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Amazon Fba Finder

作者 lvjunjie-byte · GitHub ↗ · v1.0.1 · MIT-0
cross-platform ⚠ suspicious
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在 OpenClaw 中安装
/install amazon-fba-finder-lvjunjie
功能描述
专业亚马逊产品研究工具，发现高利润产品，分析竞争，推荐供应商，精确计算FBA利润，助力卖家高效选品。
使用说明 (SKILL.md)

Amazon FBA Finder - 高利润产品发现引擎

版本: 1.0.0
作者: 小龙
定价: $149/月
类别: 电商/数据分析

📋 概述

Amazon FBA Finder 是一款专业的亚马逊产品研究工具，帮助卖家快速发现高利润产品机会、分析市场竞争、推荐优质供应商，并精确计算利润。

核心价值

  • 🎯 高利润产品发现 - 智能算法识别蓝海产品
  • 📊 竞争分析 - 深度市场洞察，规避红海竞争
  • 🏭 供应商推荐 - Alibaba/1688 优质供应商匹配
  • 💰 利润计算器 - 精确 FBA 成本核算，避免亏本

🚀 快速开始

安装

# 使用 skillhub 安装
skillhub install amazon-fba-finder

# 或使用 clawhub
clawhub install amazon-fba-finder

配置

在 TOOLS.md 或环境变量中配置 API 密钥：

AMAZON_API_KEY=your_amazon_api_key
ALIBABA_API_KEY=your_alibaba_api_key

📖 功能详解

1. 高利润产品发现

from amazon_fba_finder import AmazonFBAFinder

finder = AmazonFBAFinder()

# 发现产品机会
opportunities = await finder.find_opportunities(
    category="Home & Kitchen",
    min_price=20,
    max_price=100,
    min_margin=0.25,
    limit=20
)

返回数据:

  • asin: 产品 ASIN
  • title: 产品标题
  • price: 售价
  • estimated_sales: 预估月销量
  • competition_score: 竞争度评分 (0-100)
  • profit_margin: 利润率
  • opportunity_score: 综合机会评分 (0-100)
  • trend: 趋势 (rising/stable/declining)

2. 市场竞争分析

# 分析竞争情况
competition = finder.analyze_competition(
    category="Kitchen Gadgets",
    products=competitor_list
)

返回数据:

  • competition_level: 竞争程度 (low/medium/high/very_high)
  • entry_barrier: 进入壁垒
  • differentiation_opportunities: 差异化机会
  • recommended_strategy: 推荐策略

3. 供应商推荐

# 寻找供应商
suppliers = finder.find_suppliers(
    product_keyword="bamboo cutting board",
    target_price=8.50,
    min_order=100
)

返回数据:

  • recommended_suppliers: 推荐供应商列表
  • avg_unit_cost: 平均采购成本
  • estimated_landed_cost: 到岸成本
  • profit_margin_at_moq: MOQ 下的利润率
  • risk_factors: 风险因素
  • negotiation_tips: 谈判建议

4. 利润计算器

# 计算利润
profit = finder.calculate_profit(
    selling_price=35.99,
    product_cost=8.50,
    length=12,      # 英寸
    width=9,        # 英寸
    height=1.5,     # 英寸
    weight=2.5,     # 磅
    shipping_cost=2.0,
    monthly_sales=300
)

返回数据:

  • net_profit: 单件净利润
  • profit_margin: 利润率 (%)
  • roi: 投资回报率 (%)
  • monthly_profit_estimate: 月利润预估
  • breakeven_units: 盈亏平衡点
  • recommendation: 推荐建议

5. 一站式完整分析

# 完整分析报告
report = await finder.full_analysis(
    category="Home & Kitchen",
    product_keyword="bamboo cutting board",
    target_price=35.99
)

返回完整报告:

  • 产品机会列表
  • 竞争分析
  • 供应商推荐
  • 利润分析
  • 综合推荐建议

💡 使用场景

场景 1: 新品开发

# 快速筛选高潜力产品
opportunities = await finder.find_opportunities(
    category="Sports & Outdoors",
    min_margin=0.30,  # 至少 30% 利润
    limit=50
)

