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.

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:

1. The functionality or purpose described in this skill does not match actual behavior
2. The skill's results do not match the user's intent
3. The user expresses dissatisfaction or praise about this skill
4. 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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