yt2bb — YouTube to Bilibili Video Repurposing

Description

--- name: yt2bb description: 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. license: MIT homepage: https://github.com/Agents365-ai/yt2bb compatibility: Requires Python 3, ffmpeg, yt-dlp, whisper (openai-whisper) on PATH. Self-check steps that need vision are gracefully skipped if unavailable. platforms: [macos, linux, windows] allowed-tools: Bash(python3:*) Bash(ffmpeg:*) Bash(whisper:*) Bash(yt-dlp:*) Bash(git:*) Read Write Edit metadata: {"openclaw":{"requires":{"bins":["python3","ffmpeg","yt-dlp","whisper"]},"emoji":"🎬","os":["darwin","linux","win32"],"install":[{"id":"brew-ffmpeg","kind":"brew","formula":"ffmpeg","bins":["ffmpeg"],"label":"Install ffmpeg via Homebrew","os":["darwin"]},{"id":"apt-ffmpeg","kind":"apt","package":"ffmpeg","bins":["ffmpeg"],"label":"Install ffmpeg via apt","os":["linux"]},{"id":"brew-ytdlp","kind":"brew","formula":"yt-dlp","bins":["yt-dlp"],"label":"Install yt-dlp via Homebrew","os":["darwin"]},{"id":"pip-ytdlp","kind":"pip","package":"yt-dlp","bins":["yt-dlp"],"label":"Install yt-dlp via pip","os":["linux","win32"]},{"id":"pip-whisper","kind":"pip","package":"openai-whisper","bins":["whisper"],"label":"Install openai-whisper via pip"}]},"clawhub":{"requires":{"bins":["python3","ffmpeg","yt-dlp","whisper"]},"category":"media","install":[{"id":"brew-ffmpeg","kind":"brew","formula":"ffmpeg","bins":["ffmpeg"],"label":"Install ffmpeg via Homebrew","os":["darwin"]},{"id":"apt-ffmpeg","kind":"apt","package":"ffmpeg","bins":["ffmpeg"],"label":"Install ffmpeg via apt","os":["linux"]},{"id":"brew-ytdlp","kind":"brew","formula":"yt-dlp","bins":["yt-dlp"],"label":"Install yt-dlp via Homebrew","os":["darwin"]},{"id":"pip-ytdlp","kind":"pip","package":"yt-dlp","bins":["yt-dlp"],"label":"Install yt-dlp via pip","os":["linux","win32"]},{"id":"pip-whisper","kind":"pip","package":"openai-whisper","bins":["whisper"],"label":"Install openai-whisper via pip"}]},"hermes":{"tags":["youtube","bilibili","subtitles","bilingual","video","localization","whisper","yt-dlp"],"category":"media","requires_tools":["python3","ffmpeg","yt-dlp","whisper"],"related_skills":["ffmpeg","video-podcast-maker"]},"codex":{"requires":{"bins":["python3","ffmpeg","yt-dlp","whisper"]},"allowed-tools":["bash","read","write","edit"]},"claude-code":{"allowed-tools":"Bash(python3:*) Bash(ffmpeg:*) Bash(whisper:*) Bash(yt-dlp:*) Bash(git:*) Read Write Edit"},"pi":{"requires":{"bins":["python3","ffmpeg","yt-dlp","whisper"]},"allowed-tools":["bash","read","write","edit"]},"skillsmp":{"topics":["claude-code","claude-code-skill","claude-skills","agent-skills","skillsmp","openclaw","openclaw-skills","skill-md","pi-coding-agent","youtube","bilibili","subtitles","video"]},"author":"Agents365-ai","version":"2.5.0"} --- # 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 `publish_info.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`* | `srt_utils.py check-whisper` then transcribe | `{slug}_{lang}.srt` | | 2.5 Validate | `srt_utils.py` | `srt_utils.py validate / fix` | `{slug}_{lang}.srt` (fixed) | | 3. Translate | AI | SRT-aware batch translation | `{slug}_zh.srt` | | 4. Merge | `srt_utils.py` | `srt_utils.py merge ...` | `{slug}_bilingual.srt` | | 4.5 Style | `srt_utils.py` | `srt_utils.py to_ass --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 | `publish_info.md` | ## Update check Throttle to one check per 24 hours per installation; never mutate the skill directory without explicit user consent. `SKILL_DIR` resolved here is reused by later pipeline steps for script paths. 