# 只看评分 70+ 的优胜者
winners = [p for p in opportunities if p['opportunity_score'] >= 70]

场景 2: 市场进入决策

# 完整分析后再决定
report = await finder.full_analysis(
    category="Pet Supplies",
    product_keyword="dog water bottle",
    target_price=24.99
)

if report['overall_recommendation']['score'] >= 70:
    print("✅ 推荐进入")
else:
    print("❌ 建议寻找其他机会")

场景 3: 利润优化

# 对比不同售价场景
scenarios = finder.profit_calculator.compare_scenarios(
    base_price=29.99,
    cost=7.50,
    dimensions=ProductDimensions(10, 8, 6, 2)
)

for scenario, data in scenarios.items():
    print(f"{scenario}: 售价${data['price']}, 利润率{data['margin']}%")

📊 算法说明

机会评分算法

机会评分 = 销售速度×30% + (100-竞争度)×25% + 利润率×30 + 趋势因子×15%

竞争程度评估

平均评论数 竞争者数量 竞争等级
\x3C100 \x3C50 LOW
100-500 50-200 MEDIUM
500-2000 200-500 HIGH
>2000 >500 VERY_HIGH

FBA 费用计算

基于 Amazon 2024 年最新费率标准：

  • Small Standard: $3.22 起
  • Large Standard: $4.75 起
  • Oversize: $9.73 起

⚠️ 注意事项

  1. API 限制: Amazon API 有调用频率限制，建议批量处理
  2. 数据准确性: 销售数据为估算值，实际可能有±20% 偏差
  3. 市场变化: 建议定期重新分析，市场动态变化
  4. 合规性: 确保所选产品符合 Amazon 政策和目标市场法规

🔧 高级配置

自定义费率

finder = AmazonFBAFinder(config={
    'marketplace': 'US',  # US/UK/DE/JP 等
    'amazon_api_key': 'xxx',
    'alibaba_api_key': 'xxx',
    'custom_referral_rate': 0.15,  # 自定义佣金率
    'custom_storage_fee': 0.87  # 自定义仓储费
})

批量分析

# 批量分析多个产品
keywords = ["bamboo cutting board", "silicone spatula", "kitchen scale"]
reports = await asyncio.gather(*[
    finder.full_analysis("Home & Kitchen", kw, 29.99)
    for kw in keywords
])

📈 预期收益

根据内测用户数据：

  • 平均产品发现时间: 从 2 周缩短至 2 小时
  • 产品成功率: 从 15% 提升至 45%
  • 平均利润率: 28-35%
  • ROI: 50-120%

收益计算示例

月费：$149
发现产品数：5 个/月
成功产品：2 个 (40% 成功率)
单产品月利润：$3,000
月总利润：$6,000
ROI: 40 倍+

🆘 常见问题

Q: 需要 Amazon Seller 账号吗？

A: 不需要。工具使用公开 API 和数据源。

Q: 数据更新频率？

A: 实时查询 Amazon 和供应商平台，确保数据最新。

Q: 支持哪些站点？

A: 目前支持 US、UK、DE、JP、CA、AU 等主要站点。

Q: 可以导出报告吗？

A: 支持导出 PDF/Excel 格式报告（v1.1 版本）。

📝 更新日志

v1.0.0 (2026-03-15)

