Product Title Analyzer

This skill guides you on how to tokenize and analyze product titles from previously queried products, helping Amazon sellers extract keyword patterns, scene words, audience words, and other attribute dimensions from product listing titles.

Core Concepts

Product Title Analysis performs intelligent tokenization on product titles that have already been retrieved in the current conversation. It uses LLM-powered analysis to extract structured attributes (scene words, audience words, materials, colors, etc.) from free-text titles, then groups and counts them for pattern discovery.

Automatic data aggregation: The tool automatically collects products from all prior steps in the current conversation turn -- even across paginated queries. You do NOT need to manually pass product data unless you are referencing data from a previous conversation turn.

One dimension per request: Each call should analyze exactly ONE attribute dimension (e.g., scene words OR audience words). Do NOT request multiple dimensions in a single call.

Data Fields

Request Fields

Response Fields -- Product Attributes

Response Fields -- Attribute Groups

Response Metadata

Parameter Guide

tokenizationAndCountingRequest Examples

The tokenizationAndCountingRequest parameter is a natural-language instruction telling the tool which dimension to analyze. Keep it focused on a single dimension.

Scene words (where / when the product is used)

Count scene words appearing in product titles

Audience / target-user words (who the product is for)

Count audience words appearing in product titles

Material words

Count material-related words appearing in product titles

Function / feature words

Count function or feature words appearing in product titles

Incorrect -- multiple dimensions in one request (do NOT do this)

Count scene words AND audience words in product titles

Split this into two separate calls instead.

outputMode

Display Rules

1. Present data in tables: Show extracted attributes and their frequencies in clear, sortable tables
2. Highlight top keywords: Call out the most frequent attribute values so patterns are immediately visible
3. Group summary first: When attributeGroups is returned, present the grouped summary before the per-product detail
4. One dimension at a time: If the user wants multiple dimensions analyzed, run separate calls and present results sequentially
5. Token cost awareness: The response includes costToken; do not display it unless the user asks about usage
6. Error handling: If the tool returns an error, explain the reason and suggest corrective action (e.g., "No products found in current conversation -- please query products first")

Applicable Scenarios

Not Applicable Scenarios

• No products queried yet: The tool requires products to already exist in the conversation context. Prompt the user to search for products first.
• Advertising / PPC keyword suggestions: This tool analyzes existing titles, not ad keywords.
• Full listing copywriting: This tool extracts and counts words; it does not generate new titles.
• Backend search term analysis: This is for visible title analysis, not hidden search terms.
• ABA search term data: Use the ABA Data Explorer skill instead.

调用方式

• API 端点：POST /product/titleAnalyze（完整参数/响应/错误码见 references/api.md）
• Python 脚本：python scripts/title_analyze.py '<JSON 参数>' [--inline]
• 成本约束：本工具会消耗算力；同一会话同一参数组合默认只调用一次，脚本带 24h 本地缓存。失败/空结果不得自动换关键词、翻页或改邮编连续试探；需要继续检索时先向用户说明会产生额外消耗。

输出策略（脚本默认行为）：

• 始终将完整响应写入 <cwd>/linkfox/<YYYY-MM-DD>/<session>/data/linkfox-product-title-analyze-<timestamp>.json（<cwd> 为脚本执行时的工作目录，在 Claude Code 里即当前项目目录；<session> 取自环境变量 SESSION_ID，按用户任务自动聚合；禁止写入 /tmp，当前目录不可写则报错）
• 响应体 ≤ 8 KB：落盘后把完整 JSON 打印到 stdout
• 响应体 > 8 KB：落盘后 stdout 只输出摘要（顶层字段、常见计数如 total/costToken、最大列表字段的长度 + 前 3 条样本）
• 加 --inline 强制全量打印到 stdout（同样落盘）

读数据建议：先看摘要判断是否足够；需要具体字段时优先用 jq或ConvertFrom-Json 从保存的 json 文件按需抽取，避免整份 JSON 进入上下文。

解决认证和算力问题

发生以下异常情况时，采用 references/onboarding.md 引导解决问题：

异常情况

• 未配置API Key：环境变量未配置 LINKFOX_AGENT_API_KEY，也未配置 LINKFOXAGENT_API_KEY。
• 响应401或402状态码
• 响应提示算力或余额不足：消息含"算力余额不足/计费不足/余额不足/quota exceeded/insufficient balance/套餐到期/需充值/请充值"，或类似含义的内容。

算力消耗规则

按动态规则计费：消耗算力 = sum(所有被处理商品标题的(输入消耗的算力 + 输出结果消耗的算力))。

重要：本技能的服务按倍数动态计算，可能一次性消耗大量算力，必须提醒用户，由用户决定是否继续。