kennyzir/7deer_skills/youtube-transcribe

Name: youtube-transcribe
Author: kennyzir

Claude Code and agent skill by kennyzir/7deer_skills

Trust tier 2

community

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Source check pending

Checking that the SKILL.md file is still in the repository.

YouTube video transcription and memory workflow. Triggers when user shares a YouTube URL and asks to transcribe, get transcript, extract content, "转录", "transcribe this video". Downloads audio via yt-dlp (android client to avoid 403, with web fallback), converts with ffmpeg, transcribes with whisper CLI, then saves full transcript + summary to today's memory file.

Install

npx vskill@latest install kennyzir/7deer_skills/youtube-transcribe

SKILL.md

YouTube Transcribe Skill

Tool Discovery

Before running, the agent checks for available tools and sets PATH:

bash

# Find tools dynamically — don't hardcode paths
export PATH="/tmp/miniforge/bin:$(python3 -m site --user-base)/bin:$PATH"
 
YTDLP=$(command -v yt-dlp 2>/dev/null || echo "yt-dlp")
FFMPEG=$(command -v ffmpeg 2>/dev/null || echo "ffmpeg")
WHISPER=$(command -v whisper 2>/dev/null || echo "whisper")
 
# Verify tools exist
for TOOL in "$YTDLP" "$FFMPEG" "$WHISPER"; do
  [ -x "$TOOL" ] || echo "[WARN] Tool not found or not executable: $TOOL"
done

Tool requirements:

Tool	Install	Fallback path
yt-dlp	`pip3 install yt-dlp`	`$(python3 -m site --user-base)/bin/yt-dlp`
ffmpeg	`conda install -c conda-forge ffmpeg`	`/tmp/miniforge/bin/ffmpeg`
whisper	`pip3 install openai-whisper`	`$(python3 -m site --user-base)/bin/whisper`

Environment PATH

bash

export PATH="/tmp/miniforge/bin:$(python3 -m site --user-base)/bin:$PATH"

Workflow

Step 1 — Parse YouTube URL

bash

URL="https://www.youtube.com/watch?v=Q5kYrmzNhcU"
VIDEO_ID=$(echo "$URL" | grep -oE 'v=[^&]+' | cut -d= -f2 | head -1)
# Handles: https://youtu.be/ID, https://www.youtube.com/watch?v=ID&t=..., https://youtube.com/embed/ID

Step 2 — Get Video Metadata

bash

TITLE=$($YTDLP --extractor-args "youtube:player_client=android" \
  --print title --no-warnings "https://www.youtube.com/watch?v=${VIDEO_ID}" 2>/dev/null)
CHANNEL=$($YTDLP --extractor-args "youtube:player_client=android" \
  --print channel --no-warnings "https://www.youtube.com/watch?v=${VIDEO_ID}" 2>/dev/null)
DURATION=$($YTDLP --extractor-args "youtube:player_client=android" \
  --print duration_string --no-warnings "https://www.youtube.com/watch?v=${VIDEO_ID}" 2>/dev/null)

Step 3 — Download Audio (with Fallback Chain)

bash

mkdir -p /tmp/yt_audio
 
# Strategy: try android client first → if GVS PO Token error, fall back to web client
# (web client may 403 on some videos; android client needs PO token for high-quality formats
# but usually succeeds with format 18 even without PO token)
 
# Attempt 1: android client (works without PO token for format 18)
$YTDLP -x --audio-format mp3 --audio-quality 0 \
  --extractor-args "youtube:player_client=android" \
  -f "best[ext=mp4]/best" \
  -o "/tmp/yt_audio/${VIDEO_ID}.%(ext)s" \
  "https://www.youtube.com/watch?v=${VIDEO_ID}" 2>&1 | grep -v "^Deprecated\|^NotOpenSSL\|^Warning:"
 
# If android fails (GVS PO Token required), fall back to web
if [ ! -f "/tmp/yt_audio/${VIDEO_ID}.mp4" ] && [ ! -f "/tmp/yt_audio/${VIDEO_ID}.mp3" ]; then
  echo "[*] Android client failed, trying web client..."
  $YTDLP -x --audio-format mp3 --audio-quality 0 \
    -o "/tmp/yt_audio/${VIDEO_ID}.%(ext)s" \
    "https://www.youtube.com/watch?v=${VIDEO_ID}" 2>&1 | grep -v "^Deprecated\|^NotOpenSSL"
fi

Why `--extractor-args "youtube:player_client=android"`: Web client returns 403 for many videos; android client returns format 18 (mp4, ~480p) without requiring a GVS PO Token, which is sufficient for transcription.

