# subtitle-correction

Corrects speech-recognition mistakes in .srt subtitles while keeping their timing intact. Useful for technical videos where library names and code terms matter.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 536 installs · ★ 137
- Author: sugarforever
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/sugarforever/01coder-agent-skills/tree/HEAD/skills/subtitle-correction/SKILL.md
- Updated: 2026-10-19
- HTML: https://affpapa.org/en/nevibecode/skills/subtitle-correction

## What it does

Before editing, the skill asks for domain terms and checks its understanding of the video’s subject. If the user wants to proceed without a term list, it applies built-in patterns and flags uncertain changes for review. It handles English and Chinese subtitles, with examples covering technical names and spoken code identifiers.

The skill keeps timestamps, numbering, and entry boundaries unchanged; it must not merge or split entries. Long files can be processed in chunks. Save the result with the `-corrected` suffix, then use `scripts/subtitle_tool.py` to check structure or compare versions. Timing is a hard constraint: the tool is for text corrections, not retiming.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add sugarforever/01coder-agent-skills --skill subtitle-correction -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/subtitle-correction/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/subtitle-correction/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add sugarforever/01coder-agent-skills --skill subtitle-correction -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/subtitle-correction/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add sugarforever/01coder-agent-skills --skill subtitle-correction -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/subtitle-correction/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install sugarforever/01coder-agent-skills subtitle-correction
```

2. **Manually: put the files in place**
- File: `.github/skills/subtitle-correction/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add sugarforever/01coder-agent-skills --skill subtitle-correction -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/sugarforever/01coder-agent-skills --path skills/subtitle-correction
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/subtitle-correction/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add sugarforever/01coder-agent-skills --skill subtitle-correction -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/sugarforever/01coder-agent-skills/tree/HEAD/skills/subtitle-correction/SKILL.md

## When to use it
- correct speech-recognition mistakes in an `.srt` file
- apply consistent spelling to user-listed technical terms
- format spoken variable and method names as code
- check that timestamps and subtitle count remain unchanged

## Example prompts
- Correct the recognition errors in `lesson.srt` and preserve its timestamps and numbering.
- Review `lesson.srt` using these terms: LangChain, OpenAI, and `checkpointer`.
- Compare `lesson.srt` with `lesson-corrected.srt` using `scripts/subtitle_tool.py`.

## Source text · SKILL.md · MIT

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BEGIN THIRD-PARTY FILE 2607eeda86d8bf17
````text
---
name: subtitle-correction
description: Correct subtitle files (.srt) generated from speech recognition. Use when the user uploads subtitle files and asks to correct, fix, or proofread subtitles, especially for technical content like programming tutorials, AI/ML courses, or any content with domain-specific terminology. Supports Chinese and English subtitles with intelligent error detection and correction while preserving exact timeline information.
---

# Subtitle Correction Skill

This skill corrects speech recognition errors in subtitle files while strictly preserving timeline information.

## Interactive Workflow

### Step 1: Request Terminology from User

**IMPORTANT**: Before starting any correction, ALWAYS ask the user for domain-specific terms.

Prompt the user with:

```
在开始修正之前，请提供一些关键术语，帮助我更准确地识别和修正语音识别错误：

1. **专有名词**：人名、品牌名、产品名等（如：Anthropic、Claude）
2. **技术术语**：框架、库、工具名称（如：LangChain、OpenAI、PyTorch）
3. **领域词汇**：行业特定词汇（如：checkpointer、middleware、runtime）
4. **其他关键词**：视频中频繁出现的重要词汇

请用逗号分隔，例如：`LangChain, Agent, OpenAI, checkpointer`
```

For English users:

```
Before I begin correction, please provide key terms to help me accurately identify speech recognition errors:

1. **Proper nouns**: Names, brands, products (e.g., Anthropic, Claude)
2. **Technical terms**: Frameworks, libraries, tools (e.g., LangChain, PyTorch)
3. **Domain vocabulary**: Industry-specific terms (e.g., checkpointer, middleware)
4. **Other keywords**: Important words that appear frequently

Please separate with commas, e.g.: `LangChain, Agent, OpenAI, checkpointer`
```

### Step 2: Confirm Understanding

After receiving terms, confirm by:
1. Listing the terms received
2. Identifying the likely domain/context (AI/ML tutorial, web dev, etc.)
3. Asking if there are any additional terms before proceeding

Example response:
```
收到以下术语：
- 技术框架：LangChain, LangGraph, OpenAI
- 技术概念：Agent, checkpointer, runtime

看起来这是一个 LangChain 智能体开发的教程视频。

还有其他需要补充的术语吗？如果没有，我将开始修正字幕。
```

### Step 3: Process with Terms

Use the provided terms to:
1. Build a mental model of expected vocabulary
2. Identify likely speech recognition errors
3. Apply consistent corrections throughout

### When User Doesn't Provide Terms

If user says "没有" / "no" / "直接开始":
1. Proceed with correction using built-in patterns
2. Flag uncertain corrections for user review
3. After completion, ask if any terms were missed

## Core Workflow
````
END THIRD-PARTY FILE 2607eeda86d8bf17

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