1. If `<this-skill-dir>/.last_update` exists and is less than 24 hours old, skip this step entirely. 2. Otherwise, fetch the latest tag from upstream: ```bash git -C <this-skill-dir> ls-remote --tags origin 'v*' 2>/dev/null \ | awk '{print $2}' | sed 's|refs/tags/||' \ | sort -V | tail -1 ``` 3. Compare with this skill's `metadata.version` from the frontmatter. If the upstream tag is strictly newer (semver), tell the user one line and ask: > "A newer version of this skill is available: vX.Y.Z → vA.B.C. Want me to `git pull`?" If they say yes, run `git -C <this-skill-dir> pull --ff-only`. Refresh `.last_update` either way so the prompt doesn't repeat for 24 hours. 4. If upstream is the same or older, refresh `.last_update` silently and continue. 5. On any failure (offline, not a git checkout — e.g. ClawHub-installed copy, read-only path, no permission), swallow the error silently and continue with the user's task. Do not mention the failure. Resolve `SKILL_DIR` for use by later pipeline steps: ```bash # Find skill directory (works across Claude Code, OpenClaw, Hermes, Pi) SKILL_DIR="$(find ~/.claude/skills ~/.openclaw/skills ~/.hermes/skills ~/.pi/agent/skills ~/.agents/skills ~/myagents/myskills -maxdepth 2 -name 'yt2bb' -type d 2>/dev/null | head -1)" ``` ## Pipeline Details ### Step 1: Download **Single video:** ```bash slug="video-name" # or: slug=$(python3 "$SKILL_DIR/srt_utils.py" slugify "Video Title") mkdir -p "${slug}" yt-dlp --cookies-from-browser chrome \ -f "bv*[ext=mp4]+ba[ext=m4a]/b[ext=mp4]" \ -o "${slug}/${slug}.mp4" "https://www.youtube.com/watch?v=VIDEO_ID" ``` **Playlist / series:** ```bash yt-dlp --cookies-from-browser chrome \ -f "bv*[ext=mp4]+ba[ext=m4a]/b[ext=mp4]" \ -o "%(playlist_index)03d-%(title)s/%(playlist_index)03d-%(title)s.mp4" \ "https://www.youtube.com/playlist?list=PLAYLIST_ID" ``` After downloading, rename each folder to a clean slug and run Steps 2–6 for each video sequentially. - `-f "bv*[ext=mp4]+ba[ext=m4a]/b[ext=mp4]"`: ensure mp4 output, avoid webm - `%(playlist_index)03d`: zero-padded index to preserve playlist order - If `--cookies-from-browser` fails, export cookies first — see Troubleshooting ### Step 2: Transcribe **First run the environment check** to detect your platform and get a tailored whisper command: ```bash python3 "$SKILL_DIR/srt_utils.py" check-whisper ``` This auto-detects OS, GPU (CUDA/Metal/CPU), memory, and installed backends, then recommends the best backend + model for your hardware. If memory detection is unavailable, it falls back conservatively instead of assuming a low-memory machine. Use the command it prints. **Manual fallback** (openai-whisper, works everywhere): ```bash src_lang="en" # Change to ja/ko/es/etc. based on source video whisper_model="medium" # check-whisper recommends the best model for your hardware whisper "${slug}/${slug}.mp4" \ --model "$whisper_model" \ --language "$src_lang" \ --word_timestamps True \ --condition_on_previous_text False \ --output_format srt \ --max_line_width 40 --max_line_count 1 \ --output_dir "${slug}" mv "${slug}/${slug}.srt" "${slug}/${slug}_${src_lang}.srt" ``` **Supported backends:** | Backend | Best for | Install | |---------|----------|---------| | `mlx-whisper` | macOS Apple Silicon (fastest) | `pip install mlx-whisper` | | `whisper-ctranslate2` | Windows/Linux CUDA, or CPU (~4x faster) | `pip install whisper-ctranslate2` | | `openai-whisper` | Universal fallback | `pip install openai-whisper` | **Model selection** (auto-recommended