YouTube Clip Extractor

Overview

This skill downloads YouTube videos, analyzes transcripts for compelling clip moments, extracts clips using ffmpeg, and generates platform-ready on-screen text and captions. It integrates with the existing caption and social content skills to deliver complete, publishable assets.

When to Use This Skill

• You have a YouTube URL and want to extract the best clips
• You want automated clip identification based on hook/coda criteria
• You need clips cut and ready for Descript or other editors
• You want on-screen text hooks and platform-specific captions for each clip

Do NOT use for:

• Full podcast production workflow (use podcast-production skill instead)
• Text-only social posts (use social-content-creation skill)
• Already-downloaded videos (skip to Phase 2)

---

Prerequisites

Required Tools (install via Homebrew)

brew install yt-dlp ffmpeg

File Location

All downloads go to: Content/YouTube Transcripts/

Structure:

Content/YouTube Transcripts/
├── {video_id}.mp4              # Full video (H.264 encoded)
├── {video_id}.en.vtt           # Timestamped subtitles
└── clips/
    └── {video_id}/
        ├── clip_01_{name}.mp4  # Individual clips
        ├── clip_02_{name}.mp4
        └── {video_id}_Clip_Assets.md  # Captions & hooks

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The 4-Phase Workflow

Phase 1: Download Video & Transcript

Goal: Get video and subtitles from YouTube URL

Step 1: Download with H.264 Encoding

Use H.264 format for Descript compatibility (NOT AV1):

# Download video in H.264 format (Descript-compatible)
yt-dlp -f "bestvideo[vcodec^=avc]+bestaudio[ext=m4a]/best[vcodec^=avc]" \
  --merge-output-format mp4 \
  -o "Content/YouTube Transcripts/{video_id}.mp4" \
  "YOUTUBE_URL"

# If H.264 unavailable, download best quality then re-encode:
yt-dlp -f "bestvideo+bestaudio" --merge-output-format mp4 \
  -o "Content/YouTube Transcripts/{video_id}_temp.mp4" \
  "YOUTUBE_URL"

# Re-encode to H.264 for Descript compatibility
ffmpeg -i "{video_id}_temp.mp4" -c:v libx264 -preset fast -crf 22 \
  -c:a aac -b:a 128k "{video_id}.mp4"

Step 2: Download Subtitles

yt-dlp --write-auto-sub --sub-lang en --skip-download \
  -o "Content/YouTube Transcripts/{video_id}.%(ext)s" \
  "YOUTUBE_URL"

Phase 1 Output:

• {video_id}.mp4 — Full video (H.264)
• {video_id}.en.vtt — Timestamped subtitles

---

Phase 2: Analyze Transcript for Clips

Goal: Identify 5-8 compelling clip moments with strong hooks and codas

Clip Selection Criteria

A good clip has:

1. Strong Hook (First 3 Seconds)

   • Polarizing statement ("Your kid's addiction is actually genius")
   • Counter-intuitive reveal ("My son's first job sucked. Perfect.")
   • Direct challenge ("Never give up on the weird kid")
   • Curiosity gap ("Then everything changed...")

2. Complete Arc (30-90 seconds)

   • Clear beginning, middle, end
   • Not just a "good quote" — a complete thought
   • Setup → Tension → Resolution OR Setup → Tension → Cliffhanger

3. Stakes

   • Why does this matter?
   • Who cares?
   • What's at risk?

4. Strong Coda/Ending

   • Insight or surprising conclusion
   • Cuts right before the answer (cliffhanger)
   • Quotable final line

Scan Transcript For:

Inflection Points:

• "Then everything changed..."
• "I realized..."
• "That's when I knew..."
• "The moment I..."

Vulnerability Moments:

• Personal stakes, failures, struggles
• "I was terrified..."
• "I almost gave up..."
• "Nobody believed..."

Contradiction Moments:

• "We thought X but actually..."
• "Everyone says... but the truth is..."
• "The opposite happened..."

Surprising Insights:

• Research, data, unexpected findings
• Counter-intuitive conclusions
• "What we found was..."

