Let AI watch videos: local files or YouTube/Bilibili URLs -> timestamped transcript + keyframes + contact sheets. Offline, no API key. MCP server + CLI + agent skill.
Point Claude, Cursor or any MCP client at a video and get back a timestamped transcript plus keyframe contact sheets — offline, no API key. Local files first; URLs (YouTube, Bilibili, Douyin, Xiaohongshu, TikTok, Vimeo, …) are videos you are entitled to process, fetched via yt-dlp at ≤720p and deleted after processing by default.
Yueying (阅影) means "read video". One package gives you an MCP server, a CLI and an agent skill.
Contact sheet from a 24-second demo clip (four app screenshots with Chinese narration). The yellow label on every tile is the keyframe number and timestamp; the model cites them back to you.
The transcript of the same clip — local speech recognition, language auto-detected as Chinese:
(The app is called 月读; ASR heard the homophone 阅读. Speech recognition does that to names — the model corrects it from the on-screen text in the frames.)
- Captions first, Whisper only when needed. Platform subtitles are used when they exist. Otherwise local faster-whisper: large-v3-turbo on an NVIDIA GPU, small on CPU, automatic CPU fallback — nothing is uploaded, no key. - ffmpeg bundled. Works on Windows 11 out of the box (imageio-ffmpeg); no PATH fiddling. - Token-efficient. Keyframes are taken at scene changes, near-duplicates dropped, then packed into 3x3 contact sheets with burned-in timestamps. One sheet ≈ 1–2K tokens for nine moments; one transcript with [mm:ss] paragraphs. - Chinese platforms and the rest. Bilibili (multi-part, collections, member videos with your browser login), Douyin, Xiaohongshu — and YouTube, TikTok, Vimeo, X and every other yt-dlp site.
From the project README.
Add the radar badge to your README — it shows your project was picked up by MCP Radar and links to this page:
[](https://mcp.liqiwa.com/s/vsh5dvsch7-png--yueying.html)
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