Amazon Listing Analyzer

Amazon Listing Analyzer

1. Overview

Amazon Listing Analyzer 为 Amazon 卖家提供 Listing 健康度诊断、关键词研究和竞品对标分析，输出结构化的优化建议包。面向月销售额 $5k–$200k、1–5 年运营经验的中阶卖家。不依赖实时 API，所有数据基于内置规则库和模板生成。

2. Trigger

用户通过对话发起以下类型的分析请求：

"分析这个 Listing：..."

"帮我检查这个 Amazon Listing 的健康度"

"关键词分析：[产品名称/类目]"

"竞品对标：[ASIN 或产品描述]"

"给我一个 Amazon Listing 优化建议"

3. Workflow

用户输入 → 解析意图（健康度评分 | 关键词分析 | 竞品对标 | 优化建议包）
         → 调用对应分析模块
         → 聚合结果 → 输出结构化报告

3.1 Listing 健康度评分

1. 解析标题、五点、描述、Search Terms、Backend Keywords 2. 按以下维度打分（每项 0-100）： - 标题质量（长度、关键词、前缀品牌词） - 五点描述（数量、长度、特征覆盖） - 描述质量（结构化程度、可读性） - 图片描述（Alt 文本覆盖） - 关键词填充（无重复、合理密度） - 合规性检查（禁止词、过敏词） 3. 综合得分 = 加权平均 4. 输出诊断结论 + 分项问题列表

3.2 关键词分析

1. 基于产品信息生成种子关键词列表 2. 对每个关键词从内置词库查询： - 搜索量等级（High/Medium/Low/Unknown） - 竞争度等级（High/Medium/Low/Unknown） - 相关性评级（1-5） 3. 输出关键词矩阵表 + 建议优先词列表

3.3 竞品对标分析

1. 输入竞品 ASIN 或产品描述 2. 从内置竞品模板库匹配相似产品 3. 对比维度：标题结构、价格区间、评分分布、评论数、核心卖点 4. 输出对标表 + 差异化机会点

3.4 优化建议包

1. 综合健康度评分 + 关键词分析 + 竞品对标 2. 生成结构化建议： - 标题优化建议 - 五点描述优化建议 - 描述优化建议 - 关键词补全建议 - 图片建议清单 3. 按优先级排序输出

4. I/O Specification

输入（JSON dict 或对话文本）

{
  "intent": "health_score | keyword_analysis | competitor_benchmark | full_optimization",
  "product_title": "string (optional)",
  "bullet_points": ["string"] * 5 (optional)",
  "product_description": "string (optional)",
  "search_terms": "string (optional)",
  "backend_keywords": "string (optional)",
  "competitor_asin": "string (optional)",
  "product_category": "string (optional)",
  "product_features": ["string"] (optional)"
}

输出（JSON dict）

{
  "status": "success | partial | error",
  "module": "string",
  "result": {
    "health_score": {
      "total": 0-100,
      "dimensions": {
        "title": {"score": 0-100, "issues": []},
        "bullets": {"score": 0-100, "issues": []},
        "description": {"score": 0-100, "issues": []},
        "keywords": {"score": 0-100, "issues": []},
        "compliance": {"score": 0-100, "issues": []}
      },
      "summary": "string"
    },
    "keyword_analysis": {
      "matrix": [
        {"keyword": "string", "volume": "string", "competition": "string", "relevance": 1-5}
      ],
      "priority_keywords": ["string"],
      "long_tail_keywords": ["string"]
    },
    "competitor_benchmark": {
      "comparisons": [
        {"dimension": "string", "you": "string", "competitor": "string", "opportunity": "string"}
      ],
      "gaps": ["string"]
    },
    "optimization_package": {
      "title": {"current": "string", "suggested": "string", "priority": "high|medium|low"},
      "bullets": [{"current": "string", "suggested": "string", "priority": "string"}],
      "description": {"current": "string", "suggested": "string", "priority": "string"},
      "keywords": {"missing": [], "redundant": [], "suggested": []}
    }
  },
  "errors": ["string"] (optional)
}

5. Safety

不请求或存储用户的真实 Amazon 账户凭证

不调用任何外部 API（数据来自内置规则库）

所有分析输出为参考建议，不构成 Amazon 平台合规承诺

输入文本进行基础长度校验，拒绝超长输入（>10,000 字符）

不处理任何涉及个人信息的内容

6. Examples

Example 1: 健康度评分

输入：

{"intent": "health_score", "product_title": "Premium Wireless Bluetooth Headphones with Noise Cancellation", "bullet_points": ["High quality sound", "30-hour battery life", "Comfortable fit", "Fast charging", "Foldable design"], "product_description": "Experience music like never before...", "search_terms": "wireless headphones bluetooth noise cancellation"}

输出：

{
  "status": "success",
  "module": "health_score",
  "result": {
    "health_score": {
      "total": 72,
      "dimensions": {
        "title": {"score": 75, "issues": ["缺少核心关键词搜索量验证", "品牌词位置偏后"]},
        "bullets": {"score": 70, "issues": ["卖点不够具体，缺少数据支撑"]},
        "description": {"score": 68, "issues": ["缺少品牌故事和使用场景描述"]},
        "keywords": {"score": 78, "issues": ["Search Terms 未充分利用"]},
        "compliance": {"score": 90, "issues": []}
      },
      "summary": "Listing 健康度中等偏上，主要改进空间在标题关键词精准度和五点描述的具体性。"
    }
  }
}

Example 2: 关键词分析

输入：

{"intent": "keyword_analysis", "product_category": "Electronics > Headphones", "product_features": ["wireless", "noise cancellation", "bluetooth", "long battery life", "comfortable"]}

输出：

{
  "status": "success",
  "module": "keyword_analysis",
  "result": {
    "keyword_analysis": {
      "matrix": [
        {"keyword": "wireless headphones", "volume": "High", "competition": "High", "relevance": 5},
        {"keyword": "bluetooth headphones", "volume": "High", "competition": "High", "relevance": 5},
        {"keyword": "noise cancelling headphones", "volume": "High", "competition": "Medium", "relevance": 4},
        {"keyword": "long battery life headphones", "volume": "Medium", "competition": "Low", "relevance": 4},
        {"keyword": "comfortable headphones", "volume": "Medium", "competition": "Medium", "relevance": 3}
      ],
      "priority_keywords": ["wireless headphones", "bluetooth headphones", "noise cancelling headphones"],
      "long_tail_keywords": ["long battery life wireless headphones", "comfortable noise cancelling headphones"]
    }
  }
}

7. Acceptance Criteria

1. SKILL.md 完整 — 包含 Overview/Trigger/Workflow/I/O/Safety/Examples/Acceptance 全部 7 个模块 2. handler.py 可独立运行 — python3 handler.py 直接执行并输出有效 JSON 结果 3. 测试通过 — python3 tests/test_handler.py 至少 3 个测试用例全部通过 4. 元数据完整 — skill.json 和 .claw/identity.json 字段齐全 5. 无实时 API 依赖 — 所有数据来自内置规则库和模板 6. 输入校验 — 拒绝超长输入（>10,000 字符）并返回错误 7. 输出格式一致 — 所有模块返回统一 JSON 结构