by `check-whisper`): - `tiny` — fast draft, low accuracy, CPU-friendly (~1 GB) - `medium` — **default**, good balance (~5 GB) - `large-v3` — best accuracy, recommended for JA/KO/ZH source (~10 GB) **Notes:** - `--language`: explicitly set to avoid misdetection; supports `en`, `ja`, `ko`, `es`, etc. - `--word_timestamps True`: more precise subtitle timing - `--condition_on_previous_text False`: prevent hallucination loops - If output is garbled or repeated, add anti-hallucination flags — see Troubleshooting ### Step 2.5: Validate & Fix (optional) ```bash python3 "$SKILL_DIR/srt_utils.py" validate "${slug}/${slug}_${src_lang}.srt" # If issues found: python3 "$SKILL_DIR/srt_utils.py" fix "${slug}/${slug}_${src_lang}.srt" "${slug}/${slug}_${src_lang}.srt" ``` ### Step 3: Translate Read `{slug}_{src_lang}.srt` and translate to Chinese. **Critical rules:** These rules are modeled on the Netflix Simplified Chinese Timed Text Style Guide; follow them to produce broadcast-grade subtitles. 1. **Keep SRT format intact** — preserve index numbers, timestamps (`-->` lines) exactly as-is 2. **1:1 entry mapping** — every source entry must produce exactly one translated entry (same count) 3. **Optimize for bottom subtitles** — keep each Chinese entry to **1 line whenever possible** so the final bilingual subtitle stays compact near the bottom of the frame 4. **Max 16 full-width characters per line** (Netflix SC spec). Prefer 12–16; if a cue is very short (< 1 s) compress further so reading speed stays ≤ 9 characters/second 5. **Shorten with judgment, not mechanically** — remove filler words, repeated subjects, weak interjections, and redundant politeness before dropping key meaning 6. **Match subtitle duration** — the line must feel readable within the time on screen; if the cue is very short, compress more aggressively 7. **No trailing punctuation** on Chinese cues — drop ending `。`, `！`, `？`; keep mid-sentence `，`, `、`, `；` only when they add clarity 8. **Use full-width Chinese punctuation** inside cues (`，。！？、；：`); use **「」** for inner quotes, not `""` or `''` 9. **Half-width digits and Latin** — numbers, units, product names, and code identifiers stay half-width (`GPT-4`, `30fps`, `2026`); only punctuation is full-width 10. **Line-break discipline** — never break after function words (`的`, `了`, `吗`, `呢`, `吧`, `啊`); never split an English phrasal unit across a line break; keep modifiers with their heads 11. **Keep terminology consistent** — technical terms, names, product names, and recurring phrases should be translated the same way across batches. Maintain an inline glossary if needed 12. **Adapt, don't transliterate** — preserve register, tone, and intent over literal word matching; idioms become natural Chinese equivalents 13. **Translate in batches of 10 entries** — output each batch in valid SRT format, then continue 14. **Do NOT merge or split entries** — maintain original segmentation 15. Save as `{slug}/{slug}_zh.srt` ### Step 4: Merge ```bash python3 "$SKILL_DIR/srt_utils.py" merge \ "${slug}/${slug}_${src_lang}.srt" "${slug}/${slug}_zh.srt" "${slug}/${slug}_bilingual.srt" ``` ### Step 4.25: Netflix Lint (recommended) Run `lint` on the merged bilingual SRT to catch Netflix Timed Text Style Guide violations that `validate` doesn't cover — reading speed (CPS), per-line length, inter-cue gaps, and line count. ```bash python3 "$SKILL_DIR/srt_utils.py" lint "${slug}/${slug}_bilingual.srt" ``` **Defaults (all overridable via flags):** | Rule | Threshold | Flag | |------|-----------|------| | Reading