Character in Action:

• Showing, not telling
• Doing, not describing
• Specific moments, not abstractions

Quality Tests (Pass 4/5):

• Stranger Test: Would someone with zero context care?
• Itch Test: Creates need to know more?
• Stakes Test: Clear why it matters?
• Tease Test: Hints without giving away?
• Emotion Test: Feel something in first 5 seconds?

Phase 2 Output Format:

Create analysis document with clip recommendations:

# {Video Title} - Clip Analysis

## Video Details
- **URL:** [YouTube URL]
- **Duration:** [Total length]
- **Speaker(s):** [Names]
- **Topic:** [Primary subject]

---

## Recommended Clips

### CLIP 1: "{Descriptive Name}"
**Timestamp:** `MM:SS - MM:SS` (XX seconds)
**Hook:** [First line or opening moment]
**Arc:** [Setup → Middle → Ending summary]
**Coda:** [How it ends / final line]

**Key Quotes:**
- "[Verbatim quote 1]"
- "[Verbatim quote 2]"
- "[Verbatim quote 3]"

**Quality Tests:** Stranger ✅ | Itch ✅ | Stakes ✅ | Tease ✅ | Emotion ✅
**Why It Works:** [1-2 sentence rationale]
**Priority:** HIGH / MEDIUM / LOW

---

### CLIP 2: "{Descriptive Name}"
[Repeat structure...]

---

## Summary Table

| # | Clip Name | Timestamp | Length | Hook | Coda | Priority |
|---|-----------|-----------|--------|------|------|----------|
| 1 | [Name] | MM:SS-MM:SS | XXs | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | HIGH |
| 2 | [Name] | MM:SS-MM:SS | XXs | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ | HIGH |
| 3 | [Name] | MM:SS-MM:SS | XXs | ⭐⭐⭐ | ⭐⭐⭐⭐ | MEDIUM |

---

Phase 3: Cut Clips with FFmpeg

Goal: Extract approved clips as separate video files

Cutting Commands

Basic clip extraction (fast, uses keyframes):

ffmpeg -i "{video_id}.mp4" -ss MM:SS -to MM:SS -c copy \
  "clips/{video_id}/clip_01_{name}.mp4"

Precise cutting with re-encoding (slower but frame-accurate):

ffmpeg -ss MM:SS -i "{video_id}.mp4" -t DURATION \
  -c:v libx264 -preset fast -crf 22 -c:a aac -b:a 128k \
  "clips/{video_id}/clip_01_{name}.mp4"

Notes:

• -ss before -i = faster seeking (recommended)
• -c copy = no re-encoding (fast but may have keyframe issues)
• -c:v libx264 = re-encode to H.264 (slower but precise)
• Use H.264 output for Descript compatibility

Batch Cutting Example

# Create clips directory
mkdir -p "Content/YouTube Transcripts/clips/{video_id}"

# Cut each clip
ffmpeg -i "{video_id}.mp4" -ss 06:59 -to 08:10 -c copy "clips/{video_id}/clip_01_covid_revelation.mp4"
ffmpeg -i "{video_id}.mp4" -ss 20:50 -to 21:54 -c copy "clips/{video_id}/clip_02_whiteboard_teen.mp4"
ffmpeg -i "{video_id}.mp4" -ss 25:00 -to 26:10 -c copy "clips/{video_id}/clip_03_college_loans.mp4"

Phase 3 Output:

• Individual MP4 files for each clip
• All files in clips/{video_id}/ directory
• H.264 encoded for Descript compatibility

---

Phase 4: Generate On-Screen Text & Captions

Goal: Create platform-optimized hooks and captions for each clip

This phase uses the video-caption-creation skill methodology.

For Each Clip, Generate:

1. On-Screen Text Hook (3-5 options)

The text that appears in the first 3 seconds of the video. Must be:

• 2-4 words maximum (mobile readable)
• Stops the scroll
• Passes McDonald's Test (accessible language)
• Complements (not duplicates) audio

Hook Categories:

• Polarizing: "Your kid's [negative] is actually genius"
• Counter-Intuitive: "My son's first job sucked. Perfect."
• Direct Challenge: "Never give up on the weird kid"
• Curiosity Gap: "Then everything changed..."

