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Amazon Fba Product Finder

作者 mguozhen · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ✓ 安全检测通过
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在 OpenClaw 中安装
/install amazon-fba-product-finder
功能描述
Amazon FBA product research agent. Find profitable FBA products — extract rank, sales volume, estimated revenue, competition score, and profit potential with...
使用说明 (SKILL.md)

Amazon FBA Product Finder

Research Amazon FBA product opportunities like a pro. Analyze rank, sales volume, estimated revenue, and competition score to validate products before sourcing — no tool subscription required.

Paste product data from Amazon search results or product pages. The agent validates opportunity and outputs a go/no-go recommendation with full reasoning.

Commands

find research \x3Cproduct-idea>      # research a product idea end-to-end
find validate \x3Cdata>              # validate product with pasted Amazon data
find score \x3Cproduct>              # compute opportunity score for a product
find market \x3Ckeyword>             # analyze top 10 results for a search term
find revenue \x3Casin> \x3Cbsr>         # estimate monthly revenue from BSR
find compare \x3Cproduct1> \x3Cproduct2># compare two product opportunities
find niche \x3Ccategory>             # identify niches within a category
find checklist \x3Cproduct>          # run 10-point FBA viability checklist
find report \x3Cproduct>             # full product research report
find save \x3Cproduct>               # save research to workspace

What Data to Provide

  • Product idea / keyword — what you want to research
  • Amazon search results data — titles, prices, BSRs, review counts from the page
  • Category — main category (used for BSR-to-sales conversion)
  • Your target price point — expected selling price
  • Sourcing context — where you plan to source (Alibaba, domestic, etc.)

Product Research Framework

Step 1: Market Size Estimation

Estimate monthly revenue for the top 10 results:

  • Total market revenue = sum of estimated monthly revenue for top 10 listings
  • Healthy market: Top 10 generate $50,000-$500,000/month combined
  • Too small (\x3C$30k): Limited opportunity
  • Too large (>$1M): Possibly too competitive for new entrant

Step 2: BSR to Sales Volume Conversion

Estimated monthly sales by category and BSR:

Category: Kitchen & Home
BSR 1-100:      8,000-50,000 units/month
BSR 100-500:    2,000-8,000 units/month
BSR 500-1000:   800-2,000 units/month
BSR 1000-3000:  300-800 units/month
BSR 3000-10000: 100-300 units/month
BSR 10000+:     \x3C100 units/month

Category: Sports & Outdoors
BSR 1-100:      5,000-30,000 units/month
BSR 100-500:    1,500-5,000 units/month
BSR 500-2000:   500-1,500 units/month
BSR 2000-5000:  150-500 units/month
BSR 5000-15000: 50-150 units/month

Adjust by ±30% based on seasonality and listing quality.

Step 3: Competition Analysis

Assess the top 10 listings:

Signal Green Yellow Red
Avg reviews \x3C200 200-500 >500
Review gap (1st vs 10th) \x3C5x 5-20x >20x
Listing quality Poor-Medium Medium Excellent
Brand dominance 0 brands in top 5 1-2 brands 3+ same brands
Price range $10-$50 $5-$10 or $50-$100 \x3C$5 or >$100

Step 4: Product Opportunity Score (POS)

Score 0-100 based on 10 factors:

1. Search volume (keyword demand)        0-10 pts
2. Revenue potential (top 10 combined)   0-10 pts
3. Competition gap (review accessibility) 0-10 pts
4. Margin potential (price vs. cost)     0-10 pts
5. Product simplicity (risk of defects)  0-10 pts
6. Differentiation potential             0-10 pts
7. Seasonal stability                    0-10 pts
8. Sourcing accessibility               0-10 pts
9. Regulatory risk (no hazmat/IP issues) 0-10 pts
10. Growth trajectory                   0-10 pts

POS 75+:  Strong opportunity — proceed to sourcing
POS 60-74: Moderate opportunity — validate further
POS 45-59: Weak opportunity — significant risks
POS \x3C45:  Pass — not worth pursuing

Step 5: 10-Point FBA Viability Checklist

[ ] 1. Price $15-$70 (sweet spot for FBA margins)
[ ] 2. Lightweight \x3C2 lbs (keeps FBA fees manageable)
[ ] 3. Small dimensions (standard-size, not oversize)
[ ] 4. No seasonal dependency (sells year-round)
[ ] 5. No brand dominance in top 10 (room for new sellers)
[ ] 6. Top seller has \x3C500 reviews (achievable competition)
[ ] 7. Estimated monthly revenue $5,000+ (viable market)
[ ] 8. Clear differentiation opportunity (can improve on top listings)
[ ] 9. No dangerous goods / fragile items
[ ] 10. Sourceable on Alibaba for \x3C30% of selling price

FBA Fee Structure

Referral fee:     8-15% of selling price (varies by category)
FBA fulfillment:
  Small standard: $3.22 (≤16oz) to $4.37 (≤1lb)
  Large standard: $5.42 (≤1lb) to $9.73 (≤20lb)
Storage fee:
  Standard:       $0.87/cubic foot (Jan-Sep)
                  $2.40/cubic foot (Oct-Dec)

