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speech-adaptation言语适应

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。

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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

3

许可证

MIT

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:speech-adaptation(言语适应)
来源仓库:https://github.com/jwynia/agent-skills
仓库路径:skills/speech-adaptation
安装命令:
npx skills add https://github.com/jwynia/agent-skills --skill speech-adaptation
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。

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复制命令到本机终端执行。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/jwynia/agent-skills --skill speech-adaptation

简介

speech-adaptation 用于辅助音频、音乐、语音转写、语音合成或声音素材处理,适合在 Codex、Claude、Cursor、Gemini CLI 中生成配乐说明或整理音频流程。

  • 适用于播客、视频配音或语音工具集成的场景。
  • 使用时需确认输入音频来源、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。
  • 建议保留人工审核环节以确保内容合法合规。

SKILL.md

Speech Adaptation

Purpose

Transform comprehensive written content into purposeful spoken guidance. Speech requires 3-5x compression while maintaining functional value. Apply when converting written content to audio, podcasts, presentations, or voice assistant responses.

Core Principle

Lead with value, earn attention. Listeners can't skim. Front-load what matters and offer expansion rather than exhaustive delivery.


Functional Intent Detection

Parse the original question/content for intent:

Intent TypeSignalsFocus
Problem-solving"How do I..."Actionable steps
Learning"What is..."Core concepts + examples
Decision-making"Should I..."Key considerations + recommendation
Troubleshooting"Why isn't..."Likely causes + solutions

Context Signals

Signal TypeExamplesAdaptation
Urgency"today", "now", "urgent"Compress to immediate next steps
Scope"huge", "complex", "overwhelming"Lead with simplification
Experience"beginner", "new to"Increase explanation, decrease jargon
Personal stakes"I", "my project"Increase specificity, decrease abstraction

Content Transformation Principles

1. Hierarchical Restructuring

Written: Lists methods 1-7 equally Spoken: "There are three main approaches. Start with [most relevant]. If that doesn't work, try [backup]."

2. Front-Load Value

Written: Builds up to key insights Spoken: Lead with core insight, then supporting details if needed

3. Compress Conceptual Space

Written: Seven distinct frameworks Spoken: "Basically three strategies: sort by importance, limit your focus, or batch similar work"

4. Context-Dependent Detail

Written: Explains everything at same depth Spoken: Start simple, indicate where more detail is available

  • "Use a priority matrix - urgent versus important"
  • Optional expansion cue: "I can break down those four categories if helpful"

5. Eliminate Structural Artifacts

Remove in Speech:

  • Section headers read verbatim
  • Bullet point enumeration
  • Visual formatting cues
  • Redundant category labels

Add for Speech:

  • Transition phrases between ideas
  • Purpose statements before methods
  • Summary/recap statements

6. Progressive Revelation Strategy

  1. Core insight (one sentence)
  2. Primary recommendation (actionable step)
  3. Backup approach (if primary doesn't fit)
  4. Availability cue for additional methods

Implementation Guidelines

Pre-Processing Steps

  1. Parse original question for functional intent and context signals
  2. Identify 1-2 most relevant pieces for their specific need
  3. Determine appropriate compression ratio based on urgency/complexity

Content Selection Rules

ContextSelection
High urgency1 primary method + 1 backup
Learning focusedCore concept + 1 detailed example + availability of more
Decision supportKey considerations + clear recommendation
Complex topicSimplify conceptual framework first, offer detail expansion

Speech-Specific Adaptations

  • Replace structural language with functional language
  • Add explicit transitions between ideas
  • Use pronouns and referential terms to avoid repetition
  • Include "escape valves" for different user needs
  • End with clear next step or summary

Quality Checks

TestQuestion
CompressionIs this 30-50% of original length?
CompletenessDoes this answer their core question?
FlowWould this make sense heard linearly?
ActionDo they know what to do next?

Example Transformation

Question Type: Immediate problem-solving with overwhelm signals

Written Response: 7 methods with full explanations

Spoken Adaptation:

  1. Acknowledge state: "When facing a huge list..."
  2. Core insight: "The key is separating what needs doing from what feels urgent"
  3. Primary action: "Try this: scan for things both urgent AND important"
  4. Boundary setting: "Pick just 3 - more than that sets you up to feel behind"
  5. Escape valve: "Other approaches available if this doesn't click"

Success Metrics

  • User can act immediately after listening
  • Cognitive load feels manageable
  • Key insights retained after single hearing
  • Optional detail access feels natural when needed

Integration Points

Inbound:

  • From written documentation or articles
  • From comprehensive analysis outputs
  • From detailed framework content

Outbound:

  • To audio content production
  • To presentation delivery
  • To voice assistant responses

Complementary:

  • presentation-design: For visual + spoken coordination
  • dialogue: For conversational delivery patterns

Anti-Patterns

1. Uniform Compression

Pattern: Reducing all content by the same ratio regardless of importance. Why it fails: Not all content is equal. Some ideas need full explanation; others can be summarized in a phrase. Equal compression buries critical insights and pads trivial ones. Fix: Identify the 1-2 most important points. Protect those while ruthlessly compressing supporting material. Lead with what matters most.

2. Written Sentences Spoken

Pattern: Reading written prose aloud without restructuring for speech patterns. Why it fails: Written and spoken language have different rhythms, sentence structures, and information density. Written sentences spoken sound formal, awkward, and hard to follow. Fix: Restructure for oral delivery. Shorter sentences. More personal pronouns. Explicit transitions. Repetition for emphasis. Natural breathing points.

3. Exhaustive Completeness

Pattern: Including all information from the written source because "it might be important." Why it fails: Listeners can't skim, reread, or control pace. Information overload in speech creates immediate cognitive overload and retention collapse. Fix: Accept that spoken content is selective. Provide escape valves: "More on this if helpful." Trust that listeners can ask for expansion rather than front-loading everything.

4. Missing Signposts

Pattern: Moving between ideas without explicit verbal transitions. Why it fails: Listeners can't see paragraph breaks or headings. Without verbal signposts, ideas blur together. The structure becomes invisible. Fix: Add explicit transitions: "First..." "More importantly..." "Here's the key point..." "Moving on to..." Make the structure audible.

5. Buried Action

Pattern: Leaving actionable recommendations for the end after extensive context. Why it fails: Listeners who zone out during context miss the action items. Those still engaged have forgotten the details by the time recommendations arrive. Fix: Front-load action with context to follow. "Do X. Here's why..." rather than "Here's all the context, therefore do X."

Integration

Inbound (feeds into this skill)

SkillWhat it provides
prose-styleWritten content quality to work from
(written documentation)Source material for adaptation

Outbound (this skill enables)

SkillWhat this provides
presentation-designSpoken content structure for slide coordination
(audio production)Scripts ready for recording
(voice assistants)Responses optimized for spoken delivery

Complementary

SkillRelationship
presentation-designSpeech-adaptation handles the spoken component; presentation-design coordinates visual and spoken elements
dialogueSpeech-adaptation for informational delivery; dialogue for conversational and dramatic speech patterns

适合场景

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02

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03

需要对比不同来源的安装命令和来源信息时

04

需要参考平台分布和安装热度时

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能力 4

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能力 5

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