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Agent Skill · 开发

transcript-polisher(成绩单抛光机)

transcript-polisher 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

transcript-polisher 是开发类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为只读;许可证信息还不明确,第三方扫描已通过,按安装量在开发类的 21,746 个 Skill 里排第 18,464。

总安装 336周安装 14GitHub Stars 9下载 112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
unknown

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:transcript-polisher(成绩单抛光机)
来源仓库:https://github.com/cdeistopened/skill-stack
仓库路径:skills/transcript-polisher
安装命令:
npx skills add https://github.com/cdeistopened/skill-stack --skill transcript-polisher
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/cdeistopened/skill-stack --skill transcript-polisher

这个 Skill 是做什么的

开发类 Skill 面向写代码、改代码和排查问题,一般会读写仓库文件,部分还会执行构建或测试命令。

将原始转录本转化为可读性强的专业文档,保留原意同时优化表达。适用于访谈、播客和会议记录的后期编辑与排版场景。使用时平衡真实性与清晰度,去除冗余但保留核心信息。通过 GitHub 安装,支持主流宿主环境,建议核对发言人授权。transcript-polisher 属于开发类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/transcript-polisher

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/cdeistopened/skill-stack --skill transcript-po…;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

transcript-polisher 怎么安装?

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

transcript-polisher 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。

transcript-polisher 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。

transcript-polisher 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

transcript-polisher 用的人多吗?

公开数据是累计安装 336 次,近一周安装 14 次,下载 112 次,GitHub 9 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。

transcript-polisher 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。
  • 第三方扫描已通过,常见的高风险行为没有命中。
  • 标注为只读权限,试用阶段不容易误改本地文件。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 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。
  • 需要处理开发类任务、又不想每次都手写提示词的人。
  • 只想先试试效果、不希望它改动本地文件的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

开发类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
skill-vetter

技能审查员 · OpenClaw

8,779,018986操作浏览器MIT-0通过
ontology

本体论 · OpenClaw

6,636,960576只读MIT-0通过
entra-app-registration

内部应用程序注册 · Codex、Claude

6,631,872764接触敏感数据未明确通过
entra-app-registration

内部应用程序注册 · Codex、Claude

3,757,680201接触敏感数据未明确通过
nano-banana-pro

纳米香蕉专业版 · OpenClaw

3,526,898364接触敏感数据MIT-0有提醒
transcript-polisher

成绩单抛光机 · 本页

3369只读未明确通过

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

Transcript Polisher

Transform raw podcast or interview transcripts into polished, professional documents that maintain authentic voice while dramatically improving readability.

Purpose

Raw transcripts from automated services are often unreadable - filled with filler words, incomplete sentences, and poor formatting. This skill cleans them up while preserving what was actually said.

Core Philosophy: Balance authenticity with clarity. Remove everything that doesn't add meaning while preserving what was actually said. Create the "ideal version" of what the speaker wanted to communicate - without changing their words or ideas.

Target: 25-35% length reduction while maintaining 100% fidelity to meaning.

When to Use This Skill

  • Processing raw transcripts from Rev, Otter, Descript, YouTube auto-captions
  • Cleaning up interview recordings or video transcripts
  • Preparing spoken content for publication as articles or show notes
  • Converting conversational content into readable written format

Not for: Written content that wasn't originally spoken, pre-polished articles, or scripts that are already edited.


Workflow

Step 1: Add Document Structure

Create proper header with:

# [Guest Name]: [Compelling Episode Title]

*[Podcast Name] Episode - [Host Names]*

---

## Timestamped Outline
[Add 10 chapters maximum - see guidelines below]

---

## [Time] Chapter Title
[Content starts here]

Timestamped Outline Rules:

  • Format: **MM:SS** - Descriptive Chapter Title
  • 10 chapters maximum for 45-60 minute episodes
  • Focus on major topic changes, not minute-by-minute
  • Use compelling, specific titles (not generic descriptions)

Good Examples:

  • ✅ 12:25 - The Turnaround: From Struggling to Thriving
  • ✅ 29:08 - Why Successful Strategies Don't Spread

Poor Examples:

  • ❌ 12:25 - Guest talks about their experience
  • ❌ 29:08 - Discussion about the industry

Step 2: Identify and Label Speakers

Replace generic speaker markers (>>, Speaker 1, etc.) with actual names:

  • Bold all speaker names: **Isaac:** Content here
  • Use first names for casual podcasts, full names for professional interviews
  • Be consistent throughout

Step 3: Aggressive Editing Pass

Apply aggressive polishing while maintaining 100% fidelity.

Remove ALL filler words:

  • "um," "uh," "ah," "er," "hmm"
  • "you know," "I mean," "like" (when used as filler)
  • "so," "well," "basically," "literally" (when empty)
  • "kind of," "sort of" (when hedging unnecessarily)

Consolidate restarts:

  • ❌ "I think that... well, what I think is... the main thing is..."
  • ✅ "The main thing is..."

