Token导航 LogoToken导航

Agent Skill · AI 工具

speech-adaptation(言语适应)

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。

speech-adaptation 是AI 工具类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为只读;许可证为 MIT,第三方扫描已通过,按安装量在AI 工具类的 2,536 个 Skill 里排第 419。

总安装 5,433周安装 231GitHub Stars 69下载 1,903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
MIT

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。

命令行安装

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

这个 Skill 是做什么的

AI 工具类 Skill 用来扩展助手本身的能力,例如接模型、接检索、接外部服务。

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。

来源仓库内路径:skills/speech-adaptation

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/jwynia/agent-skills --skill speech-adaptation;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

speech-adaptation 怎么安装?

在终端执行 npx skills add https://github.com/jwynia/agent-skills --skill speech-adaptation。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

speech-adaptation 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。

speech-adaptation 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。

speech-adaptation 是免费的吗?

来源标注的许可证是 MIT。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

speech-adaptation 用的人多吗?

公开数据是累计安装 5,433 次,近一周安装 231 次,下载 1,903 次,GitHub 69 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。

speech-adaptation 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

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。
  • 3 个目录同时收录,说明这个 Skill 流传比较广。
  • 许可证明确为 MIT,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 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AI 工具类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

AI 工具类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
executing-plans

执行计划 · Codex、Claude

1,515,336173,110只读MIT通过
verification-before-completion

完成前的验证 · Codex、Claude

1,319,472173,121权限未标注MIT通过
subagent-driven-development

子 Agent 驱动开发 · Codex、Claude

1,312,416173,138只读MIT通过
copy-editing

复制编辑 · Codex、Claude

1,209,57625,632只读MIT通过
paywall-upgrade-cro

付费专区升级 cro · Codex、Claude

1,030,17625,617只读MIT通过
speech-adaptation

言语适应 · 本页

5,43369只读MIT通过

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

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

平台分布

Claude Code

27.75%
按下载量换算528

OpenCode

23.28%
按下载量换算443

Antigravity

17.89%
按下载量换算340

Codex

13.66%
按下载量换算260

Gemini CLI

7.41%
按下载量换算141

windsurf

3.75%
按下载量换算71

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

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。

继续浏览同类 Skills