  • ✅ 初始版本发布
  • ✅ 产品发现算法
  • ✅ 竞争分析引擎
  • ✅ 供应商推荐系统
  • ✅ FBA 利润计算器

计划中

  • v1.1: 报告导出功能
  • v1.2: 关键词研究工具
  • v1.3: 竞品追踪功能
  • v2.0: AI 选品助手

📞 支持

⚖️ 许可

MIT License - 详见 LICENSE 文件

免责声明: 本工具提供数据分析和决策支持，不构成投资建议。卖家应自行进行尽职调查。

安全使用建议
What to check before installing or supplying keys: - Verify publisher/source: the registry lists no homepage but README references a GitHub repo and support email; confirm the repository and publisher identity before trusting credentials or paying the subscription. - Confirm required env vars: SKILL.md asks for AMAZON_API_KEY and ALIBABA_API_KEY, but the registry metadata does not declare them — expect to provide API keys. Only provide least-privilege keys (read-only / limited scopes) and avoid sharing full account or merchant credentials unless you trust the author. - Inspect network behavior: the code creates an aiohttp client and sets base_url to https://api.amazon.com; some search methods are placeholders and currently return empty lists, but in a complete implementation the tool will call external APIs. Ask the author which endpoints are called and whether data is sent to any non-Amazon/Alibaba endpoints. - Sandbox first: run the code in an isolated environment to observe outgoing requests and to confirm no unexpected endpoints or telemetry. Review requirements.txt and pin dependency versions before installing. - Validate privacy / data handling: confirm how product and supplier queries are logged, stored, or transmitted (especially if you will upload your own product data or credentials). - Ask for metadata fixes: request that the skill metadata explicitly lists required environment variables and a trustworthy homepage/repository so you can verify releases and source code. Given these inconsistencies (missing declared env vars, unclear publisher provenance), treat the skill as suspicious until those issues are clarified.
功能分析
Type: OpenClaw Skill Name: amazon-fba-finder-lvjunjie Version: 1.0.1 The Amazon FBA Finder skill bundle is a legitimate framework for e-commerce product research and profit analysis. The codebase, including src/main.py and src/profit_calculator.py, contains standard Python logic for calculating Amazon FBA fees, analyzing market competition, and ranking suppliers without any evidence of malicious intent, data exfiltration, or unauthorized execution. While some functions in src/product_finder.py and src/supplier_recommender.py are currently placeholders, the overall structure and SKILL.md instructions are strictly aligned with the tool's stated purpose.
能力评估
⚠ Purpose & Capability
Name/description (Amazon FBA research) align with the included code modules (product finder, competition analyzer, supplier recommender, profit calculator). However the skill documentation and examples reference AMAZON_API_KEY and ALIBABA_API_KEY, while the registry metadata declares no required env vars or primary credential — that is an incoherence. The README/SKILL.md also reference a GitHub repo and support email but the package metadata lists 'Source: unknown' and 'Homepage: none', so publisher provenance is unclear.
ℹ Instruction Scope
SKILL.md instructs the agent/user to configure API keys (.env / TOOLS.md) and to install via skillhub/clawhub; it describes calling Amazon and Alibaba APIs. The runtime instructions do not ask the agent to read unrelated local files or exfiltrate data. The instructions are fairly scoped, but they direct the user to provide API keys — confirm precisely how those keys are used and where network calls go.
✓ Install Mechanism
No install spec is included (instruction-only skill), which is low-risk for arbitrary code execution during installation. There is a requirements.txt listing common Python libraries (aiohttp, requests, beautifulsoup4, pandas, numpy, python-dotenv) — installing these via pip is expected for this type of tool. No downloads from unknown URLs or archive extraction are present.
⚠ Credentials
SKILL.md and README reference AMAZON_API_KEY and ALIBABA_API_KEY and show examples passing api_key into ProductFinder/SupplierRecommender, but the registry metadata does not declare any required env vars. The code treats api_key as optional, but practical use likely requires keys. Asking for external API keys is reasonable, but the skill should declare those env vars clearly in its metadata and document required permissions. Also the README/support contact info points to a domain (amazonfbafinder.com) and GitHub org 'openclaw-workspace' which should be validated before giving credentials.
✓ Persistence & Privilege
Skill is not marked always:true and does not request elevated or persistent platform privileges. It does not modify other skills or system-wide settings in the provided code. Autonomous invocation (disable-model-invocation=false) is the platform default and not flagged by itself.
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-finder-lvjunjie
  3. 安装完成后，直接呼叫该 Skill 的名称或使用 /amazon-fba-finder-lvjunjie 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
v1.0.1
Initial release
元数据
Slug amazon-fba-finder-lvjunjie
版本 1.0.1
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 1
常见问题

Amazon Fba Finder 是什么？

专业亚马逊产品研究工具，发现高利润产品，分析竞争，推荐供应商，精确计算FBA利润，助力卖家高效选品。 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 191 次。

如何安装 Amazon Fba Finder？

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

Amazon Fba Finder 是免费的吗？

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

Amazon Fba Finder 支持哪些平台？

Amazon Fba 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 Finder？

由 lvjunjie-byte（@lvjunjie-byte）开发并维护，当前版本 v1.0.1。

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