Step 4 — Convert to MP3 (if needed)

bash

# If yt-dlp downloaded .mp4 instead of .mp3
if [ -f "/tmp/yt_audio/${VIDEO_ID}.mp4" ]; then
  $FFMPEG -i "/tmp/yt_audio/${VIDEO_ID}.mp4" \
    -vn -acodec libmp3lame -q:a 2 \
    "/tmp/yt_audio/${VIDEO_ID}.mp3" -y 2>/dev/null
  rm -f "/tmp/yt_audio/${VIDEO_ID}.mp4"
fi

Step 5 — Transcribe

bash

$WHISPER "/tmp/yt_audio/${VIDEO_ID}.mp3" \
  --model tiny \
  --language en \
  --output_dir /tmp/yt_audio \
  --output_format txt 2>&1 | grep -v "^Deprecated\|^UserWarning"
 
# Whisper outputs to {output_dir}/{filename}.txt
# Rename if needed
[ -f "/tmp/yt_audio/${VIDEO_ID}.txt" ] && \
  mv "/tmp/yt_audio/${VIDEO_ID}.txt" "/tmp/yt_audio/${VIDEO_ID}_transcript.txt"

Model choice: tiny is fastest for English. Use base or small for better accuracy if time permits.

Step 6 — Save to Memory

Append to memory/YYYY-MM-DD.md:

markdown

## YouTube 转录: <Video Title>
 
-**URL**: https://www.youtube.com/watch?v=<video_id>
-**频道**: <channel_name>
-**时长**: <duration>
-**日期**: YYYY-MM-DD
 
### 摘要
<3-5 sentence summary>
 
### 关键引用
> "<notable quote>"
 
### 核心洞察
<1-3 insights>

Step 7 — Post to Feishu (optional)

If user requests it, send a Feishu message with the summary and key quotes.

Error Handling

Error	Cause	Fix
`HTTP Error 403` on download	YouTube web client blocked	Use `--extractor-args "youtube:player_client=android"`
`android client https formats require a GVS PO Token`	Android client needs PO token for high-quality formats	Fall back to web client; format 18 (mp4) usually still downloads without token
`ffmpeg: command not found`	conda env not on PATH	`export PATH="/tmp/miniforge/bin:$PATH"`
`ModuleNotFoundError: whisper`	Using wrong python	Use `whisper` CLI directly, not `python3 -m whisper`
`exec format error` on ffmpeg	Wrong architecture binary	Use `/tmp/miniforge/bin/ffmpeg` (macOS arm64), not Linux static builds
No transcript file created	whisper failed silently	Check whisper output for CUDA/memory errors; try `base` model
`NotOpenSSLWarning`	urllib3 v2 + LibreSSL	Ignore; download still succeeds

Cleanup

bash

rm -f /tmp/yt_audio/${VIDEO_ID}.*

When NOT to Use This Skill

Video has accurate YouTube captions → Use web_fetch with transcript extraction instead (faster, more accurate, preserves speaker labels)
User only wants a summary → Ask if full transcript is needed before running (5+ min transcription vs instant captions)
Video is very long (>30 min) → Whisper inference takes significant time on CPU; warn user before starting
Non-English video → Specify language with --language <code> (e.g., --language zh for Chinese); tiny model quality degrades significantly for non-English

One-Time Installation

bash

# yt-dlp
pip3 install yt-dlp
 
# Miniforge (ffmpeg + whisper dependencies)
curl -sL "https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-MacOSX-arm64.sh" -o /tmp/miniforge.sh
chmod +x /tmp/miniforge.sh
/bin/bash /tmp/miniforge.sh -b -p /tmp/miniforge
/tmp/miniforge/bin/conda install -y ffmpeg -c conda-forge
/tmp/miniforge/bin/pip install openai-whisper
 
# Add to ~/.zshrc
echo 'export PATH="/tmp/miniforge/bin:$(python3 -m site --user-base)/bin:$PATH"' >> ~/.zshrc

Security

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Quality evaluation

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Source

SKILL.md in the repositoryCheckingyoutube-transcribe/SKILL.md

Popularity

Repository stars: 316
Repository forks: 141
7-day trend score: 29

Stars and forks are for the whole GitHub repository, not this skill alone. Not refreshed yet.

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