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voice-extractor语音提取器

Agent Skill

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。

总安装

1,747

周安装

70

GitHub Stars

261

下载量

566
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

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

简介

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。

SKILL.md

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*

适合场景

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用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

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能力 2

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能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

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

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