Token导航 LogoToken导航

Agent Skill · 研究检索

brand-voice-extractor(品牌语音提取器)

用于辅助音频、音乐、语音转写、语音合成或声音素材处理。它适合让 Agent 生成配乐说明、整理音频流程、调用语音工具或处理播客和视频配音素材。使用时需要确认输入音频来源、输出格式、时长和模型限制;涉及人声克隆、版权音乐或公开发布时,应先核对授权和合规边界。

brand-voice-extractor 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为需要联网;许可证信息还不明确,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 66,724。

总安装 259周安装 11GitHub Stars 607下载 91核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:brand-voice-extractor(品牌语音提取器)
来源仓库:https://github.com/athina-ai/goose-skills
仓库路径:skills/brand-voice-extractor
安装命令:
npx skills add https://github.com/athina-ai/goose-skills --skill brand-voice-extractor
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/athina-ai/goose-skills --skill brand-voice-extractor

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

分析企业发布的内容以提取品牌语调与写作风格指南。适合市场团队统一对外沟通语气,提升内容一致性与识别度。支持从博客、官网或指定 URL 源自动抓取文本进行风格建模。

安装命令:npx skills add https://github.com/athina-ai/goose-skills --skill brand-voice-extractor 需提供公司名及内容源链接,确保输入材料具有代表性。

来源仓库内路径:skills/brand-voice-extractor

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/athina-ai/goose-skills --skill brand-voice-ext…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

brand-voice-extractor 怎么安装?

在终端执行 npx skills add https://github.com/athina-ai/goose-skills --skill brand-voice-extractor。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

brand-voice-extractor 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

brand-voice-extractor 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

brand-voice-extractor 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

brand-voice-extractor 用的人多吗?

公开数据是累计安装 259 次,近一周安装 11 次,下载 91 次,GitHub 607 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

brand-voice-extractor 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
brand-voice-extractor

品牌语音提取器 · 本页

259607需要联网未明确有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Brand Voice Extractor

Analyze a company's published content to extract their brand voice and writing style. Reads their top content pieces and produces actionable guidelines for matching their voice in future content, outreach, or campaigns.

Quick Start

Extract brand voice for [company]. Use their blog at [url].

Or with content already cataloged:

Extract brand voice for [client]. Use the content inventory at clients/[client]/research/content-inventory.json.

Inputs

InputRequiredSource
Content URLsYesUser provides, or pulled from site-content-catalog output
Company nameYesFor context in the analysis
Number of pagesNoDefault: 15. How many pages to analyze.

Process

Phase 1: Select Content to Analyze

If content URLs are provided directly, use those. Otherwise:

  1. Read the content inventory from site-content-catalog output
  2. Select a diverse sample of 10-20 pages, prioritizing:

- Blog posts (primary voice indicator) - Landing pages (marketing voice) - Case studies (storytelling voice) - Mix of recent and older content (to detect voice evolution) - Mix of topics (to see consistency across subjects)

Selection heuristic:

  • 8-10 blog posts (mix of how-to, opinion, product updates)
  • 2-3 landing pages (homepage, product page, solutions page)
  • 2-3 case studies or customer stories (if available)
  • 1-2 comparison/vs pages (if available)

Phase 2: Fetch and Extract Text

For each selected URL:

  1. WebFetch the page
  2. Extract the main content body (strip nav, footer, sidebar)
  3. Store: title, URL, raw text, word count

Phase 3: Analyze Voice Dimensions

Analyze across these dimensions:

