No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by dean9703111 · Agent Tool · ★ 216
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is ai-agent-skill-for-video-workflow safe to install? View the security audit →
AI Agent Skills for Video Subtitle Workflow 這是一個專為影片字幕處理設計的 AI Agent Skills 集合，提供從音訊轉字幕、優化字幕、設計字卡到生成社群媒體摘要的完整工作流程。 📖 目錄 📦 包含的 Skills Audio to SRT Converter（音訊轉字幕） SRT Enhancer（字幕優化） SRT Card Annotator（字卡標註） SRT Social Summary（社群媒體摘要生成） 🚀 完整工作流程 📁 專案結構 💡 使用建議 🔧 技術細節 🤖 Cursor 員工守則 Rules 📦 包含的 Skills Audio to SRT Converter（音訊轉字幕） 將音訊檔案（MP3, WAV, M4A, FLAC 等）轉換為 SRT 字幕檔。 功能特點： 支援多種音訊格式 可自訂每行字數上限（預設 22 字，最少 4 字） 自動處理時間軸間隔（< 0.3s 自動合併） 自動檢查環境與套件依賴 使用方式： 或 輸出檔案： SRT Enhancer（字幕優化） 透過比對原稿（origin.md）優化 SRT 字幕的準確性。 功能特點： 自動修正錯漏字與專有名詞 在英文與數字前後加入半形空格 保持原有時間軸不變 使用 AI 智慧判斷，不依賴腳本 使用方式： 或 前置需求： 同資料夾需有 原稿檔案 輸出檔案： SRT Card Annotator（字卡標註） 在字幕下方智慧新增參考字卡，提升影片資訊傳達效果。 功能特點： 自動萃取重點詞句（10-16 字） 智慧分類字卡類型： 🟡 金色重點：核心結論、關鍵一句 ⚪ 白色提醒：建議、提醒、注意事項 🔴 紅色警告：風險、錯誤、踩雷 🔵 藍色列點：條列、步驟、清單 每 10 段字幕至少新增 2 個字卡 使用方式： 或 輸出檔案： ##
| Stars | 216 |
| Forks | 49 |
| Language | Python |
| Category | Agent Tool |
| Quality Score | 64.7673445174282/100 |
| Last Updated | 2026-01-28 |
| Created | 2026-01-27 |
| Platforms | python |
| Est. Tokens | ~211k |
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ai-agent-skill-for-video-workflow is 這是一個專為影片字幕處理設計的 AI Agent Skills 集合，提供從音訊轉字幕、優化字幕、設計字卡到生成社群媒體摘要的完整工作流程。. It is categorized as a Agent Tool with 216 GitHub stars.
ai-agent-skill-for-video-workflow is primarily written in Python. It covers topics such as agent-skills, ai, ai-agent.
You can find installation instructions and usage details in the ai-agent-skill-for-video-workflow GitHub repository at github.com/dean9703111/ai-agent-skill-for-video-workflow. The project has 216 stars and 49 forks, indicating an active community.
The top alternatives to ai-agent-skill-for-video-workflow on Agent Skills Hub include eCommerce-Skills, giselle, hcom. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: