Amazon Listing Analyzer

为中阶 Amazon 卖家提供 Listing 健康度诊断、关键词分析、竞品对标及结构化优化建议，助力提升销售表现。

Security Scan

The skill is self-contained and its code, instructions, and requirements align with its stated purpose (offline Amazon listing analysis using built-in rules).

Purpose & Capability

Name/description promise (health checks, keyword analysis, competitor benchmarking) matches the code and SKILL.md. The handler uses local keyword DB, forbidden-words list, and competitor templates—no unexpected cloud or unrelated capabilities requested.

Instruction Scope

SKILL.md explicitly states no external API calls and not to request Amazon credentials; the included handler.py implements analysis using only local data structures and text processing. There are no instructions to read unrelated system files or exfiltrate data.

Install Mechanism

No install spec; skill is instruction/code-only and uses standard Python stdlib (json, re, typing). Nothing is downloaded or extracted from external URLs.

Credentials

The skill declares no required environment variables, no credentials, and no config paths. The code does not reference environment secrets or external service tokens.

Persistence & Privilege

Skill does not request 'always' presence, does not modify other skills or system-wide settings, and has no installation side effects recorded.

Assessment

This skill appears coherent and self-contained: it analyzes text using built-in rules and does not request credentials or call external APIs. Before installing, consider that outputs are heuristic recommendations (not Amazon compliance/legal advice) and rely on the embedded keyword DB and templates which may be limited or stale. If you need live market metrics or seller-account actions, a different skill that requires vetted API credentials would be necessary. If you want extra assurance, review the remaining parts of handler.py (the truncated portion) to confirm there are indeed no network, file I/O, or subprocess calls; run the provided tests locally to verify behavior (including the input-length guard).

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