Pick the clearest version when speakers repeat:

  • If they say the same thing 3 ways, keep only the best one
  • Combine fragments into complete thoughts

Fix awkward grammar:

  • Complete sentence fragments
  • Fix subject-verb agreement
  • Repair run-on sentences

Keep only the strongest examples:

  • If speaker gives 5+ examples, keep 1-2 best ones
  • Remove redundant illustrations of the same point

Add clarity to vague pronouns:

  • Use minimal bracketed additions: "they [the teachers] decided..."
  • Only when meaning would be unclear otherwise

Step 4: Preserve What Matters

Never edit:

  • Unique voice and natural rhythm
  • Exact wording of powerful statements
  • Emotional moments and authenticity
  • Specialized terminology
  • Cultural references that reveal personality
  • Natural dialogue patterns
  • Humor and personality
  • Memorable phrases

The 100% Fidelity Rule:

  • ❌ Never paraphrase or change meaning
  • ❌ Never add ideas not present in original
  • ❌ Never remove key insights or stories
  • ✅ Only remove redundancy and inefficiency
  • ✅ Preserve exact wording of powerful statements
  • ✅ Keep all substantive content

Step 5: Quality Check

Before finishing:

  • Length reduced by 25-35%
  • All filler words removed
  • Speakers properly identified and bolded
  • Chapter breaks added with compelling titles
  • Grammar fixed but voice preserved
  • Key insights and stories intact
  • No meaning changed or paraphrased
  • Reads naturally when spoken aloud

Editing Guidelines

What TO Edit (Aggressively)

1. Filler Words Remove ALL instances unless part of signature style:

  • um, uh, ah, er, hmm
  • you know, I mean, like
  • so, well, basically, literally, actually
  • kind of, sort of, I think, probably, maybe

2. False Starts and Restarts

Before: "I think that... well, what I was trying to say is... the main thing is that we need to focus"
After: "The main thing is that we need to focus"

3. Redundant Explanations When the same point is made multiple ways, keep only the clearest version.

4. Verbal Hedging (when excessive)

Before: "I think it's probably maybe kind of important to sort of consider..."
After: "It's important to consider..."

5. Conversational Detours Remove tangents that don't serve the narrative or provide insight.

6. Grammar Issues

  • Incomplete sentences → complete
  • Subject-verb disagreement → fix
  • Run-on sentences → break up

7. Excessive Examples If 5+ examples are given for one point, keep 1-2 strongest.

8. Vague Pronouns (minimal intervention) Only add bracketed clarity when meaning is genuinely unclear:

"They [the board members] decided to..."

What NOT to Edit

1. Unique Voice and Natural Rhythm Keep the speaker's distinctive patterns, even if informal.

2. Powerful Statements Preserve exact wording of quotable moments.

3. Emotional Moments Don't sanitize authentic expressions:

  • ✅ Keep: "That bugged the crap out of me"
  • ❌ Don't change to: "That bothered me significantly"

4. Specialized Terminology Keep industry/domain-specific language even if uncommon.

5. Cultural References Preserve references that reveal personality and context.

6. Natural Dialogue Patterns Keep the back-and-forth feel of conversation.


Common Transcription Errors to Fix

Auto-Caption Issues

  • OCR errors (e.g., "quad code" → "Claude Code")
  • Homophone mistakes ("their" vs "there")
  • Missing punctuation
  • Run-on sentences
  • Mis-identified technical terms

Audio Transcription Issues

  • Speaker confusion
  • Overlapping dialogue
  • Background noise artifacts
  • Timestamp errors

Output Format

Final polished transcript should be:

  • Markdown formatted
  • Properly sectioned with timestamps
  • Speaker names bold and consistent
  • 25-35% shorter than original
  • Grammar perfect but voice authentic
  • Ready for publication or repurposing

Before/After Examples

Example 1: Filler Removal

Before:

"So, um, I think that, you know, what we really need to focus on is, like, the fact that, you know, students are, um, actually learning better when, you know, they have more autonomy."

After:

"Students learn better when they have more autonomy."

(15 words → 8 words = 47% reduction)

Example 2: Consolidating Restarts

Before:

"The thing about... what I'm trying to say is... the main point here is that we need to rethink how we approach this. We need to rethink our approach. What I mean is, the whole system needs to be reconsidered."

After:

"The whole system needs to be reconsidered."

(Kept only the clearest expression of the idea)

Example 3: Voice Preservation

Before:

"Look, this bugged the crap out of me for years. Nobody wanted to talk about it. Like, everyone knew, but nobody would say anything."

After:

"Look, this bugged the crap out of me for years. Nobody wanted to talk about it. Everyone knew, but nobody would say anything."

(Only removed the filler "like" - preserved the authentic voice and expression)


Bundled Resources

  • references/editing-guidelines.md - Detailed editing rules with examples
  • references/structure-template.md - Header format and chapter guidelines

Related Skills

  • anti-ai-writing - Further humanize and polish the output
  • social-content-creation - Repurpose transcript insights to social posts
  • hook-and-headline-writing - Create compelling chapter titles

*Remember: 100% fidelity to meaning, 25-35% reduction in length. Remove what doesn't add meaning. Preserve what makes it authentic.*

平台分布

Codex

35.81%
按下载量换算40

Claude

29.4%
按下载量换算33

Cursor

18.18%
按下载量换算20

Gemini CLI

10.22%
按下载量换算11

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。目前只有一个公开来源,建议同时核对源仓库。

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