Voice Optimization

Community

kangarooking

voice-optimization

当系统提示面向语音交互场景（语音助手、语音搜索、有声回答、电话客服 AI）时调用。适用于需要将文本输出优化为口语表达的系统提示设计。不适用于纯文本聊天界面，不适用于语音合成（TTS）技术选型，不适用于图像/视频多模态场景。

Overview

Publisher	kangarooking
Repository	`system-prompt-skills`
Skill name	`voice-optimization`
Stars	213
Forks	45
Bundled files	Instructions only
License	MIT
Links	Skill source Repository

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by kangarooking on GitHub. Read the source before you install it.

Installation

Install the Voice Optimization AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/kangarooking/system-prompt-skills/tree/main/voice-optimization
Enable the skill in any chat where you want it available.

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/kangarooking/system-prompt-skills.git /tmp/system-prompt-skills
mkdir -p .claude/skills
cp -r /tmp/system-prompt-skills/voice-optimization .claude/skills/voice-optimization

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Voice Optimization in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Voice Optimization on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Voice Optimization is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

语音场景优化

R — 原文 (Reading)

Perplexity Voice 要求"请快速说话"、仅支持英语、禁止说话人识别、禁止唱歌哼唱、禁止模仿；Claude Mobile 强调"始终先给答案、无前言"、列表在小屏幕上更易扫描；Sesame AI Maya 专为语音优化的对话模式。核心模式：简洁优先、口语化适配、音频安全约束、语言限制、去除视觉格式。

I — 方法论骨架 (Interpretation)

简洁优先原则：语音场景中用户注意力窗口极短，回答必须开门见山，禁止"好的，让我来回答您的问题"等前言式表达。
口语化转换：将书面语转换为自然口语——使用短句、主动语态、日常词汇，避免从句嵌套和术语堆砌。
格式降级：去除 Markdown 表格、代码块、嵌套列表等视觉格式，改用自然语言描述或简短列举。
音频安全约束：禁止识别特定说话人身份、禁止模仿真实人物声线特征、禁止唱歌或哼唱旋律。
语言限制声明：明确支持的语种范围，超出范围时引导用户调整设置而非强行处理。
长度自适应：根据问题复杂度动态调整回答长度——简单问题一两句话，复杂问题控制在合理时长内。

A1 — 案例分析 (Past Application)

案例: Perplexity Voice 的音频安全边界

问题: 语音交互中用户可能要求模仿名人声音、识别通话对象、或要求 AI 唱歌，这些行为涉及隐私、版权和安全风险。
设计模式的使用: Perplexity Voice 在系统提示中设置明确禁区——不对语音输入进行说话人识别（"No speaker identification from voice"），不执行唱歌或哼唱请求，不进行人物模仿。同时限定仅支持英语，超出能力范围时引导用户修改设置。
结论: 语音场景有独特的安全边界（声纹、模仿、演唱），这些在纯文本场景中不存在，需要专项防护。

案例: Claude Mobile 的回答优先策略

问题: 移动端语音回答中，用户听到冗长的开场白会快速失去耐心，尤其在驾驶、行走等场景下。
设计模式的使用: Claude Mobile 明确指令"Always lead with answer. No preamble."，将答案前置，解释后置。对于不同复杂度的问题设定长度层级——简单问题 1-2 句，操作指南用短列表，实质性问题 2-3 段。
结论: 语音场景对延迟感知极度敏感，去除前言可显著提升用户满意度和信息获取效率。

A2 — 触发场景 (Future Trigger) ★

用户在什么情境下需要?

设计语音助手（如智能音箱、车载助手）的系统提示
为现有文本聊天机器人添加语音交互模式
构建电话客服 AI 的对话脚本
优化播客生成或有声内容合成中的口语表达

语言信号

"语音场景下的输出优化"
"需要口语化回答"
"用户通过语音提问"
"回答会被朗读出来"
"如何让 AI 说话更自然"

与相邻 skill 的区分

与 mobile-adaptation 区别：移动适配关注屏幕尺寸约束，语音优化关注听觉通道约束；但两者都强调简洁优先，常联合使用
与 citation-system 区别：引用系统在语音场景中需要特殊处理（无法使用视觉标记），但语音优化不涉及引用格式设计本身

E — 可执行步骤 (Execution)

步骤 1：设定回答长度层级 - 完成标准：为问题复杂度定义 3-4 个层级（简单/操作/中等/复杂），每个层级规定最大句数或预估朗读时长，并在系统提示中以示例说明。
步骤 2：编写前言禁令与答案前置规则 - 完成标准：明确声明"禁止在回答开头添加确认性前言"，提供正确和错误的示例对比（如错误："好的，让我为您解答..."，正确：直接给出答案）。
步骤 3：定义格式降级规则 - 完成标准：列出需降级的视觉格式（表格→自然语言描述、嵌套列表→扁平列举、代码块→口语化步骤说明），并给出每种降级的示例。
步骤 4：设定音频安全边界 - 完成标准：明确禁止的行为清单（说话人识别、声线模仿、唱歌哼唱、人物扮演），定义超出能力范围时的标准回退话术。
步骤 5：添加口语化转换指南 - 完成标准：列出书面语到口语的转换规则（从句→短句、被动→主动、术语→日常词汇），提供 3 个以上转换示例。

B — 边界 (Boundary) ★

不要在以下情况使用

纯文本聊天界面，用户通过键盘输入和屏幕阅读
语音合成（TTS）引擎的技术选型或参数调优
音频信号处理（降噪、回声消除等）
多模态场景中语音仅为辅助通道（如视频会议中的字幕场景）

常见失败模式

照搬文本输出：直接将文本聊天回复用于语音场景，导致冗长前言、视觉格式（表格、代码块）被朗读出来，用户体验极差
忽视音频独有风险：仅优化表达方式但未设置说话人识别、模仿等音频特有的安全边界
过度简化：将所有回答压缩为一句话，丢失必要信息和上下文，应按复杂度分级而非一刀切
忽略语言限制：未声明支持的语种范围，导致多语言场景下输出混乱或质量下降

Frequently asked questions

What does the Voice Optimization AI skill do?

Why use Voice Optimization on TypingMind?

Because you install it once and use it with any model. Voice Optimization is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Voice Optimization in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/kangarooking/system-prompt-skills/tree/main/voice-optimization. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Voice Optimization?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Voice Optimization?

As many as you like. As long as a model supports skills, you can use Voice Optimization with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Voice Optimization AI skill free?

Yes. It is published on GitHub by kangarooking under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

语音场景优化

R — 原文 (Reading)

I — 方法论骨架 (Interpretation)

A1 — 案例分析 (Past Application)

案例: Perplexity Voice 的音频安全边界

案例: Claude Mobile 的回答优先策略

A2 — 触发场景 (Future Trigger) ★

用户在什么情境下需要?

语言信号

与相邻 skill 的区分

E — 可执行步骤 (Execution)

B — 边界 (Boundary) ★

不要在以下情况使用

常见失败模式

Frequently asked questions

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