Proofread
sonilo-ai/skills
Transcribe a video with Sonilo and translate the transcript into editable .srt files — one per target language, plus the detected source language — so the wording can be read and corrected before…
Fixes speech-recognition mistakes in .srt subtitle files, in Chinese or English, using terms you supply and leaving every timestamp untouched.
Install Subtitle Correction
$ npx skills add sugarforever/01coder-agent-skills --skill subtitle-correction -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install sugarforever/01coder-agent-skills subtitle-correction --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/sugarforever/01coder-agent-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/subtitle-correction .claude/skills/subtitle-correction && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "subtitle-correction" agent skill from https://github.com/sugarforever/01coder-agent-skills/tree/main/skills/subtitle-correction into .claude/skills/subtitle-correction/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "subtitle-correction", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/sugarforever/01coder-agent-skills/tree/main/skills/subtitle-correctionType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add sugarforever/01coder-agent-skills --skill subtitle-correction -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install sugarforever/01coder-agent-skills subtitle-correction --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/sugarforever/01coder-agent-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/subtitle-correction .agents/skills/subtitle-correction && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "subtitle-correction" agent skill from https://github.com/sugarforever/01coder-agent-skills/tree/main/skills/subtitle-correction into .agents/skills/subtitle-correction/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "subtitle-correction", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add sugarforever/01coder-agent-skills --skill subtitle-correction -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install sugarforever/01coder-agent-skills subtitle-correction --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/sugarforever/01coder-agent-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/subtitle-correction .cursor/skills/subtitle-correction && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "subtitle-correction" agent skill from https://github.com/sugarforever/01coder-agent-skills/tree/main/skills/subtitle-correction into .cursor/skills/subtitle-correction/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "subtitle-correction", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/sugarforever/01coder-agent-skills.git --path skills/subtitle-correction--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add sugarforever/01coder-agent-skills --skill subtitle-correction -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install sugarforever/01coder-agent-skills subtitle-correction --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/sugarforever/01coder-agent-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/subtitle-correction .gemini/skills/subtitle-correction && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "subtitle-correction" agent skill from https://github.com/sugarforever/01coder-agent-skills/tree/main/skills/subtitle-correction into .gemini/skills/subtitle-correction/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "subtitle-correction", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install sugarforever/01coder-agent-skills subtitle-correctionInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add sugarforever/01coder-agent-skills --skill subtitle-correction -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/sugarforever/01coder-agent-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/subtitle-correction .github/skills/subtitle-correction && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "subtitle-correction" agent skill from https://github.com/sugarforever/01coder-agent-skills/tree/main/skills/subtitle-correction into .github/skills/subtitle-correction/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "subtitle-correction", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add sugarforever/01coder-agent-skills --skill subtitle-correction -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install sugarforever/01coder-agent-skills subtitle-correction --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/sugarforever/01coder-agent-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/subtitle-correction .opencode/skills/subtitle-correction && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "subtitle-correction" agent skill from https://github.com/sugarforever/01coder-agent-skills/tree/main/skills/subtitle-correction into .opencode/skills/subtitle-correction/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "subtitle-correction", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
subtitle-correction- GitHub stars
- 136
- Token cost
- ~2.3k tokens
- SKILL.md length
- 753 words
- Files
- 4 (incl. scripts, references)
- Skills in repo
- 21
- Repo updated
- First seen
- Licence
- MIT
At a glance
Fixes speech-recognition mistakes in .srt subtitle files, in Chinese or English, using terms you supply and leaving every timestamp untouched.
- Works in 3 steps: Request Terminology from User → Confirm Understanding → Process with Terms
- Proofreading .srt subtitles produced by automatic speech recognition
- SKILL.md covers Interactive Workflow, Core Workflow, Strict Rules and User-Provided Terminology, plus 5 more sections
- Runs Python scripts from its folder; calls python
What it does
Before any editing the agent asks for domain terms such as proper nouns, brands and technical vocabulary, then repeats the list back and names the likely domain, for example an AI course or web development. If you have no terms, it uses built-in patterns, flags the corrections it is unsure about and asks afterward whether it missed any. It works on programming tutorials and other content with specialist terminology.
The rules are strict: never change timestamps or subtitle numbering, and never merge or split entries, so the output stays one to one with the input. Error types include Chinese homophone mistakes, such as pairs of characters that sound alike, and misrecognized framework or programming terms. Bundled files are references on the SRT format and terminology plus scripts/subtitle_tool.py. The excerpt is truncated.
When your agent uses it
- Proofreading .srt subtitles produced by automatic speech recognition
- Fixing technical terms in programming or AI course captions
- Correcting Chinese homophone errors without touching the timeline
Example prompts
- “Fix the speech recognition errors in lecture.srt; the course covers LangChain and LangGraph.”
- “Proofread this Chinese subtitle file and flag the corrections you are unsure about.”
- “Correct tutorial.srt and keep every timestamp exactly as it is.”