A) Tone
  • Formality spectrum: Casual ↔ Professional ↔ Academic
  • Emotional register: Excited ↔ Measured ↔ Dry
  • Authority stance: Peer/friend ↔ Expert/teacher ↔ Institution
  • Humor usage: Frequent ↔ Occasional ↔ None
  • Directness: Direct/bold ↔ Hedged/diplomatic
B) Vocabulary & Language
  • Reading level: Approximate grade level (simple vs. complex)
  • Jargon usage: Heavy industry jargon ↔ Plain language
  • Technical depth: Assumes expertise ↔ Explains everything
  • Power words: Common persuasion/action words they favor
  • Banned patterns: Words or phrases they conspicuously avoid
  • Unique vocabulary: Distinctive terms or phrases they use repeatedly
C) Sentence Structure
  • Average sentence length: Short/punchy ↔ Long/complex
  • Paragraph length: 1-2 sentences ↔ 3-4 ↔ 5+
  • Opening patterns: How they start articles (question, stat, story, bold claim)
  • Transition style: How they connect ideas
  • Use of fragments: Do they use incomplete sentences for emphasis?
D) Formatting Patterns
  • Headers: Frequency, style (question-based, how-to, numbered)
  • Lists: Bullets vs. numbered, frequency
  • Bold/italic: How they use emphasis
  • Images/media: Frequency, types (screenshots, illustrations, photos)
  • CTAs: Placement, style, frequency, language used
  • Pull quotes/callouts: Do they use them?
E) Content Structure
  • Typical article length: Short (<800), Medium (800-1500), Long (1500+)
  • Introduction style: Hook type, length
  • Conclusion style: Summary, CTA, open question
  • Use of data/stats: Frequent ↔ Rare
  • Use of examples: Frequent ↔ Rare
  • Storytelling: Narrative-driven ↔ Information-driven
F) Persona & Audience
  • Who they write for: Inferred target reader (role, seniority, industry)
  • Assumed knowledge level: Beginner ↔ Intermediate ↔ Expert
  • Point of view: First person singular (I) ↔ First person plural (we) ↔ Second person (you) ↔ Third person
  • Reader relationship: Peer ↔ Teacher ↔ Service provider

Phase 4: Generate Brand Voice Profile

Produce a Markdown document with this structure:

# Brand Voice Profile: [Company Name]
**Analyzed:** [Date] | **Content pieces analyzed:** [N]
**Sources:** [list of URLs analyzed]

---

## Voice Summary (2-3 sentences)

[Company] writes in a [tone] voice that [description]. Their content targets
[audience] and assumes [knowledge level]. The overall feel is [adjectives].

---

## Tone Profile

| Dimension | Position | Evidence |
|-----------|----------|----------|
| Formality | [e.g., Professional-casual] | [Example quote] |
| Emotional Register | [e.g., Measured, occasionally excited] | [Example] |
| Authority | [e.g., Expert/teacher] | [Example] |
| Humor | [e.g., Rare, dry when used] | [Example] |
| Directness | [e.g., Very direct, bold claims] | [Example] |

---

## Language & Vocabulary

### Reading Level
[Grade level estimate and what that means]

### Signature Phrases
- "[phrase 1]" — used frequently to [purpose]
- "[phrase 2]" — recurring pattern in [context]

### Jargon & Technical Depth
[How much industry jargon they use, how they handle technical concepts]

### Words They Love
[List of frequently used power words, adjectives, verbs]

### Words They Avoid
[Notable absences or patterns they steer away from]

---

## Structure & Formatting

### Typical Article Structure
[Outline of how their articles are typically organized]

### Sentence & Paragraph Style
- Average sentence length: [X words]
- Typical paragraph: [X sentences]
- Notable patterns: [fragments, rhetorical questions, etc.]

### Formatting Habits
- Headers: [style]
- Lists: [frequency and style]
- Emphasis: [bold/italic patterns]
- CTAs: [where, how often, what language]

---

## Audience & Persona

### Target Reader
[Role, seniority, industry, pain points they address]

### Knowledge Assumptions
[What they assume the reader already knows]

### Point of View
[I/we/you usage and what it signals]

---

## Writing Guidelines (Actionable)

Use these guidelines when writing content, outreach, or campaigns for [Company]:

### Do
- [Guideline 1 with example]
- [Guideline 2 with example]
- [Guideline 3 with example]

### Don't
- [Anti-pattern 1]
- [Anti-pattern 2]
- [Anti-pattern 3]

### Voice Samples

**Their style:**
> [2-3 representative quotes from their content that exemplify the voice]

**How to match it:**
> [2-3 example sentences written in their voice about a neutral topic]

Tips

  • 15 pages is the sweet spot. Fewer than 10 won't capture enough variation. More than 25 adds cost without much signal.
  • Blog posts are the best voice signal. Landing pages are more formulaic. Blog posts show the authentic voice.
  • Look for consistency AND inconsistency. If their tone shifts dramatically between content types, note it — they may have multiple voice modes.
  • Check for ghost-written content. If some posts feel dramatically different, they may use external writers. Flag this in the analysis.
  • This skill has no code script. It's an agent-executed skill — the AI agent reads the content via WebFetch and performs the analysis directly. The structured output template above guides the analysis.

Dependencies

  • Web fetch capability (for reading content pages)
  • Optional: site-content-catalog output (for selecting which content to analyze)
  • No API keys or paid tools required

平台分布

Codex

36.22%
按下载量换算33

Claude

30.66%
按下载量换算28

Cursor

18.78%
按下载量换算17

Gemini CLI

9.85%
按下载量换算9

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

继续浏览同类 Skills