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Agent Skill · 开发

voice-extractor(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。

voice-extractor 是开发类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为只读;许可证信息还不明确,第三方扫描有提醒项,按安装量在开发类的 21,746 个 Skill 里排第 11,473。

总安装 1,747周安装 70GitHub Stars 261下载 566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:voice-extractor(语音提取器)
来源仓库:https://github.com/brianrwagner/ai-marketing-claude-code-skills
仓库路径:skills/voice-extractor
安装命令:
npx skills add https://github.com/brianrwagner/ai-marketing-claude-code-skills --skill 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/brianrwagner/ai-marketing-claude-code-skills --skill voice-extractor

这个 Skill 是做什么的

开发类 Skill 面向写代码、改代码和排查问题,一般会读写仓库文件,部分还会执行构建或测试命令。

用于辅助音频处理与语音素材管理,支持配乐生成、流程整理和播客视频配音。适合处理语音转写、合成及声音提取任务,可调用专业工具链。通过 GitHub 安装后集成到 Claude、Cursor 等 AI 宿主环境使用。需确认音频来源合规性,注意版权音乐与人声克隆的授权边界。

voice-extractor 属于开发类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/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/brianrwagner/ai-marketing-claude-code-skills -…;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

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

voice-extractor 怎么安装?

在终端执行 npx skills add https://github.com/brianrwagner/ai-marketing-claude-code-skills --skill voi…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

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。

voice-extractor 需要什么权限?

权限级别是只读。该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

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。

voice-extractor 用的人多吗?

公开数据是累计安装 1,747 次,近一周安装 70 次,下载 566 次,GitHub 261 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。

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。
  • 标注为只读权限,试用阶段不容易误改本地文件。

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。
  • 需要处理开发类任务、又不想每次都手写提示词的人。
  • 只想先试试效果、不希望它改动本地文件的人。

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。

开发类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
skill-vetter

技能审查员 · OpenClaw

8,779,018986操作浏览器MIT-0通过
ontology

本体论 · OpenClaw

6,636,960576只读MIT-0通过
entra-app-registration

内部应用程序注册 · Codex、Claude

6,631,872764接触敏感数据未明确通过
entra-app-registration

内部应用程序注册 · Codex、Claude

3,757,680201接触敏感数据未明确通过
nano-banana-pro

纳米香蕉专业版 · OpenClaw

3,526,898364接触敏感数据MIT-0有提醒
voice-extractor

语音提取器 · 本页

1,747261只读未明确有提醒

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

Voice Extractor

AI-generated content all sounds the same. The fix isn't better prompts — it's teaching the AI how you actually communicate.

This skill extracts your communication DNA from writing samples and produces a Voice Guide: documented, tested, and ready to use.


Mode

Detect from context or ask: *"Quick voice snapshot, full Voice Guide, or full guide with examples?"*

ModeWhat you getBest for
quickTop 5 voice characteristics + 3 do/don't rulesFast style reference, single piece
standardFull Voice Guide: tone, vocabulary, rhythm, structureAI training, ghostwriting, brand documentation
deepFull Voice Guide + 10 sample rewrites + writing rules checklist + AI training examplesOnboarding writers, building a brand voice system

Default: standard — use quick if they just need a fast reference. Use deep if they're onboarding a ghostwriter or building a content team.


Context Loading Gates

Before extracting, collect:

  • Writing samples — minimum 3 samples OR 500 total words (see priority list below)
  • Purpose of voice guide — AI training? Ghostwriter onboarding? Team alignment?
  • Confidence zones — Any topics where they want to sound more/less authoritative?
  • Known anti-patterns — Any words or phrases they already know they want to avoid?

Sample priority (most → least authentic):

  1. Casual Slack or email (raw, unedited voice)
  2. Podcast or call transcript
  3. LinkedIn posts or articles
  4. Website copy (often edited, less authentic)

Minimum sample gate: If samples total under 500 words, stop:

"These samples are too short to extract reliable patterns. Please add 2-3 more — emails, Slack messages, or transcripts work best. The messier and more casual, the better."

Do not attempt full extraction from under 500 words. Offer quick mode instead.


Phase 1: Sample Quality Assessment

Before extracting, reason through:

  1. Sample authenticity: Are these samples from edited/polished contexts (website, press) or raw contexts (Slack, email)? More polish = less authentic voice.
  2. Sample variety: Do the samples cover different contexts (professional, casual, educational)? Single-context samples produce single-dimension voice guides.
  3. Exclusion check: Identify and flag patterns that are NOT the authentic voice:

- Platform formatting tics (LinkedIn line breaks, Twitter brevity forcing) - Typos and autocorrect errors - Phrases borrowed from others (quotes, retweets) - Unusually formal writing (legal docs, press releases)

  1. Sample size adequacy: Is there enough material for full mode, or should I use quick mode?

Output a sample assessment:

"I have [X samples / Y words] to work with. Quality: [high/medium — why]. I'll use [full/quick] mode. Excluding: [any patterns and why]."