speed (English) | ≤ 17 CPS | `--max-cps-en` | | Reading speed (Simplified Chinese) | ≤ 9 CPS | `--max-cps-zh` | | Min cue duration | 833 ms (5/6 s) | `--min-duration-ms` | | Max cue duration | 7000 ms | `--max-duration-ms` | | Min inter-cue gap | 83 ms (2 frames @ 24 fps) | `--min-gap-ms` | | Max chars/line (English) | 42 | `--max-chars-en` | | Max chars/line (Chinese, full-width) | 16 | `--max-chars-zh` | | Max lines per cue | 2 | — | **Severity model:** - **Errors** (exit code 2): duration out of bounds, CPS over limit, > 2 lines per cue. These break Netflix acceptance and should be fixed before burning. - **Warnings** (exit 0 unless errors also exist): per-line length, tight gaps. These are recommendations — address if feasible, but they don't block delivery. **When CPS errors fire**, the fix is almost always upstream — go back to Step 3 and rewrite the offending Chinese entry to fit the time window. Do **not** solve CPS by extending the cue past the source's spoken duration. **Agent-friendly output:** ```bash python3 "$SKILL_DIR/srt_utils.py" lint "${slug}/${slug}_bilingual.srt" --format json ``` Returns `{ok, error_count, warning_count, issues: [{index, code, severity, message}, ...]}` for programmatic filtering. ### Step 4.5: Style — Convert to ASS Convert the bilingual SRT to an ASS file. ASS enables per-line color, font size, and glow effects that are impossible with SRT `force_style`. Layout rule: **subtitles always stay at the bottom**. Default stack: ZH on the upper line of the bottom stack, EN on the lower line. The presets are tuned to keep the block readable while reducing overlap risk with lower-screen content. > **IMPORTANT — Ask before proceeding.** Present the preset table below to the user and ask which style they prefer. Do NOT silently pick a default. If the user has no preference, use `clean`. **Available presets:** | Preset | Look | Best for | |--------|------|----------| | `netflix` | **Pure white text, thin black outline, soft drop shadow, no box** — modeled on the Netflix Timed Text Style Guide | Professional, broadcast-grade look. Best default for documentaries, interviews, long-form content, and anything that should feel "streaming-platform native". Use with `--font "Source Han Sans SC"` on Linux / `"PingFang SC"` on macOS for closest Netflix Sans feel | | `clean` | **Yellow text on gray box** — golden ZH + light yellow EN, semi-transparent light gray background | Readability safety net for busy or mixed-brightness footage where `netflix`'s outline-only text could get visually lost. The gray box guarantees a readable contrast pad | | `glow` | **Yellow ZH + white EN with colored glow** — bright yellow ZH + white EN, blurred outer glow, no background box | Entertainment, vlogs, energetic edits. Most eye-catching, but weakest on bright or busy backgrounds | **Example prompt to user:** > 字幕有三套样式可选： > 1. `netflix` — 纯白字体 + 细黑描边 + 柔和阴影（默认推荐，Netflix 专业观感，适合纪录片/访谈/长内容） > 2. `clean` — 黄色字体 + 灰色半透明底框（亮背景或花背景的兜底选项，底框保证对比度） > 3. `glow` — 黄色/白色字体 + 彩色外发光（更抢眼，适合娱乐/Vlog） > 4. **自定义** — 提供 `.ass` 样式文件，完全控制字体、颜色、大小（可用 [Aegisub](https://aegisub.org/) 可视化编辑） > > 选哪个？默认推荐 `netflix`；如果画面特别花哨或底部信息多，可改用 `clean`。 ```bash # Netflix-grade default (white + outline + soft shadow), ZH on top python3 "$SKILL_DIR/srt_utils.py" to_ass \ "${slug}/${slug}_bilingual.srt" "${slug}/${slug}_bilingual.ass" \ --preset netflix # Gray-box fallback for busy backgrounds, EN on top python3 "$SKILL_DIR/srt_utils.py" to_ass \ "${slug}/${slug}_bilingual.srt" "${slug}/${slug}_bilingual.ass" \ --preset clean --top en # Vibrant glow (B站 entertainment style) python3 "$SKILL_DIR/srt_utils.py" to_ass \ "${slug}/${slug}_bilingual.srt" "${slug}/${slug}_bilingual.ass" \ --preset glow ``` **Custom style file** — for full control, provide an external `.ass` file with your own `[V4+ Styles]` section. It must contain styles named `EN` and `ZH`, or `to_ass` will fail early with a validation error. You can design styles visually with [Aegisub](https://aegisub.org/) and export. ```bash python3 "$SKILL_DIR/srt_utils.py" to_ass \ "${slug}/${slug}_bilingual.srt" "${slug}/${slug}_bilingual.ass" \ --style-file my_styles.ass ``` Optionally add `; en_tag=` and `; zh_tag={\blur5}` comment lines in the `.ass` file to inject ASS override tags per language. **Font by platform** (pass with `--font`, ignored when using `--style-file`): | Platform | Flag | |----------|------| | macOS | `--font "PingFang SC"` (default) | | Linux | `--font "Noto Sans CJK SC"` | | Windows | `--font "Microsoft YaHei"` | **Other options:** - `--top zh|en` — which language on the **upper line of the bottom stack** (default: `zh`) - `--res WxH` — video resolution (default: `1920x1080`) **Readability notes for all presets:** - Presets stay bottom-aligned at all times; they do **not** move to the top automatically - Font size, outline, and vertical margins scale with `--res` so 720p and 1080p keep similar visual balance - `clean` is the safest choice when you must keep subtitles at the bottom in every shot ### Step 5: Burn Subtitles Use the `ass=` filter (not `subtitles=`) — all styling comes from the ASS file. ```bash ffmpeg -i "${slug}/${slug}.mp4" \ -vf "ass='${slug}/${slug}_bilingual.ass'" \ -c:v libx264 -crf 23 -preset medium \ -c:a copy "${slug}/${slug}_bilingual.mp4" ``` - `-c:v libx264 -crf 23`: good quality with reasonable file size - `-preset medium`: balance between speed and compression (use `fast` for quicker encode) - No `force_style` needed — styles are embedded in the ASS file ### Step 6: Generate Publish Info Based on the video content (from `{slug}_{src_lang}.srt` and `{slug}_zh.srt`), generate `{slug}/publish_info.md`. All output in this file must be in **Chinese** (targeting Bilibili audience). ```markdown # Publish Info ## Source {YouTube URL} ## Titles (5 variants) 1. {Suspense/question style — spark curiosity} 2. {Data/achievement driven — emphasize results} 3. {Controversial/opinion style — spark discussion} 4. {Tutorial/practical style — emphasize utility} 5. {Emotional/relatable style — connect with audience} ## Tags {~10 comma-separated keywords covering topic, technology, domain} ## Description {3-5 sentences summarizing core content and highlights} ## Chapter Timestamps 00:00 {chapter name} ... ``` **Generation rules:** - Title style must match Bilibili conventions: conversational tone, suspense hooks, liberal use of symbols (【】, ?, !) - Tags should cover both Chinese and English keywords for discoverability - Timestamps extracted from `{slug}_bilingual.srt` at topic transition points - Description needs a strong hook — first two sentences determine whether users expand to read ## Output Structure ``` {slug}/ ├── {slug}.mp4 # Source video ├── {slug}_{src_lang}.srt # Source language subtitles ├── {slug}_zh.srt # Chinese subtitles ├── {slug}_bilingual.srt # Merged bilingual ├── {slug}_bilingual.mp4 # Final output └── publish_info.md # Bilibili upload metadata ``` ## Utility: srt_utils.py ```bash python3 "$SKILL_DIR/srt_utils.py" merge en.srt zh.srt output.srt # Merge bilingual python3 "$SKILL_DIR/srt_utils.py" merge --dry-run en.srt zh.srt output.srt # Pre-check without writing python3 "$SKILL_DIR/srt_utils.py" validate input.srt # Check timing issues python3 "$SKILL_DIR/srt_utils.py" fix input.srt output.srt # Fix timing/overlaps (multi-pass) python3 "$SKILL_DIR/srt_utils.py" slugify "Video Title" # Generate slug python3 "$SKILL_DIR/srt_utils.py" to_ass input.srt output.ass # Convert to styled ASS (default: clean, ZH on top) python3 "$SKILL_DIR/srt_utils.py" to_ass --dry-run input.srt output.ass # Pre-check without writing python3 "$SKILL_DIR/srt_utils.py" to_ass input.srt output.ass --preset glow --top en python3 "$SKILL_DIR/srt_utils.py" to_ass input.srt output.ass --style-file custom.ass # User-defined styles python3 "$SKILL_DIR/srt_utils.py" check-whisper # Detect platform, recommend whisper backend + model ``` ## Common Mistakes - **Mismatched entry counts**: Merge fails by default — fix translation or use `--pad-missing` to pad - **Font not found**: Ensure PingFang SC is installed (macOS default) or substitute (see Troubleshooting) ## Troubleshooting ### yt-dlp: Cookie Auth Failure `--cookies-from-browser chrome` requires Chrome to be closed (or uses a snapshot of the profile). If it fails: ```bash # Export cookies once, then reuse the file yt-dlp --cookies-from-browser chrome --cookies cookies.txt --skip-download "URL" yt-dlp --cookies cookies.txt -f "bv*[ext=mp4]+ba[ext=m4a]/b[ext=mp4]" -o "${slug}/${slug}.mp4" "URL" ``` For 429 / rate-limit errors, add `--sleep-interval 3 --max-sleep-interval 8`. ### whisper: Wrong Language or Hallucination Loops Symptoms: repeated phrases, garbled characters, or near-empty SRT despite clear audio. ```bash whisper "${slug}/${slug}.mp4" \ --model medium \ --language "$src_lang" \ --condition_on_previous_text False \ --no_speech_threshold 0.6 \ --logprob_threshold -1.0 \ --compression_ratio_threshold 2.0 \ --output_format srt \ --output_dir "${slug}" ``` If language is still misdetected, the audio likely has long silence or non-speech segments — add `--vad_filter True` to suppress them. ### ffmpeg: Font Not Found / CJK Boxes Pass the correct font via `--font` in the `to_ass` step (Step 4.5). The ASS file embeds the font name, so ffmpeg needs it installed at burn time. | Platform | Font | Install | |----------|------|---------| | macOS | `PingFang SC` | pre-installed | | Linux | `Noto Sans CJK SC` | `sudo apt install fonts-noto-cjk` | | Linux (alt) | `WenQuanYi Micro Hei` | `sudo apt install fonts-wqy-microhei` | | Windows | `Microsoft YaHei` | pre-installed | Regenerate the ASS file with the correct `--font` flag, then re-run the burn step. ## Privacy & Data Flow - **Browser cookies**: Step 1 uses `yt-dlp --cookies-from-browser chrome` to access age-gated or private videos. This reads Chrome cookies locally — no cookies are transmitted beyond YouTube's own servers. To avoid this, export cookies to a file first (see Troubleshooting above). - **Transcripts & translation**: Step 3 (translate) and Step 6 (publish info) are performed by the AI agent in the conversation. Transcripts are sent to whatever model/service the agent uses (e.g. Claude API). If the video contains sensitive content, use a local model for those steps. - **Auto-update check**: The pre-flight step runs `git fetch` to check for skill updates. It does not auto-pull or execute remote code. - **No telemetry**: `srt_utils.py` makes no network requests. All processing (SRT parsing, merging, ASS generation, hardware detection) is fully local.

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