Hugging Face Tokenizers
Orchestra-Research/AI-Research-SKILLs
Shows how to load, train and use fast Hugging Face tokenizers, with BPE, WordPiece and Unigram models, padding, truncation and alignment tracking.
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…
Install Linkfox Product Title Analyze
$ npx skills add linkfox-ai/linkfox-skills --skill linkfox-product-title-analyze -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install linkfox-ai/linkfox-skills linkfox-product-title-analyze --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/linkfox-ai/linkfox-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/linkfox-product-title-analyze .claude/skills/linkfox-product-title-analyze && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "linkfox-product-title-analyze" agent skill from https://github.com/linkfox-ai/linkfox-skills/tree/main/skills/linkfox-product-title-analyze into .claude/skills/linkfox-product-title-analyze/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "linkfox-product-title-analyze", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/linkfox-ai/linkfox-skills/tree/main/skills/linkfox-product-title-analyzeType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add linkfox-ai/linkfox-skills --skill linkfox-product-title-analyze -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install linkfox-ai/linkfox-skills linkfox-product-title-analyze --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/linkfox-ai/linkfox-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/linkfox-product-title-analyze .agents/skills/linkfox-product-title-analyze && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "linkfox-product-title-analyze" agent skill from https://github.com/linkfox-ai/linkfox-skills/tree/main/skills/linkfox-product-title-analyze into .agents/skills/linkfox-product-title-analyze/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "linkfox-product-title-analyze", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add linkfox-ai/linkfox-skills --skill linkfox-product-title-analyze -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install linkfox-ai/linkfox-skills linkfox-product-title-analyze --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/linkfox-ai/linkfox-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/linkfox-product-title-analyze .cursor/skills/linkfox-product-title-analyze && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "linkfox-product-title-analyze" agent skill from https://github.com/linkfox-ai/linkfox-skills/tree/main/skills/linkfox-product-title-analyze into .cursor/skills/linkfox-product-title-analyze/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "linkfox-product-title-analyze", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/linkfox-ai/linkfox-skills.git --path skills/linkfox-product-title-analyze--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add linkfox-ai/linkfox-skills --skill linkfox-product-title-analyze -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install linkfox-ai/linkfox-skills linkfox-product-title-analyze --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/linkfox-ai/linkfox-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/linkfox-product-title-analyze .gemini/skills/linkfox-product-title-analyze && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "linkfox-product-title-analyze" agent skill from https://github.com/linkfox-ai/linkfox-skills/tree/main/skills/linkfox-product-title-analyze into .gemini/skills/linkfox-product-title-analyze/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "linkfox-product-title-analyze", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install linkfox-ai/linkfox-skills linkfox-product-title-analyzeInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add linkfox-ai/linkfox-skills --skill linkfox-product-title-analyze -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/linkfox-ai/linkfox-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/linkfox-product-title-analyze .github/skills/linkfox-product-title-analyze && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "linkfox-product-title-analyze" agent skill from https://github.com/linkfox-ai/linkfox-skills/tree/main/skills/linkfox-product-title-analyze into .github/skills/linkfox-product-title-analyze/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "linkfox-product-title-analyze", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add linkfox-ai/linkfox-skills --skill linkfox-product-title-analyze -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install linkfox-ai/linkfox-skills linkfox-product-title-analyze --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/linkfox-ai/linkfox-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/linkfox-product-title-analyze .opencode/skills/linkfox-product-title-analyze && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "linkfox-product-title-analyze" agent skill from https://github.com/linkfox-ai/linkfox-skills/tree/main/skills/linkfox-product-title-analyze into .opencode/skills/linkfox-product-title-analyze/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "linkfox-product-title-analyze", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
linkfox-product-title-analyze- GitHub stars
- 107
- Token cost
- ~2k tokens
- SKILL.md length
- 832 words
- Files
- 5 (incl. scripts, references)
- Skills in repo
- 177
- Repo updated
- First seen
- Licence
- MIT
At a glance
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…
- Works in 6 steps: Present data in tables: Show extracted… → Highlight top keywords: Call out the… → Group summary first: When… → …
- Tasks that involve Natural language processing
- SKILL.md covers Core Concepts, Data Fields, Parameter Guide and Display Rules, plus 5 more sections
- Runs Python scripts from its folder; calls python; needs LINKFOX_AGENT_API_KEY and LINKFOXAGENT_API_KEY
What it does
Linkfox Product Title Analyze is an agent skill from linkfox-ai/linkfox-skills. 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。
Its SKILL.md is about 2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including scripts and reference files (for example `references/api.md`, `references/onboarding.md` and `scripts/onboarding.py`).