Phase 2: Core Energy Extraction

Identify the fundamental communication mode:

Role:

  • Teacher (breaks things down systematically)
  • Challenger (pushes back on assumptions)
  • Cheerleader (builds confidence and momentum)
  • Straight-shooter (cuts through BS efficiently)

Default energy:

  • Calm authority ("Here's what works.")
  • High enthusiasm ("This is exciting — let me show you.")
  • Understated confidence ("I've seen this a hundred times.")

Recurring themes: What topics appear unprompted across samples? These are the things they actually care about.


Phase 3: Phrase Extraction (Systematic)

Scan all samples and extract:

Transition phrases (how they shift topics):

  • Quote exact examples from samples
  • Pattern: "Here's the thing...", "What I've learned...", "Let me put it differently..."

Emphasis phrases (how they land a point):

  • Quote exact examples
  • Pattern: "The reality is...", "This is the part people miss...", "Here's the actual problem..."

Closers (how they wrap up):

  • Quote exact examples
  • Pattern: "That's the move.", "Start there.", "You've got this."

Phase 4: Confidence Zone Mapping

ZoneDescriptionLanguage Markers
Full authorityTopics they're an expert inNo hedging, definitive statements, "here's what works"
Earned perspectiveTopics with experience but not mastery"In my experience...", "What I've found..."
Active explorationTopics they're learning now"I'm testing this...", "What I'm seeing..."

Map their stated expertise areas to each zone. This calibration is what makes the voice feel real vs. one-dimensional.


Phase 5: Anti-Pattern Documentation

Extract what they'd NEVER say:

  • Words that would feel wrong in their voice
  • Phrases that make them cringe
  • Tones they naturally avoid
  • Industry jargon they hate

Source these from sample evidence where possible: "You never used [word] across [X samples] — it doesn't fit your voice."


Phase 6: Validation Test (REQUIRED)

After extracting the full profile, generate 2 test sentences on the same topic:

Version A (using the extracted voice profile):

"[Sample sentence in their voice]"

Version B (wrong voice — contrasting example):

"[Same content, different voice — shows what to avoid]"

Ask the user: "Does Version A actually sound like you when you're not overthinking it? What feels off?"

This validation catches extraction errors before the guide is put into production.


Quick Mode (--quick)

When samples are thin (300–500 words) or time is short:

  1. Read 3 samples fast
  2. Pull 10 signature phrases
  3. Note 3 things they'd never say
  4. Write 1 sentence describing their energy

Output: Minimum viable voice guide.

Difference from full mode:

  • Quick: ~10 phrases, 3 anti-patterns, 1-sentence energy descriptor
  • Full: Complete profile with confidence calibration, validated test sentences, and source-cited examples

Phase 7: Self-Critique Pass (REQUIRED)

After generating the Voice Guide:

  • Are the extracted phrases actually from the samples, or am I inferring them?
  • Does the anti-pattern list include specific words/phrases, or just vague categories?
  • Do the validation test sentences demonstrate a real difference between in-voice and out-of-voice?
  • Is the confidence zone mapping specific to named topics, or just generic?
  • Would a ghostwriter be able to use this guide without asking follow-up questions?

Flag any issues: "The anti-pattern section only has 2 entries — not enough for a usable guide. I need more samples or direct input from the user."


Output Structure

## Voice Guide: [Name] — [Date]

### Sample Assessment
- Samples: [count, types]
- Total words: [count]
- Quality: [high/medium — reason]
- Mode: [quick/full]
- Excluded: [patterns excluded + why]

---

### Core Energy
- Role: [teacher/challenger/cheerleader/straight-shooter]
- Default energy: [description]
- Recurring themes: [list]

### Signature Phrases
**Transitions:**
- "[Phrase]" (source: [email/post])
- "[Phrase]"

**Emphasis:**
- "[Phrase]" (source: [email/post])

**Closers:**
- "[Phrase]"

### Confidence Calibration
**Full authority (no hedging):**
Topics: [list]
Sounds like: "[example sentence]"

**Earned perspective:**
Topics: [list]
Sounds like: "[example sentence]"

**Active exploration:**
Topics: [list]
Sounds like: "[example sentence]"

### Anti-Patterns (Never Use)
- [Word/phrase] — why: [evidence from samples]
- [Word/phrase] — why: [evidence]

### Validation Test
**This sounds like you:**
"[Version A]"

**This doesn't:**
"[Version B — contrast]"

### Self-Critique Notes
[Any gaps, things to validate with user]

### Usage Instructions
- For AI: Paste this guide into your system prompt
- For ghostwriter: Share on day 1 — cuts revision cycles in half
- For team: This is the benchmark for "on brand"

*Skill by Brian Wagner | AI Marketing Architect | brianrwagner.com*

平台分布

Codex

37.49%
按下载量换算212

Claude

29.86%
按下载量换算169

Cursor

19.37%
按下载量换算110

Gemini CLI

8.73%
按下载量换算49

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

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。

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