lvjunjie-byte

Amazon FBA Finder

Business Opsv1.0.0
By lvjunjie-byteUpdated 2026-05-11amazon-fba-finder@clawhub_lvjunjie-byte/amazon-fba-finder
E-commerce#数据分析

According to AlphaMoat, as of 2026-09, Amazon FBA Finder has 1,548 downloads and 0 stars — ranked #4,738 of 5,078 AI skills overall, and #271 of 297 in Business Ops.

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151
Downloads
1.5K
Stars
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Overall Rank
#4,738
Top 94% overall · Top 92% in Business Ops
PlatformInstallsDownloadsStarsComments
ClawHub ↗251.1K--
SkillHub ↗1511.5K-—

Platforms count differently and update at different times.

What this skill does

The source claims features for finding product opportunities, analyzing competition, recommending suppliers, and estimating FBA costs and returns, and describes workflows using Amazon and Alibaba APIs. The available material is a product description and does not independently verify implementation, supplier information, or sales/profit estimates; it also warns that sales figures are estimates and may deviate. Verify API data, fees, Amazon policies, and target-market regulations before business decisions. Do not treat scores or revenue examples as guarantees. Discovery and supplier integrations may be partially stubbed; confirm actual implementation before relying on them. Running the examples needs Python dependencies and scoped Amazon/Alibaba keys in private storage, installed in an isolated reviewed environment.

Profile published 2026-10-27

What you can do with it

  • Filter candidate products by category and chosen criteria
  • Organize competition, supplier information, and cost factors
  • Estimate profitability under different price and cost assumptions

When to use it

  • When organizing Amazon FBA product-selection and market-research questions
  • After verifying data sources, API permissions, fees, and policy compliance

Example prompts

Generated from the skill’s documentation as examples; not results of actual runs.

List the market data, costs, and policy items that need verification for this product brief. Separate product claims from validated results and do not recommend an investment or guarantee profit.

Monthly Trend

Downloads1.5K
1849171.6K2026-03 · Downloads 2852026-05 · Downloads 5632026-07 · Downloads 9572026-08 · Downloads 1.2K2026-09 · Downloads 1.5K26-0326-09
Stars-
0112026-03 · Stars -2026-05 · Stars -2026-07 · Stars -2026-08 · Stars -2026-09 · Stars -26-0326-09

The counting method changed in 2026-09: skills with the same name are now counted separately by author, and the full catalog is covered. Dashed lines show months before the change; they are not compared with later months, and no growth is computed across the change.

MonthDownloadsMoMStars
2026-091.5K—-
2026-081.2K+25.1%-
2026-07957—-
2026-05563—-
2026-03285—-

SKILL.md (original)

The author’s original file, shown as plain text without edits or review by AlphaMoat. Links are not clickable; check commands before running them. Fetched 2026-10-23.

7.0 KB · ClawHub v1.0.0
# 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
Show all (329 lines)

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

```bash
# 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:

```bash
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

```python
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

```python
# 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

```python
# 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

```python
# 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

```python
# 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
```python
# 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
```python
# 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
```python
# 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 |
|-----------|-----------|---------|
| <100      | <50       | 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

```python
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

```python
# 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

- **文档**: https://github.com/your-repo/amazon-fba-finder
- **问题反馈**: https://github.com/your-repo/amazon-fba-finder/issues
- **邮件**: [email protected]

---

## ⚖️ 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。

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Data month: 2026-09 · Downloads, stars and installs are aggregated monthly from public skill registries (ClawHub, SkillHub). See methodology.