Yt2bb

Pre-flight: Auto Update

Run this BEFORE any pipeline step. Locates the skill directory and checks for updates. The SKILL_DIR variable is reused by later steps for script paths.

# Find skill directory (works across Claude Code, OpenClaw, Hermes, Pi)
SKILL_DIR="$(find ~/.claude/skills ~/.openclaw/skills ~/.hermes/skills ~/.pi/agent/skills ~/.agents/skills ~/myagents/myskills -maxdepth 2 -name 'yt2bb' -type d 2>/dev/null | head -1)"
echo "yt2bb: SKILL_DIR=$SKILL_DIR"
if [ -n "$SKILL_DIR" ] && [ -d "$SKILL_DIR/.git" ]; then
  git -C "$SKILL_DIR" fetch --quiet origin main 2>/dev/null
  LOCAL=$(git -C "$SKILL_DIR" rev-parse HEAD)
  REMOTE=$(git -C "$SKILL_DIR" rev-parse origin/main 2>/dev/null)
  if [ "$LOCAL" != "$REMOTE" ]; then
    echo "yt2bb: new version available. Run: git -C $SKILL_DIR pull origin main"
  else
    echo "yt2bb: up to date."
  fi
fi

Note: Does not auto-pull — the current session already loaded the old SKILL.md. Notify the user and let them update between sessions.

Pipeline Details

Step 1: Download

Single video:

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:

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:

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):

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:

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)

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

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.

python3 "$SKILL_DIR/srt_utils.py" lint "${slug}/${slug}_bilingual.srt"

Defaults (all overridable via flags):

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:

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:

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 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。

# 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 and export.

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):

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.

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).

# 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