2. Platform-Specific Captions

Platform	On-Screen Text	Caption Style	Hashtags
Instagram	Same	Short, emoji OK, accessible	5-10
TikTok	Same	Short, emoji OK, accessible	3-5
YouTube Shorts	Same	Short, minimal emoji	3-5 + #Shorts
Facebook	Same	Slightly longer, conversational, NO external links	0-2

Facebook Difference: Caption can be longer and more conversational. NO hashtags or external links (kills reach).

3. Algorithm Optimization

Per the Triple Word Score system:

• Audio: Topic words spoken in first 10 seconds
• On-Screen Text: Reinforces (not competes with) audio
• Caption: Topic-relevant keywords in first sentence
• Hashtags: Broad → Mid → Specific → Niche (10-12 total)

Phase 4 Output Format:

Create file: clips/{video_id}/{video_id}_CLIP_PACKAGE.md

# {Video Title} - Clip Package

## Source Video
- **URL:** [YouTube URL]
- **Title:** [Video title]
- **Duration:** [Total length]
- **Downloaded File:** `{video_id}.mp4`

---

## Context

[2-3 sentences explaining the backstory needed to understand the clip. Who is the speaker? What's their situation? What happened before/after the moments in the clip? This context ensures on-screen text and captions are coherent with the actual story.]

---

## Editing Instructions

**SEQUENCE (Rearranged from original - NOT linear):**

| Order | Timestamp | Speaker | Line |
|-------|-----------|---------|------|
| 1 | MM:SS-MM:SS | [Name] | "[Verbatim quote]" |
| 2 | MM:SS-MM:SS | [Name] | "[Verbatim quote]" |
| 3 | MM:SS-MM:SS | [Name] | "[Verbatim quote]" |

**OPTIONAL EXTENSION:**

| Order | Timestamp | Speaker | Line |
|-------|-----------|---------|------|
| 4 | MM:SS-MM:SS | [Name] | "[Verbatim quote]" |

---

## On-Screen Text Hook Options

1. **[Hook text]** - [Category]
2. **[Hook text]** - [Category]
3. **[Hook text]** - [Category]
4. **[Hook text]** - [Category]
5. **[Hook text]** - [Category]
6. **[Hook text]** - [Category]
7. **[Hook text]** - [Category]
8. **[Hook text]** - [Category]
9. **[Hook text]** - [Category]
10. **[Hook text]** - [Category]

---

## Platform Captions

### TikTok / Instagram Reels / YouTube Shorts
[Caption text]

[Hashtags: 3-5]

---

### Facebook
[Longer caption, conversational, NO hashtags]

---

### LinkedIn
[Professional tone caption]

[Hashtags: 3-5]

On-Screen Text Hook Categories

• Story Setup - Provides context that makes the clip make sense (e.g., "Homeschooler tries public school")
• Polarizing - Bold statement that divides opinion (e.g., "Most schools are awful")
• Contrast - Juxtaposition that creates tension (e.g., "First in class. Zero joy.")
• Curiosity Gap - Teases without revealing (e.g., "#1 out of 1,200 students")
• Story Tease - Hints at narrative arc (e.g., "She went back to homeschool after this")
• Pattern Interrupt - Subverts expectations (e.g., "This isn't anti-public school")

Context-Caption Coherence

Critical: On-screen text and captions must be coherent with the actual story in the transcript. Before writing hooks:

1. Understand the full context (who, what, when, why)
2. Identify what viewers need to know for the clip to make sense
3. Choose hooks that accurately represent the story
4. Avoid hooks that would confuse viewers when they hear the audio

Example: If the clip shows someone criticizing public school, but they were actually a homeschooler who tried public school once, hooks like "Homeschooler tries public school" or "She tried public school for one year" provide necessary context that makes the story coherent.

