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：
- 专有名词：人名、品牌名、产品名等（如：Anthropic、Claude）
- 技术术语：框架、库、工具名称（如：LangChain、OpenAI、PyTorch）
- 领域词汇：行业特定词汇（如：checkpointer、middleware、runtime）
- 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:
- Proper nouns: Names, brands, products (e.g., Anthropic, Claude)
- Technical terms: Frameworks, libraries, tools (e.g., LangChain, PyTorch)
- Domain vocabulary: Industry-specific terms (e.g., checkpointer, middleware)
- 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:
Listing the terms received Identifying the likely domain/context (AI/ML tutorial, web dev, etc.) 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:
Build a mental model of expected vocabulary Identify likely speech recognition errors Apply consistent corrections throughout When User Doesn't Provide Terms
If user says "没有" / "no" / "直接开始":
Proceed with correction using built-in patterns Flag uncertain corrections for user review After completion, ask if any terms were missed Core Workflow Read the subtitle file - Load the .srt file provided by the user Identify error patterns - Recognize common speech recognition mistakes Apply corrections - Fix errors while preserving timestamps exactly 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) Process in chunks using view_range parameter Maintain context across chunks Build complete corrected file incrementally For Technical Content Identify the domain (AI/ML, web dev, etc.) Build mental model of expected terminology 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.html
Show simple line-based diff (original/corrected lines)
python scripts/subtitle_tool.py diff original.srt corrected.srt --simple
Disable colors for piping to files
python scripts/subtitle_tool.py diff original.srt corrected.srt --no-color
Analyze file for potential speech recognition errors
python scripts/subtitle_tool.py analyze input.srt --terms "LangChain,OpenAI"
Diff Output Formats Terminal Output (Default)
Shows word-level changes with colors:
[1] 00:00:01,500 --> 00:00:04,500 今天我们来学习[-Luncheon-]{+LangChain+}框架
[3] 00:00:08,000 --> 00:00:12,500 我们可以用它来创建[-绘画-]{+会话+}应用程序
[-deleted-] - Text removed (shown in red in terminal) {+added+} - Text added (shown in green in terminal) Use --all to show unchanged entries too HTML Report (Recommended for Review)
Generates a full comparison report with:
All entries displayed (changed and unchanged) Side-by-side columns: Original | Corrected | Diff View Visual highlighting: Red strikethrough for deletions, green for additions Summary statistics: Total entries, changed count, unchanged count Jump links: Quick navigation to changed entries Change indicators: Yellow dot marks changed rows python scripts/subtitle_tool.py diff original.srt corrected.srt --html diff_report.html
Then open diff_report.html in a browser.
Validation Workflow Before correction: Run analyze to identify potential errors After correction: Run validate to ensure structural integrity Review changes: Run diff --html report.html to generate a visual comparison report Validation Checks Entry count matches original All timestamps unchanged All sequence numbers preserved No formatting corruption Example Interaction Interaction 1: User uploads file without terms
User: 帮我修正这个字幕文件
Claude:
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语音识别错误：
- 专有名词：人名、品牌名、产品名
- 技术术语：框架、库、工具名称
- 领域词汇：行业特定词汇
- 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
User: LangChain, Agent, OpenAI, LangGraph, checkpointer
Claude:
收到以下术语： - 技术框架：LangChain, LangGraph, OpenAI - 技术概念：Agent, checkpointer
这是一个 LangChain 相关的技术教程。还有补充吗？
User: 没有了，开始吧
Claude: [Proceeds with correction workflow]
Interaction 2: User provides terms upfront
User: 帮我修正字幕，术语：LangChain, OpenAI, Agent
Claude: [Confirms terms, then proceeds directly]
Correction Process Read uploaded .srt file Run analyze to identify potential errors Apply corrections using provided terms as primary reference Run validate to confirm structural integrity Save corrected file with -corrected suffix Generate diff report and present summary of changes Offer HTML report: Ask user if they want an HTML diff report for easier review
Output: Provide categorized summary of corrections made.
After completion, prompt user:
修正完成！我可以生成一个 HTML 差异报告，方便您在浏览器中查看所有修改。 需要生成 HTML 报告吗？
Correction complete! I can generate an HTML diff report for easier review in your browser. Would you like me to generate the HTML report?