/subtitle-correction

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

1. **Read the subtitle file** - Load the .srt file provided by the user 2. **Identify error patterns** - Recognize common speech recognition mistakes 3. **Apply corrections** - Fix errors while preserving timestamps exactly 4. **Output corrected file** - Return or save based on user's context

Strict Rules

Timeline Preservation

• **NEVER modify timestamps** - Keep all `00:00:00,000 --> 00:00:00,000` lines exactly as-is
• **NEVER change subtitle numbering** - Preserve sequence numbers
• **NEVER merge or split subtitle entries** - One-to-one correspondence

Error Categories

1. Phonetic Errors (同音字/谐音错误)

Common in Chinese speech recognition:

• 会话 ↔ 绘画 (huìhuà)
• 元数据 ↔ 源数据 (yuán shùjù)
• 本课 ↔ 本科 (běnkè)
• 示例 ↔ 事例 (shìlì)
• 实践 ↔ 时间 (shíjiàn)

2. Technical Term Errors

Speech recognition often fails on:

• Framework names: LangChain, LangGraph, OpenAI, PyTorch, TensorFlow
• Programming terms: API, SDK, runtime, checkpointer, middleware
• Code identifiers: snake_case names, function names, class names

3. English-Chinese Mixed Content

• Luncheon/lunch → langchain
• open EI/open Email → OpenAI
• land GRAPH → langgraph
• a memory Server → MemorySaver

4. Code-Related Terms

Convert spoken descriptions to proper format:

• "underscore" → "_" in variable names
• "dot" → "." in method calls
• Recognize camelCase, snake_case, PascalCase patterns

User-Provided Terminology

When users provide a terminology list, use it as the primary reference for corrections:

用户提供的术语：LangChain,Agent,OpenAI,LangGraph

These terms indicate:

• Expected proper spellings of technical terms
• Context about the content domain
• Hints for identifying speech recognition errors

Processing Strategy

For Long Files (>200 lines)

1. Process in chunks using `view_range` parameter 2. Maintain context across chunks 3. Build complete corrected file incrementally

For Technical Content

1. Identify the domain (AI/ML, web dev, etc.) 2. Build mental model of expected terminology 3. Apply domain-specific corrections consistently

Quality Checks

Before outputting:

• Verify all timestamps unchanged
• Verify subtitle count unchanged
• Check terminology consistency throughout
• Ensure no orphaned corrections (partial fixes)

Common Correction Patterns

Chinese AI/ML Course Content

| Error | Correction | Context | |-------|------------|---------| | 蓝犬/蓝卷/Lantern | LangChain | Framework name | | 绘画 | 会话 | Session/conversation | | 拖/tour | tool | Tool concept | | checkpoint组件 | checkpointer组件 | Memory component | | 源数据 | 元数据 | Metadata | | 大约模型 | 大模型 | Large model | | 中间键 | 中间件 | Middleware |

Code Identifiers

| Spoken | Written | |--------|---------| | user underscore 001 | user_001 | | thread underscore id | thread_id | | create underscore agent | create_agent | | runtime dot state | runtime.state |

Output Format

When saving, use `-corrected` suffix:

• Input: `filename.srt`
• Output: `filename-corrected.srt`

Validation Script

Use `scripts/subtitle_tool.py` to validate and analyze subtitle files:

# Validate corrected file preserves structure
python scripts/subtitle_tool.py validate original.srt corrected.srt

# Show word-level diff with colored output (default, changes only)
python scripts/subtitle_tool.py diff original.srt corrected.srt

# Show ALL entries (changed and unchanged) in terminal
python scripts/subtitle_tool.py diff original.srt corrected.srt --all

# Generate HTML diff report (recommended for review)
python scripts/subtitle_tool.py diff original.srt corrected.srt --html report.ht