It sits in AI & LLM Engineering, covering Natural language processing. The licence is MIT.
When your agent uses it
- Tasks that involve Natural language processing
Example prompts
- “/linkfox-product-title-analyze”
Requirements
- Python 3
- A credential in LINKFOX_AGENT_API_KEY
- A credential in LINKFOXAGENT_API_KEY
Workflow steps
6 steps, taken from the first numbered list in SKILL.md.
- 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…
What it can do on your machine
Read from SKILL.md and the folder at commit 38fef04. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 2 files in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
python
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names these keys or tokens, usually read from environment variables:
LINKFOX_AGENT_API_KEYLINKFOXAGENT_API_KEY
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Linkfox Product Title Analyze loads about 2k tokens when it runs, and up to ~3.8k if it reads all its reference files. Until then it costs about 96 tokens; SKILL.md has 832 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
SKILL.md
The full file from linkfox-ai/linkfox-skills at commit 38fef04, republished under its MIT licence (© linkfox-ai). 832 words, ~2,015 tokens.
Download SKILL.mdSave it as
.claude/skills/linkfox-product-title-analyze/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.- name
- linkfox-product-title-analyze
- description
- 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。
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)
Count scene words appearing in product titlesAudience / target-user words (who the product is for)
Count audience words appearing in product titlesMaterial words
Count material-related words appearing in product titlesFunction / feature words
Count function or feature words appearing in product titlesIncorrect -- multiple dimensions in one request (do NOT do this)
Count scene words AND audience words in product titlesSplit 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 |
Show full SKILL.md (330 more words)Show less
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
attributeGroupsis 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.
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。
© linkfox-ai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 4 other files (scripts, references) in skills/linkfox-product-title-analyze of linkfox-ai/linkfox-skills.
- SKILL.md
- references/api.md
- references/onboarding.md
- scripts/onboarding.py
- scripts/title_analyze.py
Open the folder on GitHubat commit 38fef04
Compare with similar skills
Linkfox Product Title Analyze next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Linkfox Product Title Analyze this skilllinkfox-ai/linkfox-skills | 107 | — | ~2k | Automated safety check: Pass | MIT | |
| Hugging Face TokenizersOrchestra-Research/AI-Research-SKILLs | 13k | 6 repos | ~3.4k | Automated safety check: Pass | MIT | |
| OpenMed Model Card Writermaziyarpanahi/openmed | 5.5k | — | ~1.8k | Automated safety check: Pass | Apache-2.0 | |
| Gptqmodel Tokenizer NormalizationModelCloud/GPTQModel | 1.3k | — | ~1.1k | Automated safety check: Pass | Custom licence | |
| Andrej KarpathyK-Dense-AI/mimeo | 282 | — | ~1.9k | Automated safety check: Pass | MIT | |
| Comparetaishi-i/awesome-japanese-nlp-resources | 1k | — | ~4.1k | Automated safety check: Notes | CC0-1.0 |
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通过亚马逊前台的 Alexa 购物助手发起自然语言问答，获取与问题相关的导购回答、推荐商品分组、ASIN 列表，以及可继续追问的问题。每次调用仅支持 1 条 prompt，如需追问须由 agent 总结上下文后拼接新问题发起新请求。可用 url 补充亚马逊页面上下文。当用户提到亚马逊 Alexa、Alexa 购物助手、亚马逊智能助手、AI…
Linkfox Amazon Opportunity Search By Metrics
linkfox-ai/linkfox-skills
