Extract Y2b Insights

Extract YouTube Insights

Pull the highest-signal ideas out of a YouTube video — specifically the most controversial takes and the ones that read as genuinely new (ideas you would NOT easily find already discussed across the web). Output them two ways:

1. A clean .txt report saved to disk
2. The same content printed in the terminal

This is the text-only cousin of get-y2b-clips: it reuses that skill's transcript download + VTT parsing, but produces no video clips and no subtitles — just distilled insights.

When to Use This Skill

Activate when the user:

• Provides a YouTube URL and wants "insights", "key ideas", "hot takes", "takeaways"
• Wants the "most controversial" points, or "ideas that don't already exist" / "novel ideas"
• Wants a written summary of the thinking in a video, not clips of it

If the user wants video clips → use get-y2b-clips. If the user wants subtitles burned into a video → use add-subtitles.

Dependencies Check

command -v yt-dlp || echo "MISSING: yt-dlp"

yt-dlp is the only hard dependency (used to fetch the transcript). Install:

# macOS
brew install yt-dlp
# Linux / universal
pip3 install yt-dlp

ffmpeg is NOT required for this skill (no media is produced).

Input Requirements

• Required: YouTube URL
• Optional:
• Number of insights (default: 5–8, scaled to video length)
• Focus: lean more "controversial" vs more "novel" (default: both)
• Whether to web-check novelty (default: yes, when WebSearch is available)
• Output path (default: ./insights/_/insights.txt)

Workflow

Phase 1: Setup

VIDEO_URL="USER_PROVIDED_URL"
VIDEO_TITLE=$(yt-dlp --print "%(title)s" "$VIDEO_URL")
CHANNEL=$(yt-dlp --print "%(channel)s" "$VIDEO_URL")
DURATION=$(yt-dlp --print "%(duration)s" "$VIDEO_URL")

TIMESTAMP=$(date +"%Y-%m-%d_%H-%M-%S")
SLUG=$(echo "$VIDEO_TITLE" | tr '/:?*"<>|\\' '-' | tr '[:upper:]' '[:lower:]' | tr ' ' '-' | cut -c1-50)
OUT_DIR="./insights/${TIMESTAMP}_${SLUG}"
mkdir -p "$OUT_DIR"
echo "Video: $VIDEO_TITLE ($((DURATION / 60)) min)"
echo "Output: $OUT_DIR"

Phase 2: Get the Transcript

Priority: manual subtitles → auto-generated subtitles.

cd "$OUT_DIR"

if yt-dlp --write-sub --sub-langs "en" --skip-download -o "transcript" "$VIDEO_URL" 2>/dev/null; then
    echo "Manual subtitles downloaded"
elif yt-dlp --write-auto-sub --sub-langs "en" --skip-download -o "transcript" "$VIDEO_URL" 2>/dev/null; then
    echo "Auto-generated subtitles downloaded"
else
    echo "No subtitles available"
    # Tell the user; without a transcript this skill can't extract insights.
fi

# Parse VTT -> segments.json + full_transcript.txt (timestamps preserved)
python3 .claude/skills/extract-y2b-insights/parse_vtt.py transcript.en.vtt

Phase 3: Analyze for Controversial & Novel Insights

Read full_transcript.txt and select the standout ideas. Score each candidate on two axes (0–10):

Controversy (does it cut against consensus / provoke disagreement?)

• Direct disagreement with named people, institutions, or "everyone"
• Contrarian framing: "everyone thinks X, but actually…", "unpopular opinion"
• Strong stance language: "never", "always", "completely wrong"
• Predictions that defy the current narrative

Novelty (would you struggle to find this idea already discussed online?)

• A specific mechanism, framework, or causal claim that isn't the standard talking point
• A non-obvious connection between two domains
• A concrete prediction or number that isn't the consensus figure
• Reframing of a familiar problem in a way that isn't widely circulated

Select an insight if it scores high on at least one axis (≈7+). Prefer ideas that are specific and falsifiable over vague platitudes. Skip generic advice, well-worn truisms, and anything that's just a summary of common knowledge.

