Ecommerce Dropshipping Product Research

Evaluates dropshipping products by scoring demand, competition, margin, creative fit, and risk to recommend go, test, or reject decisions.

Dropshipping Product Research

Overview

Dropshipping Product Research helps beginners and small operators evaluate whether a product is worth testing. It is a descriptive, non-API MVP focused on structured scoring, risk filtering, and clear go / test / reject decisions.

Trigger

Use this skill when the user wants to:

• evaluate a product idea for dropshipping
• compare multiple product candidates
• estimate risk, margin, and creative fit
• build a weekly shortlist for testing

Example prompts

• "Evaluate this product for dropshipping in the US"
• "Should I test a galaxy projector or pet grooming glove?"
• "Give me a go / test / reject recommendation"
• "Help me score 3 product ideas for my store"

Workflow

1. Capture candidate, market, and positioning constraints.
2. Infer demand, competition, margin, creative angle, and risk.
3. Produce a viability score and recommendation.
4. Summarize why it may win, why it may fail, and what to test next.

Inputs

• product name or keyword
• optional product link or niche
• target market
• price target or cost hints
• mode: single product / batch / trend scouting

Outputs

• viability score
• sub-scores: demand, competition, margin, creative fit, risk
• recommendation: Go / Test / Reject
• memo with hypotheses and next steps

Usage Scenarios

1. User input: "Score these 5 product ideas for a US-focused Shopify dropshipping store." → Expected output: 5-product scorecard — demand (Google Trends + Amazon BSR proxy), competition (ad-intensity check, review-count saturation), margin (cost-price vs. market-price with ad-cost breakeven), creative potential (video-ad suitability score), risk (shipping time, return rate, IP infringement check) — ranked with go/test/reject verdict.
2. User input: "I found a product on AliExpress with 10,000 orders. Is it too late to enter?" → Expected output: Market-saturation analysis — competition-velocity check, ad-auction heat (CPM trend), differentiation-gap assessment, and "late entrant" strategy options (niche-down, geo-target underserved region, content-first approach).
3. User input: "Build a weekly product-research workflow I can run in 3 hours every Sunday." → Expected output: Weekly research SOP — trend-scanning (30 min), criteria-scoring top candidates (90 min), supplier-vetting (45 min), creative-brainstorming (15 min) — with scoring spreadsheet template and decision-journal format.

Scenario 2: 在多多跨境找爆品的野路子

User input: "我在Temu上做店群，但不知道选什么品好。有没有快速找到下一个爆品的方法？" Expected output: 拼多多/Temu选品方法论——第一步：关注抖音/小红书/快手带货直播间，看哪个品在密集推广但淘宝/拼多多上竞争还不激烈（搜索量上升但商家数还没暴涨的阶段）；第二步：在1688跨境专供频道按"新品"排序，找最近上架且供应商有现货的品；第三步：在Temu和SHEIN前台按"飙升"和"最新"排序，看哪些品突然涨起来但还没被刷屏；第四步：用Google Trends查关键词在目标国（美国/欧洲）的搜索趋势，找搜索量上升30%以上的品。关键工具：1688跨境专供+神鹰数据+Google Trends+Temu前台。

Safety

• No marketplace scraping or real-time trend API access
• No guarantee of profit or compliance clearance
• Recommendations are heuristic and should be validated with real tests

Acceptance Criteria

• Must output markdown
• Must include all five scoring dimensions
• Must include Go / Test / Reject recommendation
• Must include at least three risk or execution notes