Product Title Analyze

对产品标题进行分词分析，提取词频、场景词、人群词、材质词等属性维度。当用户想分析产品标题、提取标题高频词、进行标题分词、发现场景词或人群词、对比不同商品的标题关键词用法、基于词频优化Listing标题、识别一组ASIN中的常见属性规律、title tokenization, word frequency analysis, scene keyword extraction, audience keyword analysis, title optimization, attribute keyword extraction, keyword frequency时触发此技能。即使用户未明确说"标题分析"，只要其需求涉及将产品标题拆解为有意义的词组、统计关键词频率或按提取的属性对商品分组，也应触发此技能。

linkfox-ai@linkfox-ai

Install

openclaw skills install @linkfox-ai/linkfox-product-title-analyze

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

Field	API Name	Required	Description	Example
Analysis Request	tokenizationAndCountingRequest	Yes	Natural-language instruction describing which attribute dimension to extract from titles	"Count scene words in product titles"
Output Mode	outputMode	No	How multi-value attributes are returned. `MULTIPLE_RECORDS` (default): one record per value. `COMMA_SEPARATED`: all values in one record	MULTIPLE_RECORDS
Reference Data	refResultData	No	Externally supplied product data (only needed when referencing data from a previous conversation turn)	(JSON string)

Response Fields -- Product Attributes

Field	API Name	Description	Example
ASIN	asin	Product ASIN identifier	B0XXXXXXXX
Product Title	title	Original product title	Portable Camping Lantern...
Attribute Name	attributeName	Extracted attribute category	Scene Word
Attribute Value	attributeValue	Extracted attribute value	Outdoor / Camping
Price	price	Product price	29.99
Monthly Sales	monthlySalesUnits	Monthly unit sales	1200
Monthly Revenue	monthlySalesRevenue	Monthly sales revenue	35988
Rating	rating	Product rating	4.5
Rating Count	ratings	Number of ratings	3820
Available Date	availableDate	Listing date	2024-03-15
Brand	brand	Brand name	BrandX
Image URL	imageUrl	Main product image	https://...

Response Fields -- Attribute Groups

Field	API Name	Description
Attribute Name	attributeName	The attribute category for this group (e.g., "Scene Word")
Attribute Value	attributeValue	A specific value within the group (e.g., "Outdoor")
Count	count	Number of products sharing this attribute value
ASIN List	asins	List of ASINs that share this attribute value

Response Metadata

Field	API Name	Description
Render Type	type	UI rendering style
Columns	columns	Column definitions for table rendering
Source Type	sourceType	Data source type
Token Cost	costToken	Total LLM tokens consumed (input + output)

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)

text

Count scene words appearing in product titles

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

text

Count audience words appearing in product titles

Material words

text

Count material-related words appearing in product titles

Function / feature words

text

Count function or feature words appearing in product titles

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

text

Count scene words AND audience words in product titles

Split this into two separate calls instead.

outputMode

Value	Behavior	When to Use
MULTIPLE_RECORDS	Each attribute value becomes its own record (default)	Most analysis -- easier to count, sort, and group
COMMA_SEPARATED	Multiple values stay in one record, comma-separated	When you want to see all attributes per ASIN at a glance

Display Rules

Present data in tables: Show extracted attributes and their frequencies in clear, sortable tables
Highlight top keywords: Call out the most frequent attribute values so patterns are immediately visible
Group summary first: When attributeGroups is returned, present the grouped summary before the per-product detail
One dimension at a time: If the user wants multiple dimensions analyzed, run separate calls and present results sequentially
Token cost awareness: The response includes costToken; do not display it unless the user asks about usage
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

User Says	Scenario
"What scene words appear in these titles?"	Scene-word extraction
"Analyze title keywords", "title word frequency"	General title tokenization
"What audience are these products targeting?"	Audience-word extraction
"Common materials in these listings"	Material-word extraction
"Help me optimize my title based on competitors"	Competitive title keyword analysis
"What words do top sellers use in titles?"	High-frequency keyword discovery
"Group these products by title attributes"	Attribute-based product grouping

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.

API Usage

This tool calls the LinkFox tool gateway API. See references/api.md for calling conventions, request parameters, and response structure. You can also execute scripts/title_analyze.py directly to run queries.

Feedback:

Auto-detect and report feedback via the Feedback API when any of the following apply:

The functionality or purpose described in this skill does not match actual behavior
The skill's results do not match the user's intent
The user expresses dissatisfaction or praise about this skill
Anything you believe could be improved

Call the feedback API as specified in references/api.md. Do not interrupt the user's flow.

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