Agent skill · agents365-ai
yt2bb
Use when the user wants to repurpose a YouTube video for Bilibili, add bilingual (English-Chinese) subtitles to a video, or create hardcoded subtitle versions for Chinese platforms.
What it needs
About 10k tokens when loaded.
What this skill does
yt2bb — YouTube to Bilibili Video Repurposing Overview Six-step pipeline: download → transcribe → translate → merge → burn subtitles → generate publish info. Produces a video with hardcoded bilingual (EN/ZH) subtitles and a publishinfo.md with Bilibili upload metadata. When to Use User provides a YouTube URL (single video or playlist) and wants a Bilibili-ready version User needs bilingual EN-ZH subtitles burned into video User wants to repurpose English video content for Chinese audience Quick Reference Step Tool Command Output ------ ------ --------- -------- 0. Update git Auto-check for skill updates — 1. Download yt-dlp yt-dlp --cookies-from-browser chrome -f ... -o ... {slug}.mp4 2. Transcribe whisper srtutils.py check-whisper then transcribe {slug}{lang}.srt 2.5 Validate srtutils.py srtutils.py validate / fix {slug}{lang}.srt (fixed) 3. Translate AI SRT-aware batch translation {slug}zh.srt 4. Merge srtutils.py srtutils.py merge ... {slug}bilingual.srt 4.5 Style srtutils.py srtutils.py toass --preset netflix\ clean\ glow {slug}bilingual.ass 5. Burn ffmpeg ffmpeg -c:v libx264 -vf ass=... {slug}bilingual.mp4 6. Publish AI Analyze content, generate metadata publishinfo.md Resolve the skill directory Resolve SKILLDIR for use by later pipeline steps: Pipeline Details Step 1: Download Single video: Playlist / series: After downloading, rename each folder to a clean slug and run Steps 2–6 for each video sequentially. -f "bv[ext=mp4]+ba[ext=m4a]/b[ext=mp4]": ensure mp4 output, avoid webm %(playlistindex)03d: zero-padded index to preserve playlist order If --cookies-from-browser fails, export cookies first — see Troubleshooting Step 2: Transcribe First run the environment check to detect your platform and get a tailored whisper command: This auto-detects OS, GPU (CUDA/Metal/CPU), memory, and installed backends, then recommends the best backend + model for your hardware. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills agents365-ai/yt2bb