custom filters
python3 scripts/product_opportunity_finder.py '{"keyword": "pet supplies", "min_price": 10, "max_reviews": 500}'
# Multi-platform analysis
python3 scripts/product_opportunity_finder.py '{"keyword": "phone cases", "platforms": ["amazon"]}'
# With chart output
python3 scripts/product_opportunity_finder.py '{"keyword": "yoga mat"}' --chart /tmp/output
| Parameter | Type | Default | Description |
|---|---|---|---|
keyword | string | required | Category keyword |
market | string | Target marketplace | |
preset | string | "balanced" | Filter preset |
min_price | float | - | Minimum price |
max_price | float | - | Maximum price |
min_sales | int | - | Minimum monthly sales |
max_reviews | int | - | Maximum review count |
platforms | array | ["amazon"] | Platforms to analyze |
The output will be a structured markdown report, following this format:
Product Opportunity Analysis Report: [Keyword, e.g., "Skincare"]
1. Executive Summary
2. Market Overview
3. Top Product Opportunities
| Rank | ASIN | Product Name | Brand | Price | Monthly Sales | Score | Grade | Opportunity Type |
|---|---|---|---|---|---|---|---|---|
| 1 | [opportunities[0].asin] | [opportunities[0].title] | [opportunities[0].brand] | [opportunities[0].price] | [opportunities[0].monthly_sales] | [opportunities[0].score] | [opportunities[0].grade] | [opportunities[0].opportunity_types] |
| 2 | ... | ... | ... | ... | ... | ... | ... | ... |
(Showing top 5-10 opportunities)
4. Opportunity Type Distribution
| Opportunity Type | Count |
|---|---|
| Low Competition (LOW_COMPETITION) | [insights.opportunity_types.LOW_COMPETITION] |
| Quality Gap (QUALITY_GAP) | [insights.opportunity_types.QUALITY_GAP] |
| Price Gap (PRICE_GAP) | [insights.opportunity_types.PRICE_GAP] |
| Rising Star (RISING_STAR) | [insights.opportunity_types.RISING_STAR] |
| Channel Arbitrage (CHANNEL_ARBITRAGE) | [insights.opportunity_types.CHANNEL_ARBITRAGE] |
| Niche Segment (NICHE_SEGMENT) | [insights.opportunity_types.NICHE_SEGMENT] |
| (Other types, if any) |
5. Actionable Recommendations
6. Attached Visualizations
| Chart | Description | File |
|---|---|---|
| Seasonality Trend | Monthly pattern | seasonality.png |
| Segment Opportunities | By type bar | segments.png |
| ROI Waterfall | Margin breakdown | roi_waterfall.png |
| Radar Score | Multi-dimension | radar.png |
| Product Comparison | Top opportunities | comparison.png |
| Signal | Description |
|---|---|
| FBT Match | Products frequently bought together |
| Complementary | Same customer, different need |
| Accessory | Main product + add-ons |
🎯 Selection Phase
├── product-opportunity-finder → Find specific products ← YOU ARE HERE
└── new-product-tracker → Track rising products
| Variable | Required | Description |
|---|---|---|
NEXSCOPE_API_KEY | Yes | NexScope proxy API key |
NEXSCOPE_PROXY_BASE | Yes | NexScope proxy base URL |
references/opportunity-types.md — Opportunity definitionsreferences/visualization.md — Chart specificationsreferences/display-rules.md — Chart styling guidelinesWhen analyzing more items than you want to run in a single invocation, keep charts and reports aligned by saving each batch as raw JSON and then generating one merged result.
Step 1: run each batch with --output to save intermediate JSON.
python3 scripts/product_opportunity_finder.py '{"keyword": "example"}' --output /tmp/batch1.json
python3 scripts/product_opportunity_finder.py '{"keyword": "example 2"}' --output /tmp/batch2.json
Step 2: merge every batch JSON and generate the final unified chart.
python3 scripts/product_opportunity_finder.py --merge /tmp/batch1.json /tmp/batch2.json --sort score --chart /tmp/final-charts
Use the merged JSON output and /tmp/final-charts/merged_ranking.png for the final report. Do not present per-batch charts as final charts when the text report has been globally re-ranked.
Compare Amazon competitor business metrics: price, reviews, BSR, sales, seller count, traffic. Triggers: competitor metrics, who performs best, price/BSR/review comparison. Use for business performance, not listing copy; use competitor-listing-analyzer for content structure.
Analyze Amazon listing content and structure: title, bullets, images, A+ content, and listing quality. Triggers: listing breakdown, reverse-engineer listings, title/bullet/image analysis. Use for content audits, not business metrics; use competitor-analyzer for price/BSR/sales.
Validate whether demand is real by comparing search volume with sales and buying intent. Triggers: is demand real, fake demand, inflated volume, are people buying, conversion gap. Use for demand proof, not broad market overview or niche go/no-go scoring.
Check visual/design patent similarity from product images. Triggers: design infringement, look-alike patent, visual similarity, design clearance. Use for appearance/design risk, not utility claims or legal-status checks.
Suggest product differentiation and USP using competitor/review evidence. Triggers: how to stand out, USP, competitive advantage, product improvement. Use for positioning and feature strategy, not raw competitor metrics or review monitoring.
Find visually similar Amazon products from an image. Triggers: reverse image search, find lookalikes, who else sells this, source by image. Use for marketplace lookalikes, not patent similarity or design infringement checks.