marketing-youtube-mine

name

marketing-youtube-mine

description

Mine YouTube comments for unanswered questions via yt-dlp — search videos by query or channel, pull top comments, extract question-shaped comments with reply-based answered detection, and cluster the unanswered ones. Output is drop-in compatible with marketing-reddit's mine command, so downstream daily SEO johnkueh-daily SEO loops consume either source identically. No API key (yt-dlp scrapes the public site). Replaces the retired comment-mine YouTube path. Triggers on "mine youtube", "youtube comments for", "what are people asking on youtube", "youtube questions about", "yt mine", "pull comments from this video", "recent uploads from channel".

marketing-youtube-mine

yt-dlp-backed comment miner. Two-stage: ytsearch (or a channel's /videos tab) finds videos, then per-video comment extraction with --extractor-args "youtube:max_comments=N,...;comment_sort=top". Question detection, answered heuristics, and clustering are a straight Python port of marketing-reddit's NLP, so both miners produce the same shapes and quality bar.

Setup — nothing to configure

Only dependency is yt-dlp on PATH (brew install yt-dlp). No API key, no config. Run doctor to confirm:

python3 ~/Projects/claude-skills/skills/marketing-youtube-mine/cli.py doctor

Commands

CLI=~/Projects/claude-skills/skills/marketing-youtube-mine/cli.py

# End-to-end mine: search → comments → questions → clusters (the daily-loop entry point)
python3 $CLI mine --query "retatrutide dosing" --videos 8 --comments 250 \
  --topic-keywords "reta|retatrutide|glp|dose"        # optional regex filter

# List videos: by search query, or a channel's recent uploads
python3 $CLI videos --query "claude code" --limit 15
python3 $CLI videos --channel @LennysPodcast --limit 15

# Raw comments for one video (debugging / deeper reads)
python3 $CLI comments --video VIDEO_ID --limit 250

Output shape (mine)

{
  "query": "...", "videos_scanned": 8, "comments_scanned": 1900,
  "n_questions": 31, "n_unanswered": 12,
  "questions": [{ "q": "...", "score": 14, "answered": false, "n_replies": 0,
                  "source": "youtube-comment", "thread_title": "<video title>",
                  "permalink": "https://www.youtube.com/watch?v=..." }],
  "top_questions": [...],
  "clusters_unanswered": [{ "key": "dose + split", "count": 3, "top_score": 14,
                            "sample": "...", "permalink": "...", "examples": [...] }],
  "failures": [{ "video": "...", "error": "..." }]
}

Same questions[] / clusters_unanswered fields as marketing-reddit — merge the two sources by concatenating questions arrays.

Behavior notes

• --comments N is per video, top-sorted; replies are capped at 10 per thread (enough for answered detection without paying for full reply trees).
• answered = a top-level question has at least one substantive reply (≥40 chars, not itself a question, not "idk/same/yes/no").
• One dead/private video never kills the mine — it lands in failures[] and the rest proceed (the daily loops require best-effort mining).
• Cost: free; wall-clock ~20–60s per video at 250 comments. 8 videos ≈ 3–6 min.
• A small n_questions is normal: top-sorted comments skew to highly-replied (answered) threads, and --topic-keywords drops off-topic questions before extraction. Drop the topic filter for breadth, filter downstream instead.
• YouTube occasionally rate-limits comment fetches on cloud IPs; on residential connections (where the daily loops run) this is rare. A persistent failures[] wall means update yt-dlp (brew upgrade yt-dlp) first.