Capture the exact timestamp from segments.json for each insight so the user can jump to it (&t=s).

Phase 4: Web-Check Novelty (optional but recommended)

For each candidate flagged as "novel", do a quick WebSearch to test whether the idea is genuinely uncommon:

• Search the core claim in a few words.
• If results show the idea is widely repeated → lower its novelty score (or drop it).
• If results show only the opposite/conventional view, or little on the specific

framing → keep it and fill in web_contrast (what the common online view is, and how this differs).

Only do this when WebSearch is available and the user hasn't opted out. Keep it light (1 quick search per candidate) — this is a sanity check, not exhaustive research.

Phase 5: Write insights.json

Create $OUT_DIR/insights.json following this schema:

{
  "source": {
    "url": "https://www.youtube.com/watch?v=VIDEO_ID",
    "title": "Video Title",
    "channel": "Channel Name",
    "duration_minutes": 92
  },
  "insights": [
    {
      "title": "Short headline for the idea",
      "claim": "1-3 sentence statement of the insight as the speaker frames it.",
      "timestamp": "00:14:05",
      "type": "controversial",
      "controversy_score": 9,
      "novelty_score": 6,
      "why": "Why this is controversial and/or appears to be a genuinely new idea.",
      "web_contrast": "What the conventional / commonly-found-online view is, and how this differs.",
      "quote": "Optional short verbatim quote from the transcript."
    }
  ]
}

• type: "controversial", "novel", or "both".
• Order insights strongest-first (highest combined controversy + novelty).
• web_contrast is optional; include it whenever a web-check was done.

Phase 6: Render to .txt AND terminal

python3 .claude/skills/extract-y2b-insights/render_insights.py \
    --insights "$OUT_DIR/insights.json" \
    --output "$OUT_DIR/insights.txt"

This writes a clean insights.txt and prints the same report (colorized) to the terminal. Use --no-print to only write the file.

Phase 7: Summary

Tell the user:

• How many insights were extracted and the path to insights.txt
• A one-line teaser of the top 1–2 insights
• That timestamps are included so they can jump to each moment in the video

Console Progress Reporting

[SETUP] Fetching video info...
  ✓ Video: "Title Here" (92 min) — Channel Name

[TRANSCRIPT] Downloading subtitles...
  ✓ Auto-generated English subtitles found
  ✓ Parsed 1,204 segments

[ANALYSIS] Scoring controversial & novel ideas...
  ✓ 7 insights selected (web-checked 4 for novelty)

[OUTPUT]
  ✓ insights.json written
  ✓ insights.txt written + printed below

Error Handling

| Issue | Solution | |-------|----------| | MISSING: yt-dlp | Provide install command, then retry | | No subtitles available | Inform user — without a transcript, insights can't be extracted. Offer to fall back to get-y2b-clips' Whisper path if they want audio transcription | | Private/unavailable video | Inform user, cannot proceed | | Very short video | Extract fewer insights (1–3) | | WebSearch unavailable | Skip Phase 4; still extract based on transcript, note novelty is un-verified |

Output Files

insights/
  YYYY-MM-DD_HH-MM-SS_/
    transcript.en.vtt      # raw subtitles
    segments.json          # timestamped segments
    full_transcript.txt    # readable transcript w/ timestamps
    insights.json          # structured insights (source of truth)
    insights.txt           # final human-readable report (also printed to terminal)

Example Session

User: Pull the most controversial / original ideas from https://www.youtube.com/watch?v=abc123 and save them to a txt.

Claude:

1. Checks yt-dlp
2. Fetches video info + downloads the transcript, parses to segments.json
3. Scores ideas for controversy + novelty; web-checks the novel ones
4. Writes insights.json, then runs render_insights.py → insights.txt + terminal print
5. Summarizes the top insights and the file path

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: DidierRLopes
• Source: DidierRLopes/get-y2b-clips
• License: MIT

Install and usage instructions live in the source repository linked above.