# 在 Claude Code 环境中运行（自动触发 LLM 分类）
python .claude/skills/keyword-research/scripts/workflow.py B07PWTJ4H1 US --claude-code-env

# 跳过分类，仅采集数据（后续可手动LLM分类）
python .claude/skills/keyword-research/scripts/workflow.py B07PWTJ4H1 US --skip-classification

# 禁用LLM分类，使用规则分类
python .claude/skills/keyword-research/scripts/workflow.py B07PWTJ4H1 US --disable-llm-classification

输入: ASIN + 站点 + (可选) 产品信息
  ↓
┌─────────────────────────────────────────────────────────────┐
│ Step 1: 基础数据采集                                         │
├─────────────────────────────────────────────────────────────┤
│ 1. product_traffic_terms      → 产品流量词 (50-200个)       │
│ 2. competitor_product_keywords → 竞品布局词 (100-500个)     │
│ 3. category_keywords           → 类目核心词 (100-500个)      │
└─────────────────────────────────────────────────────────────┘
  ↓
┌─────────────────────────────────────────────────────────────┐
│ Step 2: 长尾词扩展                                          │
├─────────────────────────────────────────────────────────────┤
│ 从基础词中选择 Top 30 核心词                                 │
│ → 对每个调用 keyword_related_words (50-100个延伸词)         │
│ → 预计获取 1000-2000 个长尾词                               │
└─────────────────────────────────────────────────────────────┘
  ↓
┌─────────────────────────────────────────────────────────────┐
│ Step 3: 数据清洗                                            │
├─────────────────────────────────────────────────────────────┤
│ 1. 去重（归一化：小写、去除特殊字符）                        │
│ 2. 过滤无效词（过短、非英文、乱码）                          │
│ 3. 合并搜索量/CPC 等指标                                    │
└─────────────────────────────────────────────────────────────┘
  ↓
最终词库: 2000+ 关键词

产品信息: Coat Rack Wall Mount, Wood, 5 Hooks, Entryway

否定词: freestanding, over door, floor, tree, shoe
品牌词: umbra, simplehuman, mDesign, household essentials
材质词: wood, wooden, metal, aluminum, bamboo
场景词: entryway, bathroom, mudroom, garage, bedroom
属性词: wall mount, heavy duty, rustic, vintage, expandable, 5 hook
功能词: hanging, storage, organizer, display
核心词: coat rack, hook, hanger, hat rack, towel rack
其他: coatrac (拼写错误), perchero (西语)

curl -s -X POST "https://mcp.sorftime.com?key={API_KEY}" \
  -H "Content-Type: application/json" \
  -d '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"product_traffic_terms","arguments":{"amzSite":"US","asin":"ASIN"}}}'

curl -s -X POST "https://mcp.sorftime.com?key={API_KEY}" \
  -H "Content-Type: application/json" \
  -d '{"jsonrpc":"2.0","id":2,"method":"tools/call","params":{"name":"competitor_product_keywords","arguments":{"amzSite":"US","asin":"ASIN"}}}'

# 首先获取产品详情以获取 NodeID
curl -s -X POST "https://mcp.sorftime.com?key={API_KEY}" \
  -H "Content-Type: application/json" \
  -d '{"jsonrpc":"2.0","id":3,"method":"tools/call","params":{"name":"product_detail","arguments":{"amzSite":"US","asin":"ASIN"}}}'

# 然后获取类目关键词
curl -s -X POST "https://mcp.sorftime.com?key={API_KEY}" \
  -H "Content-Type: application/json" \
  -d '{"jsonrpc":"2.0","id":4,"method":"tools/call","params":{"name":"category_keywords","arguments":{"amzSite":"US","nodeId":"NODE_ID"}}}'

curl -s -X POST "https://mcp.sorftime.com?key={API_KEY}" \
  -H "Content-Type: application/json" \
  -d '{"jsonrpc":"2.0","id":N,"method":"tools/call","params":{"name":"keyword_related_words","arguments":{"amzSite":"US","searchKeyword":"KEYWORD"}}}'

你是一位亚马逊关键词分类专家。请根据以下产品信息，将关键词列表按 8 个维度分类。

【产品信息】
产品名称: {product_name}
材质: {material}
核心属性: {features}
使用场景: {use_cases}
否定特征: {negative_features}

【分类维度】
1. NEGATIVE: 不相关的词，需直接否定
2. BRAND: 竞品品牌名称
3. MATERIAL: 材质相关词 (wood, metal, aluminum...)
4. SCENARIO: 使用场景词 (entryway, bathroom...)
5. ATTRIBUTE: 属性修饰词 (wall mount, heavy duty...)
6. FUNCTION: 功能词 (hanging, storage...)
7. CORE: 核心产品词 (coat rack, hook...)
8. OTHER: 其他（拼写错误、其他语言等）

【待分类关键词】
{keywords_json}

【输出格式】
请以 JSON 格式输出：
{
  "NEGATIVE": ["word1", "word2", ...],
  "BRAND": [...],
  ...
}

keyword-reports/
└── {ASIN}_{Site}_{YYYYMMDD}/
    ├── report.md                    # Markdown 分析报告
    ├── keywords.csv                 # 完整关键词词库（分类后）
    ├── negative_words.txt           # 否定词清单
    ├── brand_words.txt              # 品牌词清单
    ├── categorized_summary.json     # 分类统计
    └── dashboard.html               # HTML 可视化仪表板

keyword,category,search_volume,cpc,competition,application,relevance_score
coat rack,CORE,54000,1.85,high,广泛匹配,1.00
wooden coat rack,MATERIAL,12000,1.25,medium,精准匹配,0.95
freestanding coat rack,NEGATIVE,4500,0.85,low,直接否定,0.00
...

python workflow.py B07PWTJ4H1 US --product-info product.json

请输入产品核心属性（用逗号分隔）:
> Wall Mount, 5 Hooks, 16.5 inches, Heavy Duty

{
  "product_name": "Coat Rack Wall Mount",
  "material": "Wood",
  "features": ["Wall Mount", "5 Hooks", "16.5 inches", "Heavy Duty"],
  "use_cases": ["Entryway", "Bathroom", "Mudroom", "Garage"],
  "negative_features": ["Freestanding", "Over Door", "Floor"]
}

curl -s -X POST "https://mcp.sorftime.com?key={API_KEY}" \
  -H "Content-Type: application/json" \
  -d '{"jsonrpc":"2.0","id":N,"method":"tools/call","params":{"name":"TOOL_NAME","arguments":{"amzSite":"US","KEY":"VALUE"}}}'

cat keyword-reports/{ASIN}_{Site}_{YYYYMMDD}/classification_prompt.txt

keyword-reports/{ASIN}_{Site}_{YYYYMMDD}/categorized_result.json

cd keyword-reports/{ASIN}_{Site}_{YYYYMMDD}/
python ../../.claude/skills/keyword-research/scripts/regenerate_reports.py

python .claude/skills/keyword-research/scripts/regenerate_reports.py --asin B0FG6QG8C8 --site US

python .claude/skills/keyword-research/scripts/regenerate_reports.py --dir "keyword-reports\B0FG6QG8C8_US_20260314"

python .claude/skills/keyword-research/scripts/regenerate_reports.py --list