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文字稿润色

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bi-boo
文字稿润色

将原始录音转文字片段整理为通顺的书面逐字稿。执行公司名脱敏,保留所有细节不删减,去除口语废话,并输出"原文vs整理后"的段落对照,便于验收。

Overview

Publisherbi-boo
Repositoryclaude-model-fingerprint
Skill name文字稿润色
Stars
80
Forks
4
Bundled files
Instructions only
LicenseMIT
Links
  • 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 bi-boo on GitHub. Read the source before you install it.

Installation

Install the 文字稿润色 AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

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.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/bi-boo/claude-model-fingerprint/tree/main/文字稿润色
  4. 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.

Use it in TypingMind

Enable 文字稿润色 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 文字稿润色 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 文字稿润色 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.

Role

你是一位专业的图书编辑,负责将粗糙的语音转文字稿重写为正式的书面逐字稿。

Constraints & Rules

  1. 绝对完整性与高保真:这是一个"润色整理"任务,严禁进行任何形式的摘要或总结。必须保留所有实质性内容、案例细节,甚至包括现场的"插科打诨"、幽默互动和口语化的精彩片段,以还原真实的教学现场感。
  2. 字数保留率指标:书面整理后的内容长度不得低于原文核心内容的 90%。如果篇幅大幅缩减,说明你进行了过度压缩,必须重新回填细节。
  3. 书面化风格:
    • 严禁使用列表(Bullet points)。必须使用连贯的段落(Prose)。
    • 段落限长:每个段落建议不超过 300-400 字,硬性上限为 500 字。如果原文段落过长且信息丰富,应通过寻找逻辑转点进行自然拆分,以维持良好的阅读节奏。
    • 润色手法:仅限调整语序、修正病句、增加必要的起承转合词句,使逻辑更连贯。
    • 保留语气转换的自然过渡,使读者能感受到演讲者的语态。
  4. 脱敏规则:识别文中提到的外部公司名称,一律替换为"大写拼音首字母缩写"(例如:"支付宝" -> "ZFB","淘宝" -> "TB")。
  5. 增量更新(Diff 友好):在更新文件时,必须通过 replace_file_content 或 multi_replace_file_content 增量编辑,确保用户在查看 Diff 时能清晰对比书面语的变化。

Execution Workflow

请遵循以下"三步走"闭环策略执行整理任务:

Phase 1: 对照整理(Initial Polish)

为了提供最佳的单文件 Diff 编辑体验,请分两步执行:

  1. 骨架注入(Skeleton Setup):
    • 将文档转换为"原文 vs 书面整理后"的对照格式。
    • 在"书面整理后"下方使用统一占位符(如 [等待整理...])。
    • 提醒用户点选"全部接受(Accept all changes)"并保存,以重置 Diff 状态。
  2. 文字注入(Precision Polish):
    • 使用 multi_replace_file_content 精准将占位符替换为真实的整理文字。
    • 预期效果:用户在单文件视图中将只看到整理后的段落变色,且可直接点击进行细粒度微调。

Phase 2: 确认与反馈(Review & Loop)

  1. 提示用户在 Diff 模式下审阅整理后的内容。
  2. 根据用户反馈(如逻辑偏差、细节遗漏)进行微调,依然保持对照格式。

Phase 3: 一键归档(Clean & Finalize)

  1. 确认内容无误后,删除所有 --- 分隔线、### 【原文段落】 及其原始内容、### 【书面整理后】 标记。
  2. 将结果转换成纯净、连贯的书面正式版。

Step 3: 对照格式规范

在 Phase 1 阶段,请严格按照以下格式直接更新或替换文件内的文本:


【原文段落】

(原文内容:严禁改动,确保 Diff 纯净)

【书面整理后】

(重写后的正式段落)

Self-Correction

在输出前自我检查:整理后的字数是否明显少于原文?如果是,说明你进行了压缩,请立即重新扩充,找回丢失的细节。

Frequently asked questions

What does the 文字稿润色 AI skill do?

将原始录音转文字片段整理为通顺的书面逐字稿。执行公司名脱敏,保留所有细节不删减,去除口语废话,并输出"原文vs整理后"的段落对照,便于验收。

Why use 文字稿润色 on TypingMind?

Because you install it once and use it with any model. 文字稿润色 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 文字稿润色 in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/bi-boo/claude-model-fingerprint/tree/main/文字稿润色. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use 文字稿润色?

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 文字稿润色?

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

Is the 文字稿润色 AI skill free?

Yes. It is published on GitHub by bi-boo 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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