---

Complete Workflow Example

# PHASE 1: Download
yt-dlp -f "bestvideo[vcodec^=avc]+bestaudio" --merge-output-format mp4 \
  -o "Content/YouTube Transcripts/cvGtVmI4jTQ.mp4" \
  "https://www.youtube.com/watch?v=cvGtVmI4jTQ"

yt-dlp --write-auto-sub --sub-lang en --skip-download \
  -o "Content/YouTube Transcripts/cvGtVmI4jTQ.%(ext)s" \
  "https://www.youtube.com/watch?v=cvGtVmI4jTQ"

# PHASE 2: Analyze transcript (manual review)
# Read VTT file, identify clips using criteria above

# PHASE 3: Cut clips
mkdir -p "Content/YouTube Transcripts/clips/cvGtVmI4jTQ"
ffmpeg -i "cvGtVmI4jTQ.mp4" -ss 06:59 -to 08:10 \
  -c:v libx264 -preset fast -crf 22 -c:a aac \
  "clips/cvGtVmI4jTQ/clip_01_covid_revelation.mp4"

# PHASE 4: Generate assets (create markdown file with hooks/captions)

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Related Skills

This skill integrates with:

Skill	When to Use	What It Provides
video-caption-creation	Phase 4	On-screen text hook categories, Triple Word Score system, platform caption guidelines
youtube-downloader	Phase 1 (alternative)	Detailed yt-dlp installation checks, error handling, transcript-only workflow
text-content	After clips ready	Framework fitting for text posts about clips
podcast-production	Full episode workflow	Complete 4-checkpoint production system

Skill Cross-References

From video-caption-creation:

• Hook categories (Polarizing, Counter-Intuitive, Direct Challenge, Curiosity Gap)
• Triple Word Score system (Audio + On-Screen + Caption + Hashtags)
• Platform-specific hashtag counts
• McDonald's Test for accessibility

From text-content:

• Platform voice guidelines (LinkedIn vs Facebook vs Instagram)
• Framework fitting method
• 360+ templates in references/

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Common Mistakes to Avoid

Download Issues

• ❌ Downloading AV1 codec (Descript can't import)
• ❌ Not re-encoding to H.264 when needed
• ❌ Forgetting to download subtitles

Clip Selection Issues

• ❌ Choosing "good quotes" instead of complete arcs
• ❌ Clips too long (>90 seconds) or too short (<30 seconds)
• ❌ No clear hook in first 3 seconds
• ❌ Giving away the punchline in the hook

Cutting Issues

• ❌ Cutting at non-keyframes (use re-encode for precision)
• ❌ Starting mid-sentence
• ❌ Ending before natural conclusion

Caption Issues

• ❌ On-screen text too long (>4 words)
• ❌ Same caption for Facebook as other platforms
• ❌ External links in Facebook caption
• ❌ Hashtags in Facebook caption

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Quality Checklist

Before delivering clips:

Video Files:

• All clips are H.264 encoded
• Each clip is 30-90 seconds
• Audio and video are synced
• Clean start/end points (no mid-word cuts)

Clip Selection:

• Each clip passes 4/5 quality tests
• Strong hook in first 3 seconds
• Complete arc (not just a quote)
• Clear stakes (why it matters)

Captions & Hooks:

• 3-5 on-screen text options per clip
• On-screen text is 2-4 words max
• Platform-specific captions created
• Facebook caption is different (longer, no hashtags)
• Hashtag strategy spans broad to niche

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Version History

• v1.1 (2025-12-20): Streamlined output format

  • Removed "Target Length" and "Concept" sections from output
  • Removed "Why This Edit Works" section
  • Added "Context" section for backstory coherence
  • Simplified on-screen text hooks to numbered list with categories only
  • Added "Story Setup" hook category for context-providing hooks
  • Added "Context-Caption Coherence" guidance
  • Updated output template to match streamlined format

• v1.0 (2025-12-02): Initial skill creation

  • 4-phase workflow: Download → Analyze → Cut → Caption
  • Integration with video-caption-creation skill
  • H.264 encoding for Descript compatibility
  • Platform-specific caption guidelines
  • Quality tests from podcast-production skill