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linkfox-ai/linkfox-skills/skills/linkfox-product-title-analyze/SKILL.md

linkfox-product-title-analyze

Use it for research tasks; the detail page covers purpose, installation, and practical steps.

Source repository stars
85
Declared platforms
0
Static risk flags
0
Last source update
2026-08-21
Source checked
2026-08-25
DecisionCompatibilityInstallWorkflowPermissionsEvidenceSourceFAQ

Decision brief

What it does: where it fits

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.

Best for

    Not for

    • Tasks that require unconfirmed production actions or broad system permissions.
    • Environments where the pinned source and install steps cannot be inspected.

    Compatibility matrix

    Platform support, with evidence labels

    PlatformStatusEvidenceWhat to check
    CodexNot declaredNo explicit evidencePortability before use
    Claude CodeNot declaredNo explicit evidencePortability before use
    CursorNot declaredNo explicit evidencePortability before use
    Gemini CLINot declaredNo explicit evidencePortability before use
    Open the compatibility checker

    Installation

    Inspect first. Install second.

    The source command is displayed only when detected. A safe inspection prompt is always available so your agent can explain every action before execution.

    Source-detected install commandSource
    npx skills add https://github.com/linkfox-ai/linkfox-skills --skill "skills/linkfox-product-title-analyze"
    Safe inspection promptEditorial

    Inspect the Agent Skill "linkfox-product-title-analyze" from https://github.com/linkfox-ai/linkfox-skills/blob/93a1dbf71f793169db2b01928ec385fe1f7bd843/skills/linkfox-product-title-analyze/SKILL.md at commit 93a1dbf71f793169db2b01928ec385fe1f7bd843. List every install step, command, network request, credential, file read/write, external action, and rollback step. Explain whether it fits my task. Do not install or execute anything until I approve.

    Workflow

    What the source asks the agent to do

    1. 01

      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, th…

      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, audien…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 r…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.
    2. 02

      Data Fields

      Review the “Data Fields” section in the pinned source before continuing.

      Review and apply the “Data Fields” source section.
    3. 03

      Request Fields

      Review the “Request Fields” section in the pinned source before continuing.

      Review and apply the “Request Fields” source section.
    4. 04

      Response Fields -- Product Attributes

      Review the “Response Fields -- Product Attributes” section in the pinned source before continuing.

      Review and apply the “Response Fields -- Product Attributes” source section.
    5. 05

      Response Fields -- Attribute Groups

      Review the “Response Fields -- Attribute Groups” section in the pinned source before continuing.

      Review and apply the “Response Fields -- Attribute Groups” source section.

    Permission review

    Static risk signals and limitations

    No configured static risk pattern was detected

    This is not proof of safety. Runtime behavior, indirect dependencies, and hidden external systems are outside the static scan.

    Evidence record

    Why each signal appears

    EvidenceSourceComputedTestedEditorial
    SignalValueEvidence typeMeaning
    Quality score91/100ComputedDocumentation, specificity, maintenance, and trust rules
    Repository stars85SourceRepository attention, not individual Skill quality
    Compatibility0 platformsSourceDeclared in the catalog source record
    Usage guideautomated source guideEditorialGenerated or reviewed according to the visible evidence level

    Pinned source

    Provenance and original SKILL.md

    Repository
    linkfox-ai/linkfox-skills
    Skill path
    skills/linkfox-product-title-analyze/SKILL.md
    Commit
    93a1dbf71f793169db2b01928ec385fe1f7bd843
    License
    MIT
    Collected
    2026-08-25
    Default branch
    main
    View the original SKILL.md

    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

    FieldAPI NameRequiredDescriptionExample
    Analysis RequesttokenizationAndCountingRequestYesNatural-language instruction describing which attribute dimension to extract from titles"Count scene words in product titles"
    Output ModeoutputModeNoHow multi-value attributes are returned. MULTIPLE_RECORDS (default): one record per value. COMMA_SEPARATED: all values in one recordMULTIPLE_RECORDS
    Reference DatarefResultDataNoExternally supplied product data (only needed when referencing data from a previous conversation turn)(JSON string)

    Response Fields -- Product Attributes

    FieldAPI NameDescriptionExample
    ASINasinProduct ASIN identifierB0XXXXXXXX
    Product TitletitleOriginal product titlePortable Camping Lantern...
    Attribute NameattributeNameExtracted attribute categoryScene Word
    Attribute ValueattributeValueExtracted attribute valueOutdoor / Camping
    PricepriceProduct price29.99
    Monthly SalesmonthlySalesUnitsMonthly unit sales1200
    Monthly RevenuemonthlySalesRevenueMonthly sales revenue35988
    RatingratingProduct rating4.5
    Rating CountratingsNumber of ratings3820
    Available DateavailableDateListing date2024-03-15
    BrandbrandBrand nameBrandX
    Image URLimageUrlMain product imagehttps://...

    Response Fields -- Attribute Groups

    FieldAPI NameDescription
    Attribute NameattributeNameThe attribute category for this group (e.g., "Scene Word")
    Attribute ValueattributeValueA specific value within the group (e.g., "Outdoor")
    CountcountNumber of products sharing this attribute value
    ASIN ListasinsList of ASINs that share this attribute value

    Response Metadata

    FieldAPI NameDescription
    Render TypetypeUI rendering style
    ColumnscolumnsColumn definitions for table rendering
    Source TypesourceTypeData source type
    Token CostcostTokenTotal 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)

    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

    ValueBehaviorWhen to Use
    MULTIPLE_RECORDSEach attribute value becomes its own record (default)Most analysis -- easier to count, sort, and group
    COMMA_SEPARATEDMultiple values stay in one record, comma-separatedWhen you want to see all attributes per ASIN at a glance

    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

    User SaysScenario
    "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.

    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：本工具会消耗积分；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。

    解决认证和积分问题

    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
    • 响应提示积分或余额不足：消息含"积分余额不足/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。

    积分消耗规则

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

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

    Frequently asked questions

    What to verify before installation and use

    What does the linkfox-product-title-analyze source document cover?

    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.

    How do I install linkfox-product-title-analyze?

    The source record exposes this install command: npx skills add https://github.com/linkfox-ai/linkfox-skills --skill "skills/linkfox-product-title-analyze". Inspect the command and pinned source before running it.

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