Quick check: Target 25-35% net margin after all fees

Margin Calculation Template

Selling price:        $XX.XX
- Referral fee (15%): $XX.XX
- FBA fee:            $XX.XX
- COGS (incl. ship):  $XX.XX
- PPC cost (est. 15%): $XX.XX
= Net profit per unit: $XX.XX
= Net margin:          XX%

Minimum viable margin: 20% net

Workspace

Creates ~/fba-research/ containing:

  • opportunities/ — validated product research reports
  • rejected/ — products considered but passed
  • pipeline/ — products under active consideration
  • sourcing/ — supplier notes linked to products

Output Format

Every product research outputs:

  1. Product Overview — name, category, price range, estimated market size
  2. Opportunity Score — POS out of 100 with factor breakdown
  3. Top 10 Competitive Landscape — table of top listings with key metrics
  4. Revenue Estimate — range of monthly revenue at various BSR positions
  5. Margin Analysis — expected net profit per unit at target price
  6. Viability Checklist — pass/fail on all 10 criteria
  7. Go/No-Go Recommendation — clear verdict with reasoning
  8. Next Steps — if go: sourcing plan; if no-go: why and what to look for instead
安全使用建议
This skill appears to do what it says: it analyzes Amazon data you paste in and saves research reports under ~/fba-research/. Before installing or using it, be aware it will write files to your home directory (the workspace) and can run Bash commands if the agent is granted that tool — only provide product data you want analyzed (do not paste credentials or private files). No network credentials or external API keys are requested. If you prefer not to persist reports, run the agent in a sandbox or delete ~/fba-research/ after use.
功能分析
Type: OpenClaw Skill Name: amazon-fba-product-finder Version: 1.0.0 The skill bundle consists of a metadata file and a markdown instruction set (SKILL.md) for an AI agent to perform Amazon FBA product research. It defines a methodology for analyzing market data, estimating sales volume, and calculating profit margins. While it requests access to the Bash tool, the instructions are focused on managing a local workspace (~/fba-research/) and processing user-provided data without any evidence of malicious intent, data exfiltration, or unauthorized system access.
能力评估
Purpose & Capability
Name/description (FBA product research) matches the SKILL.md: it defines BSR→sales heuristics, scoring, checklists, and commands for researching and saving product reports. There are no unrelated environment variables, binaries, or install steps requested that would be disproportionate to product-research functionality.
Instruction Scope
Instructions ask the agent to accept pasted Amazon search/product data from the user, compute estimates, score opportunities, and optionally save reports. The SKILL.md does not direct the agent to read arbitrary system files, access external endpoints, or collect unrelated data. It does specify creating a workspace in the user's home directory to store reports, which is appropriate for this use case.
Install Mechanism
No install spec or code files are provided (instruction-only skill), so nothing will be downloaded or written beyond any files the agent saves to the declared workspace. This is low-risk from an install perspective.
Credentials
The skill requires no environment variables, credentials, or config paths. That is proportionate for a tool that operates on user-provided paste data and local report files.
Persistence & Privilege
The skill will create and write to ~/fba-research/ (opportunities/, rejected/, pipeline/, sourcing/). Writing a dedicated workspace in the user's home directory is reasonable, but users should be aware the agent will persist reports locally. always:false and default autonomous invocation are present (normal).
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install amazon-fba-product-finder
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /amazon-fba-product-finder 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.0
Initial release of Amazon FBA Product Finder—research and validate profitable FBA opportunities using advanced product analysis without extra tools. - Research product ideas, estimate sales/revenue, analyze competition, and assess FBA viability directly from Amazon data. - Inspired by Jungle Scout methodology; provide only product data, and receive a full opportunity report and GO/NO-GO recommendation. - Includes commands for niche discovery, product comparison, revenue estimation, competitive analysis, and a detailed FBA profit checklist. - Outputs structured research reports covering opportunity score, margin analysis, top 10 listing landscape, and sourcing guidance. - Creates organized workspace directories for ongoing product and sourcing research. - Designed for everyone interested in FBA product validation—no tool subscriptions required.
元数据
Slug amazon-fba-product-finder
版本 1.0.0
许可证 MIT-0
累计安装 1
当前安装数 1
历史版本数 1
常见问题

Amazon Fba Product Finder 是什么?

Amazon FBA product research agent. Find profitable FBA products — extract rank, sales volume, estimated revenue, competition score, and profit potential with... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 160 次。

如何安装 Amazon Fba Product Finder?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install amazon-fba-product-finder」即可一键安装,无需额外配置。

Amazon Fba Product Finder 是免费的吗?

是的,Amazon Fba Product Finder 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Amazon Fba Product Finder 支持哪些平台?

Amazon Fba Product Finder 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 Amazon Fba Product Finder?

由 mguozhen(@mguozhen)开发并维护,当前版本 v1.0.0。

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