Requirements
- Python to run the bundled subtitle_tool.py script
Workflow steps
3 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit e51fb6e. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 1 file in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
python
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Subtitle Correction loads about 2.3k tokens when it runs, and up to ~3.8k if it reads all its reference files. Until then it costs about 108 tokens; SKILL.md has 753 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
SKILL.md
The full file from sugarforever/01coder-agent-skills at commit e51fb6e, republished under its MIT licence (© sugarforever). 753 words, ~2,293 tokens.
Download SKILL.mdSave it as
.claude/skills/subtitle-correction/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.- 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:
- 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 --> 00:00:00,000lines 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,LangGraphThese 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_rangeparameter - 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 |
Show full SKILL.md (299 more words)Show less
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
--allto 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.htmlThen open diff_report.html in a browser.
Validation Workflow
- Before correction: Run
analyzeto identify potential errors - After correction: Run
validateto ensure structural integrity - Review changes: Run
diff --html report.htmlto 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：
1. **专有名词**：人名、品牌名、产品名
2. **技术术语**：框架、库、工具名称
3. **领域词汇**：行业特定词汇
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`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
analyzeto identify potential errors - Apply corrections using provided terms as primary reference
- Run
validateto confirm structural integrity - Save corrected file with
-correctedsuffix - 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?© sugarforever, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 3 other files (scripts, references) in skills/subtitle-correction of sugarforever/01coder-agent-skills.
- SKILL.md
- references/srt-format.md
- references/terminology.md
- scripts/subtitle_tool.py
Open the folder on GitHubat commit e51fb6e
Compare with similar skills
Subtitle Correction next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Subtitle Correction this skillsugarforever/01coder-agent-skills | 136 | — | ~2.3k | Automated safety check: Pass | MIT | |
| Proofreadsonilo-ai/skills | 115 | 1 repos | ~4.3k | Automated safety check: Notes | MIT | |
| KrillinAI Subtitle Generatorkrillinai/OpenCreator | 13k | 1 repos | ~613 | Automated safety check: Pass | Apache-2.0 | |
| Youtube PublishAndonywang123/Epost | 197 | — | ~3.4k | Automated safety check: Warn | None | |
| Video TranslationNoizAI/skills | 526 | — | ~1.3k | Automated safety check: Notes | None | |
| Check Metadata Styleowid/etl | 158 | — | ~4.4k | Automated safety check: Pass | MIT |
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Related
Categories
Questions about Subtitle Correction
What does Subtitle Correction do?
Fixes speech-recognition mistakes in .srt subtitle files, in Chinese or English, using terms you supply and leaving every timestamp untouched. Before any editing the agent asks for domain terms such as proper nouns, brands and technical vocabulary, then repeats the list back and names the likely domain, for example an AI course or web development. If you have no terms, it uses built-in patterns, flags the corrections it is unsure about and asks afterward whether it missed any.
When should I use Subtitle Correction?
Subtitle Correction fits situations like: proofreading .srt subtitles produced by automatic speech recognition; fixing technical terms in programming or AI course captions; correcting Chinese homophone errors without touching the timeline.
How do I install Subtitle Correction in Claude Code?
Run `npx skills add sugarforever/01coder-agent-skills --skill subtitle-correction -a claude-code`. Or copy the skill folder (skills/subtitle-correction in sugarforever/01coder-agent-skills) into .claude/skills/subtitle-correction in your project. Claude Code loads it when a task matches its description.
How do I install Subtitle Correction in Codex?
Run `npx skills add sugarforever/01coder-agent-skills --skill subtitle-correction -a codex`. Or copy the skill folder (skills/subtitle-correction in sugarforever/01coder-agent-skills) into .agents/skills/subtitle-correction in your project. Codex loads it when a task matches its description.
Can I use Subtitle Correction in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add sugarforever/01coder-agent-skills --skill subtitle-correction -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/subtitle-correction, .gemini/skills/subtitle-correction, .github/skills/subtitle-correction and .opencode/skills/subtitle-correction in your project.
What does Subtitle Correction need to run?
Going by SKILL.md and its folder, Subtitle Correction needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python to run the bundled subtitle_tool.py script.
Does Subtitle Correction access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Subtitle Correction safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Subtitle Correction use?
Subtitle Correction is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Subtitle Correction use?
About 2.3k tokens (SKILL.md is roughly 9.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 1.5k tokens, read only when the agent opens those files.
What are the alternatives to Subtitle Correction?
Skills that share tags, products or a category with Subtitle Correction: Proofread (sonilo-ai/skills, 115 stars), KrillinAI Subtitle Generator (krillinai/OpenCreator, 13k stars), Youtube Publish (Andonywang123/Epost, 197 stars) and Video Translation (NoizAI/skills, 526 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Subtitle Correction?
sugarforever (a GitHub user) maintains it in sugarforever/01coder-agent-skills, which has 136 GitHub stars. The repository holds 21 skills in this directory. The repository was last updated on June 19, 2026.
Source: sugarforever/01coder-agent-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.