亚马逊反向选品：基于历史商业洞察报告沉淀的指标数据池，按 30+ 项商业维度（市场规模与增长、价格区间与档位份额、竞争密度与头部集中度、人群画像如年龄/性别/收入、评论卖点与痛点等）反向筛选亚马逊赛道与关键词。当用户提到反向选品、指标筛选、细分市场反查、蓝海赛道挖掘、低竞争赛道、新人友好赛道、品牌分散市场、痛点切入、卖点反查、定价档位机会、人群画像选品、Amazon niche reverse…
Linkfox Amazon Product Detail
linkfox-ai/linkfox-skills
通过ASIN获取亚马逊商品详细信息，包括标题、图片、五点描述、规格参数、A+页面、价格、评分评论、变体等；可在取得原始HTML时尝试提取Item Highlights（商品亮点）。当用户提到亚马逊商品详情、ASIN查询、商品页面数据、Listing分析、五点描述提取、Item…
Linkfox Amazon Reviews List
linkfox-ai/linkfox-skills
按ASIN获取并分析亚马逊商品评论，支持15个站点(含美国站)，按星级筛选评论。当用户提到亚马逊评论、美国站评论、商品评价、买家投诉、差评、好评、星级评分、评论分析、评论情感、产品改良建议、Vine评论、已验证购买评论、竞品评论研究、Amazon reviews, US reviews, Amazon.com reviews, product feedback, negative review…
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Questions about Linkfox Product Title Analyze
What does Linkfox Product Title Analyze do?
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…. Linkfox Product Title Analyze is an agent skill from linkfox-ai/linkfox-skills.
When should I use Linkfox Product Title Analyze?
Linkfox Product Title Analyze fits situations like: tasks that involve Natural language processing.
How do I install Linkfox Product Title Analyze in Claude Code?
Run `npx skills add linkfox-ai/linkfox-skills --skill linkfox-product-title-analyze -a claude-code`. Or copy the skill folder (skills/linkfox-product-title-analyze in linkfox-ai/linkfox-skills) into .claude/skills/linkfox-product-title-analyze in your project. Claude Code loads it when a task matches its description.
How do I install Linkfox Product Title Analyze in Codex?
Run `npx skills add linkfox-ai/linkfox-skills --skill linkfox-product-title-analyze -a codex`. Or copy the skill folder (skills/linkfox-product-title-analyze in linkfox-ai/linkfox-skills) into .agents/skills/linkfox-product-title-analyze in your project. Codex loads it when a task matches its description.
Can I use Linkfox Product Title Analyze in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add linkfox-ai/linkfox-skills --skill linkfox-product-title-analyze -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/linkfox-product-title-analyze, .gemini/skills/linkfox-product-title-analyze, .github/skills/linkfox-product-title-analyze and .opencode/skills/linkfox-product-title-analyze in your project.
What does Linkfox Product Title Analyze need to run?
Going by SKILL.md and its folder, Linkfox Product Title Analyze needs Python for the scripts in its folder, the command-line tools its instructions call (python) and credentials named LINKFOX_AGENT_API_KEY and LINKFOXAGENT_API_KEY. Our summary lists: Python 3; A credential in LINKFOX_AGENT_API_KEY; A credential in LINKFOXAGENT_API_KEY.
Does Linkfox Product Title Analyze access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Linkfox Product Title Analyze safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Linkfox Product Title Analyze use?
Linkfox Product Title Analyze is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Linkfox Product Title Analyze use?
About 2k tokens (SKILL.md is roughly 8.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 1.7k tokens, read only when the agent opens those files.
What are the alternatives to Linkfox Product Title Analyze?
Skills that share tags, products or a category with Linkfox Product Title Analyze: Hugging Face Tokenizers (Orchestra-Research/AI-Research-SKILLs, 13k stars), OpenMed Model Card Writer (maziyarpanahi/openmed, 5.5k stars), Gptqmodel Tokenizer Normalization (ModelCloud/GPTQModel, 1.3k stars) and Andrej Karpathy (K-Dense-AI/mimeo, 282 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Linkfox Product Title Analyze?
linkfox-ai (a GitHub user) maintains it in linkfox-ai/linkfox-skills, which has 107 GitHub stars. The repository holds 177 skills in this directory. The repository was last updated on September 14, 2026.
Source: linkfox-ai/linkfox-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.